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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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|
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Share chat
Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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|
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|
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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2026-05-07T09:45:22.811350Z INFO screenpipe_engin 2026-05-07T09:45:22.811350Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-05-07T09:45:22.811366Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-05-07T09:45:23.497850Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 1 excluded)
2026-05-07T09:45:23.576837Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 1: frame_id=155, dur=39ms
2026-05-07T09:45:23.594869Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 1 excluded)
2026-05-07T09:45:23.728324Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 2: frame_id=156, dur=87ms
2026-05-07T09:45:25.114503Z WARN screenpipe_a11y::tree::macos_lines: lines: AXUIElementCopyParameterizedAttributeValue(AXLineForIndex) failed status=os::Status { raw: -25212, fcc: "....", help: "https://www.osstatus.com?search=-25212" } — first failure (further failures suppressed); search highlights will fall back to paragraph bbox on this app
2026-05-07T09:45:29.149038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7681127700032315998, trigger=visual_change)
2026-05-07T09:45:48.802125Z INFO screenpipe_engine::meeting_detector: meeting v2: Idle -> Confirming (app=Firefox, signals=2)
2026-05-07T09:45:53.994026Z INFO screenpipe_engine::meeting_detector: meeting v2: Confirming -> Active (app=Firefox, signals=2, browser=true)
2026-05-07T09:45:53.994983Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting started (id=1, app=Firefox, title=None)
2026-05-07T09:45:53.995269Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=true, manual=false, active_id=Some(1), app=Some("Firefox"), source=Some("ui_scan")
2026-05-07T09:46:22.681313Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 77 eligible frames
2026-05-07T09:46:24.975724Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 7.6MB → 1.8MB (4.2x), 38 JPEGs deleted
2026-05-07T09:46:27.284414Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 5.7MB → 0.4MB (14.3x), 37 JPEGs deleted
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T09:51:27.298006Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 2 eligible frames
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T09:56:27.307108Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 45 eligible frames
2026-05-07T09:56:29.766853Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 29 frames, 4.6MB → 2.5MB (1.8x), 29 JPEGs deleted
2026-05-07T09:56:31.182378Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.5MB → 1.1MB (2.4x), 16 JPEGs deleted
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:00:50.039083Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=4913720669938969944, trigger=visual_change)
2026-05-07T10:01:31.184069Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 70 eligible frames
2026-05-07T10:01:33.701926Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 6.4MB → 2.7MB (2.4x), 37 JPEGs deleted
2026-05-07T10:01:35.921087Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 31 frames, 5.6MB → 1.7MB (3.3x), 31 JPEGs deleted
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:06:35.921469Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 37 eligible frames
2026-05-07T10:06:37.286011Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 18 frames, 3.2MB → 1.1MB (3.0x), 18 JPEGs deleted
2026-05-07T10:06:38.611334Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 17 frames, 3.2MB → 0.7MB (4.3x), 17 JPEGs deleted
2026-05-07T10:07:10.923890Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-363038137104660743, trigger=click)
2026-05-07T10:07:54.763156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=click)
2026-05-07T10:07:58.866469Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7750749190140780004, trigger=click)
2026-05-07T10:08:03.510124Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=visual_change)
2026-05-07T10:08:39.088273Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3643569030250279342, trigger=visual_change)
2026-05-07T10:09:18.346872Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6569246041316117301, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:10:36.954420Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4721988080886038618, trigger=visual_change)
2026-05-07T10:10:54.330626Z INFO screenpipe_engine::meeting_detector: meeting v2: Active -> Ending (no controls, app=Firefox, id=1, grace=300s)
2026-05-07T10:11:12.369084Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8225140425134796296, trigger=visual_change)
2026-05-07T10:11:38.678230Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:11:40.024314Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.8MB → 1.0MB (2.9x), 16 JPEGs deleted
2026-05-07T10:11:40.843106Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.0MB → 0.3MB (6.4x), 10 JPEGs deleted
2026-05-07T10:12:12.538283Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7652725149678140352, trigger=click)
2026-05-07T10:12:16.550570Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9173838423795606666, trigger=visual_change)
2026-05-07T10:12:31.745217Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:34.790862Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:37.784752Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:40.806177Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:43.854881Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:59.015149Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:14.166750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:17.184510Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:34.773468Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=click)
2026-05-07T10:13:44.486450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=visual_change)
2026-05-07T10:13:54.236529Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7077166956358270957, trigger=click)
2026-05-07T10:13:59.559230Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3980541844315414876, trigger=visual_change)
2026-05-07T10:14:11.809168Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6523625761174407917, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:15:45.403085Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:45.438150Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:58.662028Z INFO screenpipe_engine::meeting_detector: meeting v2: Ending -> Idle (timeout=300s, app=Firefox, id=1)
2026-05-07T10:15:58.663307Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting ended (id=1)
2026-05-07T10:15:58.663499Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=false, manual=false, active_id=None, app=None, source=None
2026-05-07T10:16:05.043304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=visual_change)
2026-05-07T10:16:06.212558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=click)
2026-05-07T10:16:10.088825Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.585397Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.626664Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:14.079057Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=visual_change)
2026-05-07T10:16:16.219212Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:16.238237Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.211409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.247202Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:37.973759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.014645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.719453Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.757458Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.323203Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.361472Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:40.850433Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 50 eligible frames
2026-05-07T10:16:42.489832Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 25 frames, 4.5MB → 1.4MB (3.2x), 25 JPEGs deleted
2026-05-07T10:16:44.044480Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 23 frames, 4.2MB → 1.1MB (3.7x), 23 JPEGs deleted
2026-05-07T10:16:44.383786Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=visual_change)
2026-05-07T10:17:13.547514Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:17:23.997418Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:17:36.067503Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=visual_change)
2026-05-07T10:17:48.254094Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:18:06.328441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:21.374558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:27.400579Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:36.379491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:18:39.375238Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:19:43.208935Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
2026-05-07T10:19:49.257421Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:21:21.848375Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=click)
2026-05-07T10:21:23.439805Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=visual_change)
2026-05-07T10:21:38.803777Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4689651471004117672, trigger=click)
2026-05-07T10:21:44.054102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 84 eligible frames
2026-05-07T10:21:46.307600Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 5.0MB → 1.3MB (3.8x), 38 JPEGs deleted
2026-05-07T10:21:49.031129Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.2MB → 1.4MB (5.2x), 44 JPEGs deleted
2026-05-07T10:23:02.085605Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:23:05.086593Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:24:05.661776Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
2026-05-07T10:24:09.585701Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:24:11.714956Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:25:31.235235Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4393937499657261667, trigger=visual_change)
2026-05-07T10:25:38.986311Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2365554338448923185, trigger=click)
2026-05-07T10:26:49.039316Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 88 eligible frames
2026-05-07T10:26:52.006289Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 42 frames, 9.5MB → 4.4MB (2.1x), 42 JPEGs deleted
2026-05-07T10:26:54.644073Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.6MB → 1.1MB (7.0x), 44 JPEGs deleted
2026-05-07T10:28:33.499505Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:35.956588Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:36.666492Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:36.789359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:39.100987Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:40.047061Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:40.064268Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:42.052599Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:44.774285Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:44.791022Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:45.378301Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:46.470785Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:46.575295Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.652750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.668490Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:48.439575Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:50.165309Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:50.177581Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:51.452287Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:51.994437Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:52.053841Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:54.432682Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:03.124980Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:03.868714Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:05.079320Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.543967Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.576904Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.792645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.865500Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:20.773748Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:21.723479Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:21.752924Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:23.883669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1480943928003130782, trigger=visual_change)
2026-05-07T10:29:35.016352Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:36.054767Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:36.117899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.114508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:38.937597Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.987983Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:31:28.462010Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:28.506716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:54.652328Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 102 eligible frames
2026-05-07T10:31:57.015395Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 13.6MB → 2.6MB (5.3x), 37 JPEGs deleted
2026-05-07T10:32:00.752190Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 63 frames, 7.5MB → 3.0MB (2.5x), 63 JPEGs deleted
2026-05-07T10:32:26.599963Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:32:27.400474Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:34:30.099383Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:34:30.729647Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:03.954351Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:13.717941Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8871609275972961978, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:37:00.792475Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 104 eligible frames
2026-05-07T10:37:03.726678Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 45 frames, 15.7MB → 2.8MB (5.7x), 45 JPEGs deleted
2026-05-07T10:37:07.426691Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 57 frames, 8.5MB → 3.4MB (2.5x), 57 JPEGs deleted
2026-05-07T10:37:20.569450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.162289Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.307132Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:30.755107Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:32.262354Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:32.364050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:45.907491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:47.205630Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:47.304637Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:41:59.417224Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5950044591583842886, trigger=click)
2026-05-07T10:41:59.477946Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5950044591583842886, trigger=click)
2026-05-07T10:42:04.742903Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:04.791839Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:07.459097Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 38 eligible frames
2026-05-07T10:42:08.925855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 20 frames, 4.3MB → 2.0MB (2.1x), 20 JPEGs deleted
2026-05-07T10:42:09.013373Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:09.056483Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:10.403020Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.7MB → 1.2MB (2.2x), 16 JPEGs deleted
2026-05-07T10:42:15.023525Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:15.087803Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:55.509669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2284708141138457574, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:47:10.412520Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 76 eligible frames
2026-05-07T10:47:12.469581Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 34 frames, 8.4MB → 1.8MB (4.7x), 34 JPEGs deleted
2026-05-07T10:47:15.163169Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.2MB → 2.4MB (3.0x), 40 JPEGs deleted
2026-05-07T10:49:59.513306Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-5898226934247819222, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:52:15.173889Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 73 eligible frames
2026-05-07T10:52:17.082276Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 8.4MB → 0.7MB (11.6x), 32 JPEGs deleted
2026-05-07T10:52:19.490930Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 39 frames, 5.9MB → 2.2MB (2.7x), 39 JPEGs deleted
2026-05-07T10:54:22.187516Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:54:28.229775Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:10.540942Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:13.602063Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:16.605531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:55:24.247307Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:57:19.473518Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:57:20.680885Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 11 frames, 2.9MB → 0.5MB (5.3x), 11 JPEGs deleted
2026-05-07T10:57:21.976198Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 15 frames, 2.5MB → 1.3MB (1.9x), 15 JPEGs deleted
2026-05-07T10:58:11.572541Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:11.672796Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:12.154759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:13.639499Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:15.413079Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:15.495531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:16.595925Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:21.437769Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.085129Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.869865Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.981197Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:42.120927Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:42.168157Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:59.393221Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3964650797953228084, trigger=click)
2026-05-07T10:58:59.482526Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=click)
2026-05-07T10:59:49.917826Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
2026-05-07T11:00:17.089194Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T11:02:21.972630Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 22 eligible frames
2026-05-07T11:02:22.860530Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.6MB → 0.5MB (4.8x), 10 JPEGs deleted
2026-05-07T11:02:23.655299Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 1.7MB → 0.4MB (4.9x), 10 JPEGs deleted
2026-05-07T11:03:02.204893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:02.270520Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.248220Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.306387Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:59.105792Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T11:05:34.710050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:07:23.661054Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 75 eligible frames
2026-05-07T11:07:25.906642Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 10.0MB → 0.4MB (23.0x), 38 JPEGs deleted
2026-05-07T11:07:28.176089Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 35 frames, 6.9MB → 2.5MB (2.8x), 35 JPEGs deleted
2026-05-07T11:08:06.864979Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:15.119215Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:08:29.818126Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:32.981193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:33.042213Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:50.978274Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.734286Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.851489Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:07.119123Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:37.291815Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:09:40.319359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:14.288318Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:14.424670Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.847960Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.940298Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T11:10:50.032003Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:56.222667Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:56.312244Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:20.348299Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:21.120460Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.721850Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.804892Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.056996Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.140586Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.305845Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.383186Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.114228Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.191508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:35.851585Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:45.797893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:11:58.899193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:12:02.054717Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.130409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.755693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.846886Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:03.619909Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=visual_change)
2026-05-07T11:12:10.642255Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:10.727852Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.690324Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.781304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:21.621763Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:25.624917Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:28.182732Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 74 eligible frames
2026-05-07T11:12:30.153855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 7.1MB → 0.9MB (8.3x), 32 JPEGs deleted
2026-05-07T11:12:32.796836Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.6MB → 2.8MB (2.7x), 40 JPEGs deleted
2026-05-07T11:12:40.535068Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:40.647364Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.220156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.301848Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.222251Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.266764Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:13:01.235609Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:01.299808Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.450693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.586876Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.862308Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.940165Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:14:01.162441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:03.453473Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:08.993859Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:09.054039Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.312363Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.354292Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.396038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.489451Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:22.083385Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=visual_change)
2026-05-07T11:14:36.252205Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.015757Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.664591Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=visual_change)
2026-05-07T11:14:45.857760Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:45.904716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:47.073442Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T11:16:05.411899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture fo...
|
iTerm2
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screenpipe"
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NULL
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|
2026-05-07T09:45:22.811350Z INFO screenpipe_engin 2026-05-07T09:45:22.811350Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-05-07T09:45:22.811366Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-05-07T09:45:23.497850Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 1 excluded)
2026-05-07T09:45:23.576837Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 1: frame_id=155, dur=39ms
2026-05-07T09:45:23.594869Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 1 excluded)
2026-05-07T09:45:23.728324Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 2: frame_id=156, dur=87ms
2026-05-07T09:45:25.114503Z WARN screenpipe_a11y::tree::macos_lines: lines: AXUIElementCopyParameterizedAttributeValue(AXLineForIndex) failed status=os::Status { raw: -25212, fcc: "....", help: "https://www.osstatus.com?search=-25212" } — first failure (further failures suppressed); search highlights will fall back to paragraph bbox on this app
2026-05-07T09:45:29.149038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7681127700032315998, trigger=visual_change)
2026-05-07T09:45:48.802125Z INFO screenpipe_engine::meeting_detector: meeting v2: Idle -> Confirming (app=Firefox, signals=2)
2026-05-07T09:45:53.994026Z INFO screenpipe_engine::meeting_detector: meeting v2: Confirming -> Active (app=Firefox, signals=2, browser=true)
2026-05-07T09:45:53.994983Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting started (id=1, app=Firefox, title=None)
2026-05-07T09:45:53.995269Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=true, manual=false, active_id=Some(1), app=Some("Firefox"), source=Some("ui_scan")
2026-05-07T09:46:22.681313Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 77 eligible frames
2026-05-07T09:46:24.975724Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 7.6MB → 1.8MB (4.2x), 38 JPEGs deleted
2026-05-07T09:46:27.284414Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 5.7MB → 0.4MB (14.3x), 37 JPEGs deleted
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T09:51:27.298006Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 2 eligible frames
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T09:56:27.307108Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 45 eligible frames
2026-05-07T09:56:29.766853Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 29 frames, 4.6MB → 2.5MB (1.8x), 29 JPEGs deleted
2026-05-07T09:56:31.182378Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.5MB → 1.1MB (2.4x), 16 JPEGs deleted
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:00:50.039083Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=4913720669938969944, trigger=visual_change)
2026-05-07T10:01:31.184069Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 70 eligible frames
2026-05-07T10:01:33.701926Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 6.4MB → 2.7MB (2.4x), 37 JPEGs deleted
2026-05-07T10:01:35.921087Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 31 frames, 5.6MB → 1.7MB (3.3x), 31 JPEGs deleted
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:06:35.921469Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 37 eligible frames
2026-05-07T10:06:37.286011Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 18 frames, 3.2MB → 1.1MB (3.0x), 18 JPEGs deleted
2026-05-07T10:06:38.611334Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 17 frames, 3.2MB → 0.7MB (4.3x), 17 JPEGs deleted
2026-05-07T10:07:10.923890Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-363038137104660743, trigger=click)
2026-05-07T10:07:54.763156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=click)
2026-05-07T10:07:58.866469Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7750749190140780004, trigger=click)
2026-05-07T10:08:03.510124Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=visual_change)
2026-05-07T10:08:39.088273Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3643569030250279342, trigger=visual_change)
2026-05-07T10:09:18.346872Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6569246041316117301, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:10:36.954420Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4721988080886038618, trigger=visual_change)
2026-05-07T10:10:54.330626Z INFO screenpipe_engine::meeting_detector: meeting v2: Active -> Ending (no controls, app=Firefox, id=1, grace=300s)
2026-05-07T10:11:12.369084Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8225140425134796296, trigger=visual_change)
2026-05-07T10:11:38.678230Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:11:40.024314Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.8MB → 1.0MB (2.9x), 16 JPEGs deleted
2026-05-07T10:11:40.843106Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.0MB → 0.3MB (6.4x), 10 JPEGs deleted
2026-05-07T10:12:12.538283Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7652725149678140352, trigger=click)
2026-05-07T10:12:16.550570Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9173838423795606666, trigger=visual_change)
2026-05-07T10:12:31.745217Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:34.790862Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:37.784752Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:40.806177Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:43.854881Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:59.015149Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:14.166750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:17.184510Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:34.773468Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=click)
2026-05-07T10:13:44.486450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=visual_change)
2026-05-07T10:13:54.236529Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7077166956358270957, trigger=click)
2026-05-07T10:13:59.559230Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3980541844315414876, trigger=visual_change)
2026-05-07T10:14:11.809168Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6523625761174407917, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:15:45.403085Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:45.438150Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:58.662028Z INFO screenpipe_engine::meeting_detector: meeting v2: Ending -> Idle (timeout=300s, app=Firefox, id=1)
2026-05-07T10:15:58.663307Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting ended (id=1)
2026-05-07T10:15:58.663499Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=false, manual=false, active_id=None, app=None, source=None
2026-05-07T10:16:05.043304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=visual_change)
2026-05-07T10:16:06.212558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=click)
2026-05-07T10:16:10.088825Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.585397Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.626664Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:14.079057Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=visual_change)
2026-05-07T10:16:16.219212Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:16.238237Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.211409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.247202Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:37.973759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.014645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.719453Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.757458Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.323203Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.361472Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:40.850433Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 50 eligible frames
2026-05-07T10:16:42.489832Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 25 frames, 4.5MB → 1.4MB (3.2x), 25 JPEGs deleted
2026-05-07T10:16:44.044480Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 23 frames, 4.2MB → 1.1MB (3.7x), 23 JPEGs deleted
2026-05-07T10:16:44.383786Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=visual_change)
2026-05-07T10:17:13.547514Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:17:23.997418Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:17:36.067503Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=visual_change)
2026-05-07T10:17:48.254094Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:18:06.328441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:21.374558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:27.400579Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:36.379491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:18:39.375238Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:19:43.208935Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
2026-05-07T10:19:49.257421Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:21:21.848375Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=click)
2026-05-07T10:21:23.439805Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=visual_change)
2026-05-07T10:21:38.803777Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4689651471004117672, trigger=click)
2026-05-07T10:21:44.054102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 84 eligible frames
2026-05-07T10:21:46.307600Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 5.0MB → 1.3MB (3.8x), 38 JPEGs deleted
2026-05-07T10:21:49.031129Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.2MB → 1.4MB (5.2x), 44 JPEGs deleted
2026-05-07T10:23:02.085605Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:23:05.086593Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:24:05.661776Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
2026-05-07T10:24:09.585701Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:24:11.714956Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:25:31.235235Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4393937499657261667, trigger=visual_change)
2026-05-07T10:25:38.986311Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2365554338448923185, trigger=click)
2026-05-07T10:26:49.039316Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 88 eligible frames
2026-05-07T10:26:52.006289Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 42 frames, 9.5MB → 4.4MB (2.1x), 42 JPEGs deleted
2026-05-07T10:26:54.644073Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.6MB → 1.1MB (7.0x), 44 JPEGs deleted
2026-05-07T10:28:33.499505Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:35.956588Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:36.666492Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:36.789359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:39.100987Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:40.047061Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:40.064268Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:42.052599Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:44.774285Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:44.791022Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:45.378301Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:46.470785Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:46.575295Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.652750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.668490Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:48.439575Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:50.165309Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:50.177581Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:51.452287Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:51.994437Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:52.053841Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:54.432682Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:03.124980Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:03.868714Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:05.079320Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.543967Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.576904Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.792645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.865500Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:20.773748Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:21.723479Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:21.752924Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:23.883669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1480943928003130782, trigger=visual_change)
2026-05-07T10:29:35.016352Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:36.054767Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:36.117899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.114508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:38.937597Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.987983Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:31:28.462010Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:28.506716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:54.652328Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 102 eligible frames
2026-05-07T10:31:57.015395Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 13.6MB → 2.6MB (5.3x), 37 JPEGs deleted
2026-05-07T10:32:00.752190Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 63 frames, 7.5MB → 3.0MB (2.5x), 63 JPEGs deleted
2026-05-07T10:32:26.599963Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:32:27.400474Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:34:30.099383Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:34:30.729647Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:03.954351Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:13.717941Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8871609275972961978, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:37:00.792475Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 104 eligible frames
2026-05-07T10:37:03.726678Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 45 frames, 15.7MB → 2.8MB (5.7x), 45 JPEGs deleted
2026-05-07T10:37:07.426691Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 57 frames, 8.5MB → 3.4MB (2.5x), 57 JPEGs deleted
2026-05-07T10:37:20.569450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.162289Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.307132Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:30.755107Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:32.262354Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:32.364050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:45.907491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:47.205630Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:47.304637Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:41:59.417224Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5950044591583842886, trigger=click)
2026-05-07T10:41:59.477946Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5950044591583842886, trigger=click)
2026-05-07T10:42:04.742903Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:04.791839Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:07.459097Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 38 eligible frames
2026-05-07T10:42:08.925855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 20 frames, 4.3MB → 2.0MB (2.1x), 20 JPEGs deleted
2026-05-07T10:42:09.013373Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:09.056483Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:10.403020Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.7MB → 1.2MB (2.2x), 16 JPEGs deleted
2026-05-07T10:42:15.023525Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:15.087803Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:55.509669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2284708141138457574, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:47:10.412520Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 76 eligible frames
2026-05-07T10:47:12.469581Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 34 frames, 8.4MB → 1.8MB (4.7x), 34 JPEGs deleted
2026-05-07T10:47:15.163169Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.2MB → 2.4MB (3.0x), 40 JPEGs deleted
2026-05-07T10:49:59.513306Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-5898226934247819222, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:52:15.173889Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 73 eligible frames
2026-05-07T10:52:17.082276Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 8.4MB → 0.7MB (11.6x), 32 JPEGs deleted
2026-05-07T10:52:19.490930Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 39 frames, 5.9MB → 2.2MB (2.7x), 39 JPEGs deleted
2026-05-07T10:54:22.187516Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:54:28.229775Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:10.540942Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:13.602063Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:16.605531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:55:24.247307Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:57:19.473518Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:57:20.680885Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 11 frames, 2.9MB → 0.5MB (5.3x), 11 JPEGs deleted
2026-05-07T10:57:21.976198Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 15 frames, 2.5MB → 1.3MB (1.9x), 15 JPEGs deleted
2026-05-07T10:58:11.572541Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:11.672796Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:12.154759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:13.639499Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:15.413079Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:15.495531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:16.595925Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:21.437769Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.085129Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.869865Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.981197Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:42.120927Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:42.168157Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:59.393221Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3964650797953228084, trigger=click)
2026-05-07T10:58:59.482526Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=click)
2026-05-07T10:59:49.917826Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
2026-05-07T11:00:17.089194Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T11:02:21.972630Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 22 eligible frames
2026-05-07T11:02:22.860530Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.6MB → 0.5MB (4.8x), 10 JPEGs deleted
2026-05-07T11:02:23.655299Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 1.7MB → 0.4MB (4.9x), 10 JPEGs deleted
2026-05-07T11:03:02.204893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:02.270520Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.248220Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.306387Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:59.105792Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T11:05:34.710050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:07:23.661054Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 75 eligible frames
2026-05-07T11:07:25.906642Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 10.0MB → 0.4MB (23.0x), 38 JPEGs deleted
2026-05-07T11:07:28.176089Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 35 frames, 6.9MB → 2.5MB (2.8x), 35 JPEGs deleted
2026-05-07T11:08:06.864979Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:15.119215Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:08:29.818126Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:32.981193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:33.042213Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:50.978274Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.734286Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.851489Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:07.119123Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:37.291815Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:09:40.319359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:14.288318Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:14.424670Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.847960Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.940298Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T11:10:50.032003Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:56.222667Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:56.312244Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:20.348299Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:21.120460Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.721850Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.804892Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.056996Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.140586Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.305845Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.383186Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.114228Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.191508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:35.851585Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:45.797893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:11:58.899193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:12:02.054717Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.130409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.755693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.846886Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:03.619909Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=visual_change)
2026-05-07T11:12:10.642255Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:10.727852Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.690324Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.781304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:21.621763Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:25.624917Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:28.182732Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 74 eligible frames
2026-05-07T11:12:30.153855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 7.1MB → 0.9MB (8.3x), 32 JPEGs deleted
2026-05-07T11:12:32.796836Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.6MB → 2.8MB (2.7x), 40 JPEGs deleted
2026-05-07T11:12:40.535068Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:40.647364Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.220156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.301848Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.222251Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.266764Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:13:01.235609Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:01.299808Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.450693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.586876Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.862308Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.940165Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:14:01.162441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:03.453473Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:08.993859Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:09.054039Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.312363Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.354292Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.396038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.489451Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:22.083385Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=visual_change)
2026-05-07T11:14:36.252205Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.015757Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.664591Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=visual_change)
2026-05-07T11:14:45.857760Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:45.904716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:47.073442Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T11:16:05.411899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture fo...
|
iTerm2
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screenpipe"
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NULL
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|
2026-05-07T09:45:22.811350Z INFO screenpipe_engin 2026-05-07T09:45:22.811350Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-05-07T09:45:22.811366Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-05-07T09:45:23.497850Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 1 excluded)
2026-05-07T09:45:23.576837Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 1: frame_id=155, dur=39ms
2026-05-07T09:45:23.594869Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 1 excluded)
2026-05-07T09:45:23.728324Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 2: frame_id=156, dur=87ms
2026-05-07T09:45:25.114503Z WARN screenpipe_a11y::tree::macos_lines: lines: AXUIElementCopyParameterizedAttributeValue(AXLineForIndex) failed status=os::Status { raw: -25212, fcc: "....", help: "https://www.osstatus.com?search=-25212" } — first failure (further failures suppressed); search highlights will fall back to paragraph bbox on this app
2026-05-07T09:45:29.149038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7681127700032315998, trigger=visual_change)
2026-05-07T09:45:48.802125Z INFO screenpipe_engine::meeting_detector: meeting v2: Idle -> Confirming (app=Firefox, signals=2)
2026-05-07T09:45:53.994026Z INFO screenpipe_engine::meeting_detector: meeting v2: Confirming -> Active (app=Firefox, signals=2, browser=true)
2026-05-07T09:45:53.994983Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting started (id=1, app=Firefox, title=None)
2026-05-07T09:45:53.995269Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=true, manual=false, active_id=Some(1), app=Some("Firefox"), source=Some("ui_scan")
2026-05-07T09:46:22.681313Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 77 eligible frames
2026-05-07T09:46:24.975724Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 7.6MB → 1.8MB (4.2x), 38 JPEGs deleted
2026-05-07T09:46:27.284414Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 5.7MB → 0.4MB (14.3x), 37 JPEGs deleted
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T09:51:27.298006Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 2 eligible frames
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T09:56:27.307108Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 45 eligible frames
2026-05-07T09:56:29.766853Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 29 frames, 4.6MB → 2.5MB (1.8x), 29 JPEGs deleted
2026-05-07T09:56:31.182378Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.5MB → 1.1MB (2.4x), 16 JPEGs deleted
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:00:50.039083Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=4913720669938969944, trigger=visual_change)
2026-05-07T10:01:31.184069Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 70 eligible frames
2026-05-07T10:01:33.701926Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 6.4MB → 2.7MB (2.4x), 37 JPEGs deleted
2026-05-07T10:01:35.921087Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 31 frames, 5.6MB → 1.7MB (3.3x), 31 JPEGs deleted
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:06:35.921469Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 37 eligible frames
2026-05-07T10:06:37.286011Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 18 frames, 3.2MB → 1.1MB (3.0x), 18 JPEGs deleted
2026-05-07T10:06:38.611334Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 17 frames, 3.2MB → 0.7MB (4.3x), 17 JPEGs deleted
2026-05-07T10:07:10.923890Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-363038137104660743, trigger=click)
2026-05-07T10:07:54.763156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=click)
2026-05-07T10:07:58.866469Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7750749190140780004, trigger=click)
2026-05-07T10:08:03.510124Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=visual_change)
2026-05-07T10:08:39.088273Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3643569030250279342, trigger=visual_change)
2026-05-07T10:09:18.346872Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6569246041316117301, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:10:36.954420Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4721988080886038618, trigger=visual_change)
2026-05-07T10:10:54.330626Z INFO screenpipe_engine::meeting_detector: meeting v2: Active -> Ending (no controls, app=Firefox, id=1, grace=300s)
2026-05-07T10:11:12.369084Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8225140425134796296, trigger=visual_change)
2026-05-07T10:11:38.678230Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:11:40.024314Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.8MB → 1.0MB (2.9x), 16 JPEGs deleted
2026-05-07T10:11:40.843106Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.0MB → 0.3MB (6.4x), 10 JPEGs deleted
2026-05-07T10:12:12.538283Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7652725149678140352, trigger=click)
2026-05-07T10:12:16.550570Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9173838423795606666, trigger=visual_change)
2026-05-07T10:12:31.745217Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:34.790862Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:37.784752Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:40.806177Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:43.854881Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:59.015149Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:14.166750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:17.184510Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:34.773468Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=click)
2026-05-07T10:13:44.486450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=visual_change)
2026-05-07T10:13:54.236529Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7077166956358270957, trigger=click)
2026-05-07T10:13:59.559230Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3980541844315414876, trigger=visual_change)
2026-05-07T10:14:11.809168Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6523625761174407917, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:15:45.403085Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:45.438150Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:58.662028Z INFO screenpipe_engine::meeting_detector: meeting v2: Ending -> Idle (timeout=300s, app=Firefox, id=1)
2026-05-07T10:15:58.663307Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting ended (id=1)
2026-05-07T10:15:58.663499Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=false, manual=false, active_id=None, app=None, source=None
2026-05-07T10:16:05.043304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=visual_change)
2026-05-07T10:16:06.212558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=click)
2026-05-07T10:16:10.088825Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.585397Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.626664Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:14.079057Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=visual_change)
2026-05-07T10:16:16.219212Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:16.238237Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.211409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.247202Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:37.973759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.014645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.719453Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.757458Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.323203Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.361472Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:40.850433Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 50 eligible frames
2026-05-07T10:16:42.489832Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 25 frames, 4.5MB → 1.4MB (3.2x), 25 JPEGs deleted
2026-05-07T10:16:44.044480Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 23 frames, 4.2MB → 1.1MB (3.7x), 23 JPEGs deleted
2026-05-07T10:16:44.383786Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=visual_change)
2026-05-07T10:17:13.547514Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:17:23.997418Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:17:36.067503Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=visual_change)
2026-05-07T10:17:48.254094Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:18:06.328441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:21.374558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:27.400579Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:36.379491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:18:39.375238Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:19:43.208935Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
2026-05-07T10:19:49.257421Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:21:21.848375Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=click)
2026-05-07T10:21:23.439805Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=visual_change)
2026-05-07T10:21:38.803777Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4689651471004117672, trigger=click)
2026-05-07T10:21:44.054102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 84 eligible frames
2026-05-07T10:21:46.307600Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 5.0MB → 1.3MB (3.8x), 38 JPEGs deleted
2026-05-07T10:21:49.031129Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.2MB → 1.4MB (5.2x), 44 JPEGs deleted
2026-05-07T10:23:02.085605Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:23:05.086593Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:24:05.661776Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
2026-05-07T10:24:09.585701Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:24:11.714956Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:25:31.235235Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4393937499657261667, trigger=visual_change)
2026-05-07T10:25:38.986311Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2365554338448923185, trigger=click)
2026-05-07T10:26:49.039316Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 88 eligible frames
2026-05-07T10:26:52.006289Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 42 frames, 9.5MB → 4.4MB (2.1x), 42 JPEGs deleted
2026-05-07T10:26:54.644073Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.6MB → 1.1MB (7.0x), 44 JPEGs deleted
2026-05-07T10:28:33.499505Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:35.956588Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:36.666492Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:36.789359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:39.100987Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:40.047061Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:40.064268Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:42.052599Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:44.774285Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:44.791022Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:45.378301Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:46.470785Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:46.575295Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.652750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.668490Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:48.439575Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:50.165309Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:50.177581Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:51.452287Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:51.994437Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:52.053841Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:54.432682Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:03.124980Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:03.868714Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:05.079320Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.543967Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.576904Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.792645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.865500Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:20.773748Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:21.723479Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:21.752924Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:23.883669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1480943928003130782, trigger=visual_change)
2026-05-07T10:29:35.016352Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:36.054767Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:36.117899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.114508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:38.937597Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.987983Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:31:28.462010Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:28.506716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:54.652328Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 102 eligible frames
2026-05-07T10:31:57.015395Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 13.6MB → 2.6MB (5.3x), 37 JPEGs deleted
2026-05-07T10:32:00.752190Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 63 frames, 7.5MB → 3.0MB (2.5x), 63 JPEGs deleted
2026-05-07T10:32:26.599963Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:32:27.400474Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:34:30.099383Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:34:30.729647Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:03.954351Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:13.717941Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8871609275972961978, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:37:00.792475Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 104 eligible frames
2026-05-07T10:37:03.726678Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 45 frames, 15.7MB → 2.8MB (5.7x), 45 JPEGs deleted
2026-05-07T10:37:07.426691Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 57 frames, 8.5MB → 3.4MB (2.5x), 57 JPEGs deleted
2026-05-07T10:37:20.569450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.162289Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.307132Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:30.755107Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:32.262354Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:32.364050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:45.907491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:47.205630Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:47.304637Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:41:59.417224Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5950044591583842886, trigger=click)
2026-05-07T10:41:59.477946Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5950044591583842886, trigger=click)
2026-05-07T10:42:04.742903Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:04.791839Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:07.459097Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 38 eligible frames
2026-05-07T10:42:08.925855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 20 frames, 4.3MB → 2.0MB (2.1x), 20 JPEGs deleted
2026-05-07T10:42:09.013373Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:09.056483Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:10.403020Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.7MB → 1.2MB (2.2x), 16 JPEGs deleted
2026-05-07T10:42:15.023525Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:15.087803Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:55.509669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2284708141138457574, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:47:10.412520Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 76 eligible frames
2026-05-07T10:47:12.469581Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 34 frames, 8.4MB → 1.8MB (4.7x), 34 JPEGs deleted
2026-05-07T10:47:15.163169Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.2MB → 2.4MB (3.0x), 40 JPEGs deleted
2026-05-07T10:49:59.513306Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-5898226934247819222, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:52:15.173889Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 73 eligible frames
2026-05-07T10:52:17.082276Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 8.4MB → 0.7MB (11.6x), 32 JPEGs deleted
2026-05-07T10:52:19.490930Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 39 frames, 5.9MB → 2.2MB (2.7x), 39 JPEGs deleted
2026-05-07T10:54:22.187516Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:54:28.229775Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:10.540942Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:13.602063Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:16.605531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:55:24.247307Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:57:19.473518Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:57:20.680885Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 11 frames, 2.9MB → 0.5MB (5.3x), 11 JPEGs deleted
2026-05-07T10:57:21.976198Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 15 frames, 2.5MB → 1.3MB (1.9x), 15 JPEGs deleted
2026-05-07T10:58:11.572541Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:11.672796Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:12.154759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:13.639499Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:15.413079Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:15.495531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:16.595925Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:21.437769Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.085129Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.869865Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.981197Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:42.120927Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:42.168157Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:59.393221Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3964650797953228084, trigger=click)
2026-05-07T10:58:59.482526Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=click)
2026-05-07T10:59:49.917826Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
2026-05-07T11:00:17.089194Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T11:02:21.972630Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 22 eligible frames
2026-05-07T11:02:22.860530Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.6MB → 0.5MB (4.8x), 10 JPEGs deleted
2026-05-07T11:02:23.655299Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 1.7MB → 0.4MB (4.9x), 10 JPEGs deleted
2026-05-07T11:03:02.204893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:02.270520Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.248220Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.306387Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:59.105792Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T11:05:34.710050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:07:23.661054Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 75 eligible frames
2026-05-07T11:07:25.906642Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 10.0MB → 0.4MB (23.0x), 38 JPEGs deleted
2026-05-07T11:07:28.176089Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 35 frames, 6.9MB → 2.5MB (2.8x), 35 JPEGs deleted
2026-05-07T11:08:06.864979Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:15.119215Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:08:29.818126Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:32.981193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:33.042213Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:50.978274Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.734286Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.851489Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:07.119123Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:37.291815Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:09:40.319359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:14.288318Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:14.424670Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.847960Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.940298Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T11:10:50.032003Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:56.222667Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:56.312244Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:20.348299Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:21.120460Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.721850Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.804892Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.056996Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.140586Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.305845Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.383186Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.114228Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.191508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:35.851585Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:45.797893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:11:58.899193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:12:02.054717Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.130409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.755693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.846886Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:03.619909Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=visual_change)
2026-05-07T11:12:10.642255Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:10.727852Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.690324Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.781304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:21.621763Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:25.624917Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:28.182732Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 74 eligible frames
2026-05-07T11:12:30.153855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 7.1MB → 0.9MB (8.3x), 32 JPEGs deleted
2026-05-07T11:12:32.796836Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.6MB → 2.8MB (2.7x), 40 JPEGs deleted
2026-05-07T11:12:40.535068Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:40.647364Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.220156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.301848Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.222251Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.266764Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:13:01.235609Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:01.299808Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.450693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.586876Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.862308Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.940165Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:14:01.162441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:03.453473Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:08.993859Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:09.054039Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.312363Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.354292Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.396038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.489451Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:22.083385Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=visual_change)
2026-05-07T11:14:36.252205Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.015757Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.664591Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=visual_change)
2026-05-07T11:14:45.857760Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:45.904716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:47.073442Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T11:16:05.411899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture fo...
|
iTerm2
|
screenpipe"
|
NULL
|
|
2026-05-07T09:45:22.811350Z INFO screenpipe_engin 2026-05-07T09:45:22.811350Z INFO screenpipe_engine::event_driven_capture: event-driven capture started for monitor 2 (device: monitor_2)
2026-05-07T09:45:22.811366Z INFO screenpipe_engine::vision_manager::monitor_watcher: Starting monitor watcher (event-driven via CGDisplayRegisterReconfigurationCallback, 60s backstop poll)
2026-05-07T09:45:23.497850Z INFO sck_rs::stream_manager: persistent SCK stream started for display 1 (1440x900, 2fps, 1 excluded)
2026-05-07T09:45:23.576837Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 1: frame_id=155, dur=39ms
2026-05-07T09:45:23.594869Z INFO sck_rs::stream_manager: persistent SCK stream started for display 2 (3008x1253, 2fps, 1 excluded)
2026-05-07T09:45:23.728324Z INFO screenpipe_engine::event_driven_capture: startup capture for monitor 2: frame_id=156, dur=87ms
2026-05-07T09:45:25.114503Z WARN screenpipe_a11y::tree::macos_lines: lines: AXUIElementCopyParameterizedAttributeValue(AXLineForIndex) failed status=os::Status { raw: -25212, fcc: "....", help: "https://www.osstatus.com?search=-25212" } — first failure (further failures suppressed); search highlights will fall back to paragraph bbox on this app
2026-05-07T09:45:29.149038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7681127700032315998, trigger=visual_change)
2026-05-07T09:45:48.802125Z INFO screenpipe_engine::meeting_detector: meeting v2: Idle -> Confirming (app=Firefox, signals=2)
2026-05-07T09:45:53.994026Z INFO screenpipe_engine::meeting_detector: meeting v2: Confirming -> Active (app=Firefox, signals=2, browser=true)
2026-05-07T09:45:53.994983Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting started (id=1, app=Firefox, title=None)
2026-05-07T09:45:53.995269Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=true, manual=false, active_id=Some(1), app=Some("Firefox"), source=Some("ui_scan")
2026-05-07T09:46:22.681313Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 77 eligible frames
2026-05-07T09:46:24.975724Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 7.6MB → 1.8MB (4.2x), 38 JPEGs deleted
2026-05-07T09:46:27.284414Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 5.7MB → 0.4MB (14.3x), 37 JPEGs deleted
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T09:51:27.298006Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 2 eligible frames
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T09:56:27.307108Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 45 eligible frames
2026-05-07T09:56:29.766853Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 29 frames, 4.6MB → 2.5MB (1.8x), 29 JPEGs deleted
2026-05-07T09:56:31.182378Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.5MB → 1.1MB (2.4x), 16 JPEGs deleted
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:00:50.039083Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=4913720669938969944, trigger=visual_change)
2026-05-07T10:01:31.184069Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 70 eligible frames
2026-05-07T10:01:33.701926Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 6.4MB → 2.7MB (2.4x), 37 JPEGs deleted
2026-05-07T10:01:35.921087Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 31 frames, 5.6MB → 1.7MB (3.3x), 31 JPEGs deleted
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:06:35.921469Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 37 eligible frames
2026-05-07T10:06:37.286011Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 18 frames, 3.2MB → 1.1MB (3.0x), 18 JPEGs deleted
2026-05-07T10:06:38.611334Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 17 frames, 3.2MB → 0.7MB (4.3x), 17 JPEGs deleted
2026-05-07T10:07:10.923890Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-363038137104660743, trigger=click)
2026-05-07T10:07:54.763156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=click)
2026-05-07T10:07:58.866469Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7750749190140780004, trigger=click)
2026-05-07T10:08:03.510124Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4602046684169633189, trigger=visual_change)
2026-05-07T10:08:39.088273Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3643569030250279342, trigger=visual_change)
2026-05-07T10:09:18.346872Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6569246041316117301, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:10:36.954420Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4721988080886038618, trigger=visual_change)
2026-05-07T10:10:54.330626Z INFO screenpipe_engine::meeting_detector: meeting v2: Active -> Ending (no controls, app=Firefox, id=1, grace=300s)
2026-05-07T10:11:12.369084Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8225140425134796296, trigger=visual_change)
2026-05-07T10:11:38.678230Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:11:40.024314Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.8MB → 1.0MB (2.9x), 16 JPEGs deleted
2026-05-07T10:11:40.843106Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.0MB → 0.3MB (6.4x), 10 JPEGs deleted
2026-05-07T10:12:12.538283Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7652725149678140352, trigger=click)
2026-05-07T10:12:16.550570Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9173838423795606666, trigger=visual_change)
2026-05-07T10:12:31.745217Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:34.790862Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:37.784752Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:40.806177Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:43.854881Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:12:59.015149Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:14.166750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:17.184510Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1669118135250037105, trigger=visual_change)
2026-05-07T10:13:34.773468Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=click)
2026-05-07T10:13:44.486450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6229566017341650404, trigger=visual_change)
2026-05-07T10:13:54.236529Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7077166956358270957, trigger=click)
2026-05-07T10:13:59.559230Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3980541844315414876, trigger=visual_change)
2026-05-07T10:14:11.809168Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=6523625761174407917, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:15:45.403085Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:45.438150Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6029182552546457581, trigger=click)
2026-05-07T10:15:58.662028Z INFO screenpipe_engine::meeting_detector: meeting v2: Ending -> Idle (timeout=300s, app=Firefox, id=1)
2026-05-07T10:15:58.663307Z INFO screenpipe_engine::meeting_detector: meeting v2: meeting ended (id=1)
2026-05-07T10:15:58.663499Z INFO screenpipe_engine::routes::meetings: meeting_status_changed: active=false, manual=false, active_id=None, app=None, source=None
2026-05-07T10:16:05.043304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=visual_change)
2026-05-07T10:16:06.212558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=312614534752492596, trigger=click)
2026-05-07T10:16:10.088825Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.585397Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:11.626664Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6145754442538527174, trigger=click)
2026-05-07T10:16:14.079057Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=visual_change)
2026-05-07T10:16:16.219212Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:16.238237Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.211409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:17.247202Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=806643008695069553, trigger=click)
2026-05-07T10:16:37.973759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.014645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.719453Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:38.757458Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.323203Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:39.361472Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8875948178524934281, trigger=click)
2026-05-07T10:16:40.850433Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 50 eligible frames
2026-05-07T10:16:42.489832Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 25 frames, 4.5MB → 1.4MB (3.2x), 25 JPEGs deleted
2026-05-07T10:16:44.044480Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 23 frames, 4.2MB → 1.1MB (3.7x), 23 JPEGs deleted
2026-05-07T10:16:44.383786Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-8875948178524934281, trigger=visual_change)
2026-05-07T10:17:13.547514Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:17:23.997418Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:17:36.067503Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=visual_change)
2026-05-07T10:17:48.254094Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8224741320031956579, trigger=click)
2026-05-07T10:18:06.328441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:21.374558Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:27.400579Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=8392580966194121284, trigger=visual_change)
2026-05-07T10:18:36.379491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:18:39.375238Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7524776963116161484, trigger=visual_change)
2026-05-07T10:19:43.208935Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
2026-05-07T10:19:49.257421Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840747455939898472, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:21:21.848375Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=click)
2026-05-07T10:21:23.439805Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=201887528283740068, trigger=visual_change)
2026-05-07T10:21:38.803777Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=4689651471004117672, trigger=click)
2026-05-07T10:21:44.054102Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 84 eligible frames
2026-05-07T10:21:46.307600Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 5.0MB → 1.3MB (3.8x), 38 JPEGs deleted
2026-05-07T10:21:49.031129Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.2MB → 1.4MB (5.2x), 44 JPEGs deleted
2026-05-07T10:23:02.085605Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:23:05.086593Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2104275679555505311, trigger=visual_change)
2026-05-07T10:24:05.661776Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
2026-05-07T10:24:09.585701Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=click)
2026-05-07T10:24:11.714956Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=763931354791105339, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:25:31.235235Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4393937499657261667, trigger=visual_change)
2026-05-07T10:25:38.986311Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2365554338448923185, trigger=click)
2026-05-07T10:26:49.039316Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 88 eligible frames
2026-05-07T10:26:52.006289Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 42 frames, 9.5MB → 4.4MB (2.1x), 42 JPEGs deleted
2026-05-07T10:26:54.644073Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 44 frames, 7.6MB → 1.1MB (7.0x), 44 JPEGs deleted
2026-05-07T10:28:33.499505Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:35.956588Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:36.666492Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:36.789359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:39.100987Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:40.047061Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:40.064268Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:42.052599Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:44.774285Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:44.791022Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:45.378301Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:46.470785Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:46.575295Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.652750Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:47.668490Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:48.439575Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:50.165309Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:50.177581Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:51.452287Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:28:51.994437Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:52.053841Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:28:54.432682Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:03.124980Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:03.868714Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:05.079320Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.543967Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:06.576904Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.792645Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:08.865500Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:20.773748Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:21.723479Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:21.752924Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:23.883669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1480943928003130782, trigger=visual_change)
2026-05-07T10:29:35.016352Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:36.054767Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:36.117899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.114508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=visual_change)
2026-05-07T10:29:38.937597Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4111022203962396269, trigger=click)
2026-05-07T10:29:38.987983Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4111022203962396269, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:31:28.462010Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:28.506716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=9014748524946296965, trigger=click)
2026-05-07T10:31:54.652328Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 102 eligible frames
2026-05-07T10:31:57.015395Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 37 frames, 13.6MB → 2.6MB (5.3x), 37 JPEGs deleted
2026-05-07T10:32:00.752190Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 63 frames, 7.5MB → 3.0MB (2.5x), 63 JPEGs deleted
2026-05-07T10:32:26.599963Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:32:27.400474Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:34:30.099383Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:34:30.729647Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:03.954351Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:35:13.717941Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-8871609275972961978, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:37:00.792475Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 104 eligible frames
2026-05-07T10:37:03.726678Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 45 frames, 15.7MB → 2.8MB (5.7x), 45 JPEGs deleted
2026-05-07T10:37:07.426691Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 57 frames, 8.5MB → 3.4MB (2.5x), 57 JPEGs deleted
2026-05-07T10:37:20.569450Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.162289Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:29.307132Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:30.755107Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:32.262354Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:32.364050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:45.907491Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=visual_change)
2026-05-07T10:37:47.205630Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=2524758617991974503, trigger=click)
2026-05-07T10:37:47.304637Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T10:41:59.417224Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5950044591583842886, trigger=click)
2026-05-07T10:41:59.477946Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5950044591583842886, trigger=click)
2026-05-07T10:42:04.742903Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:04.791839Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=5999585032610140654, trigger=click)
2026-05-07T10:42:07.459097Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 38 eligible frames
2026-05-07T10:42:08.925855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 20 frames, 4.3MB → 2.0MB (2.1x), 20 JPEGs deleted
2026-05-07T10:42:09.013373Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:09.056483Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3109201817482414574, trigger=click)
2026-05-07T10:42:10.403020Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 16 frames, 2.7MB → 1.2MB (2.2x), 16 JPEGs deleted
2026-05-07T10:42:15.023525Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:15.087803Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7036607934458099426, trigger=click)
2026-05-07T10:42:55.509669Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2284708141138457574, trigger=visual_change)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T10:47:10.412520Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 76 eligible frames
2026-05-07T10:47:12.469581Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 34 frames, 8.4MB → 1.8MB (4.7x), 34 JPEGs deleted
2026-05-07T10:47:15.163169Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.2MB → 2.4MB (3.0x), 40 JPEGs deleted
2026-05-07T10:49:59.513306Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-5898226934247819222, trigger=visual_change)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T10:52:15.173889Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 73 eligible frames
2026-05-07T10:52:17.082276Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 8.4MB → 0.7MB (11.6x), 32 JPEGs deleted
2026-05-07T10:52:19.490930Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 39 frames, 5.9MB → 2.2MB (2.7x), 39 JPEGs deleted
2026-05-07T10:54:22.187516Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:54:28.229775Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:10.540942Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:13.602063Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
2026-05-07T10:55:16.605531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1639151917924912395, trigger=visual_change)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T10:55:24.247307Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=2524758617991974503, trigger=click)
2026-05-07T10:57:19.473518Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 28 eligible frames
2026-05-07T10:57:20.680885Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 11 frames, 2.9MB → 0.5MB (5.3x), 11 JPEGs deleted
2026-05-07T10:57:21.976198Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 15 frames, 2.5MB → 1.3MB (1.9x), 15 JPEGs deleted
2026-05-07T10:58:11.572541Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:11.672796Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:12.154759Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:13.639499Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:15.413079Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:15.495531Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:16.595925Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=visual_change)
2026-05-07T10:58:21.437769Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.085129Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.869865Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:32.981197Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-2179464293260827512, trigger=click)
2026-05-07T10:58:42.120927Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:42.168157Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=1967027037406422417, trigger=click)
2026-05-07T10:58:59.393221Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3964650797953228084, trigger=click)
2026-05-07T10:58:59.482526Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=click)
2026-05-07T10:59:49.917826Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
2026-05-07T11:00:17.089194Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3964650797953228084, trigger=visual_change)
tip: install a starter bundle of pipes:
screenpipe install https://screenpi.pe/start.json
2026-05-07T11:02:21.972630Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 22 eligible frames
2026-05-07T11:02:22.860530Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 2.6MB → 0.5MB (4.8x), 10 JPEGs deleted
2026-05-07T11:02:23.655299Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 10 frames, 1.7MB → 0.4MB (4.9x), 10 JPEGs deleted
2026-05-07T11:03:02.204893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:02.270520Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.248220Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:05.306387Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
2026-05-07T11:03:59.105792Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-7900633084178088567, trigger=click)
tip: sign in for higher AI quotas + cloud sync:
screenpipe login
2026-05-07T11:05:34.710050Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:07:23.661054Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 75 eligible frames
2026-05-07T11:07:25.906642Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 38 frames, 10.0MB → 0.4MB (23.0x), 38 JPEGs deleted
2026-05-07T11:07:28.176089Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 35 frames, 6.9MB → 2.5MB (2.8x), 35 JPEGs deleted
2026-05-07T11:08:06.864979Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:15.119215Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:08:29.818126Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:32.981193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:33.042213Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:50.978274Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.734286Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:08:52.851489Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:07.119123Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:09:37.291815Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:09:40.319359Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:14.288318Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:14.424670Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.847960Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:16.940298Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
tip: get the screenpipe desktop app for the full experience
https://screenpi.pe
2026-05-07T11:10:50.032003Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:10:56.222667Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:10:56.312244Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:20.348299Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:21.120460Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.721850Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:23.804892Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.056996Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:29.140586Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.305845Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:30.383186Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.114228Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:32.191508Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=click)
2026-05-07T11:11:35.851585Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=3673172198391692637, trigger=visual_change)
2026-05-07T11:11:45.797893Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:11:58.899193Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:12:02.054717Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.130409Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.755693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:02.846886Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:03.619909Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=visual_change)
2026-05-07T11:12:10.642255Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:10.727852Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.690324Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:11.781304Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-3238466536228167005, trigger=click)
2026-05-07T11:12:21.621763Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:25.624917Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:28.182732Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: found 74 eligible frames
2026-05-07T11:12:30.153855Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 32 frames, 7.1MB → 0.9MB (8.3x), 32 JPEGs deleted
2026-05-07T11:12:32.796836Z INFO screenpipe_engine::snapshot_compaction: snapshot compaction: 40 frames, 7.6MB → 2.8MB (2.7x), 40 JPEGs deleted
2026-05-07T11:12:40.535068Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:40.647364Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.220156Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:41.301848Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.222251Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=7933120038421061284, trigger=click)
2026-05-07T11:12:43.266764Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=7933120038421061284, trigger=click)
2026-05-07T11:13:01.235609Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:01.299808Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.450693Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:32.586876Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.862308Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:13:44.940165Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-4952689273601773303, trigger=click)
2026-05-07T11:14:01.162441Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:03.453473Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:08.993859Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:09.054039Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.312363Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:12.354292Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.396038Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:21.489451Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=click)
2026-05-07T11:14:22.083385Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-6840767885990663182, trigger=visual_change)
2026-05-07T11:14:36.252205Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.015757Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:43.664591Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=visual_change)
2026-05-07T11:14:45.857760Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:45.904716Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 2 (hash=-1282942611038083818, trigger=click)
2026-05-07T11:14:47.073442Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture for monitor 1 (hash=-1282942611038083818, trigger=click)
tip: wire screenpipe into claude with one command:
claude mcp add screenpipe -- npx -y screenpipe-mcp
then ask claude to build a pipe that tracks who you are, your todos, and how you spend your time from your screen activity
2026-05-07T11:16:05.411899Z INFO screenpipe_engine::event_driven_capture: content dedup: skipping capture fo...
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iTerm2
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screenpipe"
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NULL
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Last login: Thu May 7 09:45:09 on ttys010
DOCKER
Last login: Thu May 7 09:45:09 on ttys010
DOCKER
Close Tab
DEV (-zsh)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
Python"
Close Tab
⌥⌘1
Python"...
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iTerm2
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Python"
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NULL
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Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
DOCKER
Close Tab
DEV (-zsh)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
-zsh...
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iTerm2
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-zsh
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NULL
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|
Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
DOCKER
Close Tab
DEV (-zsh)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
-zsh...
|
iTerm2
|
-zsh
|
NULL
|
|
Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
DOCKER
Close Tab
DEV (-zsh)
Close Tab
APP (-zsh)
Close Tab
-zsh
Close Tab
screenpipe"
Close Tab
-zsh
Close Tab
⌥⌘1
-zsh...
|
iTerm2
|
-zsh
|
NULL
|
|
Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Last login: Thu May 7 09:45:09 on ttys010
Poetry Last login: Thu May 7 09:45:09 on ttys010
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Last login: Thu May 7 09:45:09 on ttys010
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Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Last login: Thu May 7 09:45:09 on ttys010
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Poetry could not find a pyproject.toml file in /Users/lukas or its parents
Poetry could not find a pyproject.toml file in /Users/lukas or its parents
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~ $ cd ~/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ ll
total 667416
drwxr-xr-x 11 lukas staff 352 7 May 13:40 .
drwx------+ 93 lukas staff 2976 7 May 13:40 ..
drwxr-xr-x 18 lukas staff 576 6 May 20:31 data
-rw-r--r-- 1 lukas staff 336154624 7 May 13:40 db.sqlite
-rw-r--r-- 1 lukas staff 65536 7 May 10:42 db.sqlite-shm
-rw-r--r-- 1 lukas staff 4408432 7 May 13:40 db.sqlite-wal
drwxr-xr-x 8 lukas staff 256 6 May 20:27 pipes
-rw-r--r-- 1 lukas staff 28408 6 May 21:02 screenpipe.2026-05-06.0.log
-rw-r--r-- 1 lukas staff 159469 7 May 13:40 screenpipe.2026-05-07.0.log
-rwxr-xr-x 1 lukas staff 14994 6 May 20:26 screenpipe_sync.sh
-rw-r--r-- 1 lukas staff 3167 7 May 09:23 sync.log
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe
449M /Users/lukas/.screenpipe
lukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Edit
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
Retry
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Retry
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After...
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms....
|
Claude
|
Claude
|
NULL
|
|
Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untoucheo• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 g(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Sweight=:§limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcais acauire('burst") + acauiredaiv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms# Scratchpad1701 words remainingYou get 2000 words perweek. Upgrade for unlimitedUpgrade to Pro%, Invite your team# Get a free monthSettings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.A HS_Jocal (jiminny@localhost] console [PROD] x 4 console (EU)CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:03AsKJiminnykepoпtAcuivityservice lestntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.(from RateLimitException):+0 ..InsightsYour Usage Your VoiceYour Voice Profile is ready!Generate profileYour dictations are private and only stored locally. Never shared or stored by Wispr Flow. Your report is based on local data.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot';rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! OAbsolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryingihour. iob marked failedgetRetryAtteroRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and<> Code64-22UTF-8Tall aView allAccent allif 4 spaces...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelpDOCKERLast login: Thu May₴1DEV (-zsh)7 09:45:09on ttys010₴82APP (-zsh)Poetry could not find a pyproject.toml file in /Users/lukas or its parentsPoetry could not find a pyproject.tomlfile in /Users/lukas or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~$cd ~/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny~/.screenpipe $ 11total667416drwxr-xr-xdrwx-drwxr-xr-x11lukasstaff3527 May13:4093lukasstaff29767 May13:4018lukasstaff5766May20:31datalukasstaff3361546247May13:40db.sqlite-rw-r--r--lukasstaff655367May10:42db.sqlite-shm-rw-r--r--lukasstaff44084327May13:40db.sqlite-waldrwxr-xr-x8lukasstaff2566May20:27pipes-rw-r--r--lukasstaff284086May21:02screenpipe.2026-05-06.0.10g-rw-r--r--lukasstaff1594697May13:40screenpipe.2026-05-07.0.10g-rwxr-xr-xlukasstaff149946May20:26-rw-r--r--1 lukasstaff3167screenpipe_sync.sh7 May 09:23 sync. loglukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe449M/Users/lukas/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ П-zshallSupport Daily - in 1h 16 mA100% <478Thu 7 May 13:44:05181-zsh• 84|screenpipe*•$5-zsh...
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Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untoucheo• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 g(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Sweight=:$limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcais acauire('burst") + acauiredaiv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms• Scratchpad1701 words remainingweek. Upgrade for unlimitedUpgrade to Pro%, Invite your team# Get a free monthộ Settings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.A HS_Jocal (jiminny@localhost] console [PROD] x 4 console (EU)CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:08AsKJiminnykepoпtAcuivityservice lestntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.(from RateLimitException):+0 ..InsightsYour Usage Your VoiceYour Voice Profile is ready!Generate profileYour dictations are private and only stored locally. Never shared or stored by Wispr Flow. Your report is based on local data.bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot';rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! OAbsolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryingihour. iob marked failedgetRetryAtterRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42app/Jobs/Crm/Delete/[ DeleteCrmEntityTrait.php +18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and<> Code64-22UTF-8Tall aView allAccent allif 4 spaces...
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iTerm2ShellEditViewSessionScriptsProfilesWindowHel iTerm2ShellEditViewSessionScriptsProfilesWindowHelpDOCKERLast login: Thu May₴1DEV (-zsh)7 09:45:09on ttys010₴82APP (-zsh)Poetry could not find a pyproject.toml file in /Users/lukas or its parentsPoetry could not find a pyproject.tomlfile in /Users/lukas or its parentslukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~$cd ~/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny~/.screenpipe $ 11total667416drwxr-xr-xdrwx-drwxr-xr-x11lukasstaff3527 May13:4093lukasstaff29767 May13:4018lukasstaff5766May20:31datalukasstaff3361546247May13:40db.sqlite-rw-r--r--lukasstaff655367May10:42db.sqlite-shm-rw-r--r--lukasstaff44084327May13:40db.sqlite-waldrwxr-xr-x8lukasstaff2566May20:27pipes-rw-r--r--lukasstaff284086May21:02screenpipe.2026-05-06.0.10g-rw-r--r--lukasstaff1594697May13:40screenpipe.2026-05-07.0.10g-rwxr-xr-xlukasstaff149946May20:26-rw-r--r--1 lukasstaff3167screenpipe_sync.sh7 May 09:23 sync. loglukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ du -sh ~/.screenpipe449M/Users/lukas/.screenpipelukas@Lukas-Kovaliks-MacBook-Pro-Jiminny ~/.screenpipe $ П-zshallSupport Daily - in 1h 16 mA100% <478Thu 7 May 13:44:08181-zsh• 84|screenpipe*•$5-zsh...
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Wispr FlowFileDictationViewEditHelpHubSpot rate l Wispr FlowFileDictationViewEditHelpHubSpot rate limit implementation strategyWindowuntouchedl• Burst → Search: No. Saturating burst leaves Search's 5/s untouched• Either → Daily: Yes, but only by contributing to the sankills you here, not the type.In theory you can sustain ~50 search + ~190 other = ~240 d(until daily runs out), because the two buckets bill indepenImplementation consequenceYour rate limiter needs to know which bucket a request hitDauleril.Slimiter-›acouire"search', Swerghts):$limiter->acquire('burst', $weight=1);$limiter->acquire('daily', $weight=1);// for se// for e// everyn Redis: three senarate kevs, three senarate Lua acquire siparameter). A search call calls acquire('search') + acqcalis acauire('burst' + acauiredanv').One subtle pitfall: when you backoff on a 429, inspect polknow which bucket actually fired. If you see SECONDLY fro:to delay search-bound jobs — burst-bound jobs in the queunause evervthing on anv 429. vou waste capacity on the bi(or two throttle keys) make this clean; one global pause isThe other subtlety: the burst response gives you X-HubSpoocal sare aoainst. our searen coesn'"rerurn tnose neader.unverified — be slightly more conservative there (e.g., budunaccountee-tor callsKeep going in Claude CodeSwitch to Claude Code and let Claude work directly in yrunning and testing as it goes.Ibl Flow (Basioo Homelol InsightsD Dictionary* SnippetsTr Style*8. Transforms• Scratchpad1701 words remainingVoukUpoodwortnimifedUpgrade to Pro%, Invite your team# Get a free monthộ Settings® HelpOk, so what will happen each time I make a request? Let's say I have 100,000 requests to make thatare inctant Dolneed to inerement each requect haced on the tune? For eyamnle ifthere ic asearch request, I need to increment both the search bucket and also the daily bucket. If it's aburst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I needto see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure,adding additional load to each request?Opus 4.7 AdaptiveClaude is Al and can make mistakes. Please double-check responses.InsightsYour Usage Your VoiceA HS_Jocal (jiminny@localhost]A console [PROD] X A console [EU]CascadeHubspot Rate Limiting$0Support Daily - in 1h 16m100% 12Thu 7 May 13:44:09AsKJiminnykepoпtAcuivityservice lest+0 ..ntil is deadline, not delav. Job continues retrvina until now > retrvUnti10.rrom RateLimit Excent ionInsights building...Will notify you when it's ready!bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid-C66560290101') = uuid;provider = 'hubspot':rns the seconds to wait right now before this specitic attemot should retry. Comes from:Limiter:: requestavallablein → max or all bucket avallableino valuesn HubSpot's 429 response Retry-After headerUpdate ready! ®Absolute deadline Datelime)All failures (rate limit + real errors)Keep retrying with backoff() delays until deadlineYes (increments attempt count)gether in DeleteCrmEntityTrait:te limit → HandleRateLimit middleware catches itback to queue, attempt count stays the sameTter 10 seconds3 times with RFAI errors (not rate limit).ries, checks retryUntil()1 hour, keeps retryinghour. iob marked failedgetRetryAtterRelative delay int secondsOnly RateLimitExceptionRelease immediately with specific delayNo (release() doesn't increment attempts)-1 hour): Ensures we don't give up on deletes due to temporary issues (including sustained rate limiting)mit + aetRetrvAfter@: Ensures we don't burn through stries durina rate limit storms11, 3 rate limit hits would exhaust stries = 3 and fail the job. With retryUntil, those rate limit releases don't count against the try• can keen trving for an hour1| 14 files with changesapp/Jobs/Middleware/ HandleRateLimit.php +42ann/lohelCrm/Doloto/ DolotoCrmEntitvTrait nhn t18Reiect alllOK review documatation again to make sure we cover all rate limits there are for hubspot. Lets add support for different rate limit and< CodeTall aView allAccent allif 4 spaces...
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iTerm2
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds...
|
Claude
|
Claude
|
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|
|
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(...
|
Claude
|
Claude
|
NULL
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Retry
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
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Claude finished the response
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock)....
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically...
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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SELECT * FROM teams WHERE name LIKE '%litify%'; # 1069, 994, 24993
SELECT * FROM users WHERE id = 25061;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 994;
SELECT * FROM crm_profiles WHERE user_id = 25061;
select * from crm_configurations where id = 834;
SELECT * FROM teams WHERE id = 882;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations WHERE provider = 'hubspot' and crm_provider_id = 7270388;
SELECT * FROM contacts where crm_configuration_id = 834;
SELECT * FROM opportunities WHERE team_id = 933
# AND crm_provider_id IN ('20131586060','46017317898','52543911090','53451356564','54101251892','54323768459');
AND id IN (8482561,18352941,19042734,19232139,19445140,19472541);
SELECT * FROM opportunity_contacts
WHERE opportunity_id IN (8482561,18352941,19042734,19232139,19445140,19472541);
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 485; #
SELECT * FROM opportunities WHERE team_id = 933 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
select crm.provider, l.* from leads l join crm_configurations crm on l.crm_configuration_id = crm.id
where crm.provider NOT IN ('salesforce', 'integration-app', 'bullhorn', 'copper')
# and l.converted_at IS NOT NULL
;
# [PASSWORD_DOTS]
SELECT * FROM activities a WHERE type IN ('email-inbound', 'email-outbound')
and opportunity_id IS NULL
order by id desc;
SELECT * FROM teams WHERE id = 604; # 598
SELECT * FROM activities WHERE id = 74410828; # [EMAIL]
SELECT * FROM accounts WHERE id = 20068382;
SELECT * FROM accounts WHERE id = 35186038;
SELECT * FROM contacts WHERE team_id = 852 and updated_at > '2026-01-23 12:30:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 559 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('cb6342b6-a183-401c-b0af-ede92b2ae763') = uuid;
select * from sidekick_settings where team_id = 781;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 26651871; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 7562435;
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8420347; # opflit 2100
SELECT * FROM crm_layouts WHERE crm_configuration_id = 711;
SELECT * FROM activities where crm_configuration_id = 711 and crm_provider_id IS NULL
and is_internal = 0 and status = 'completed'
order by id desc;
SELECT * FROM crm_layout_entities
WHERE crm_layout_id IN (2352, 2353);
;
SELECT * FROM crm_configurations where provider = 'hubspot' and id = 530;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 556 and sa.provider = 'hubspot';
SELECT * FROM activities WHERE uuid_to_bin('c6ca4b22-7738-4563-a95d-b8a9598924ae') = uuid;
SELECT * FROM activities WHERE uuid_to_bin('442abb2b-28bd-4be8-9c25-19e9bf02766d') = uuid;
select * from contacts
where crm_configuration_id = 530
and crm_provider_id = 872252;
select * from activities where crm_configuration_id = 530
and user_id = 14343 and type like '%softphone%'
and created_at between '2026-01-28 15:00:00' and '2026-01-28 15:10:00';
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 25666868; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id = 8646335; # Teya
SELECT * FROM crm_configurations where provider = 'hubspot' and crm_provider_id IN (5933397);
SELECT t.name, t.id, t.owner_id, c.id, c.provider, c.crm_base_url FROM teams t
JOIN crm_configurations c ON t.id = c.team_id
WHERE t.status = 'active';
SELECT * FROM teams where id = 1091;
SELECT * FROM crm_configurations where team_id = 1091;
SELECT * FROM activity_providers where team_id = 1091;
SELECT * FROM activities where crm_configuration_id = 1024 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT * FROM teams WHERE name LIKE '%Leadventure%';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1091 and sa.provider = 'salesforce';
SELECT * FROM teams WHERE name LIKE '%Wilson%'; # 862, 812
SELECT * FROM teams where id = 862;
SELECT * FROM crm_configurations where team_id = 862;
SELECT * FROM activity_providers where team_id = 862;
SELECT * FROM activities where crm_configuration_id = 812 and type IN ('softphone', 'softphone-outbound')
and provider NOT IN ('hubspot', 'aircall')
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by id desc;
SELECT t.id, crm.id, crm.provider, ap.* FROM teams t
join crm_configurations crm on t.id = crm.team_id
join activity_providers ap on t.id = ap.team_id
where t.status = 'active' and ap.is_enabled = 1
and crm.provider = 'hubspot'
and ap.provider NOT IN ('hubspot', 'aircall', 'uploader', 'gong', 'twilio', 'zoom-bot', 'google-meet', 'ms-teams',
'outreach', 'close', 'ringcentral', 'dialpad', 'zoom-phone');
SELECT * FROM teams where id = 1068;
SELECT * FROM crm_configurations where team_id = 1068;
SELECT * FROM activity_providers where team_id = 1068;
SELECT * FROM activities a
where crm_configuration_id = 993 and type IN ('softphone', 'softphone-outbound')
and a.provider NOT IN ('hubspot', 'uploader', 'gong', 'twilio', 'google-meet', 'ms-teams','close'
)
# and telephony_provider_id = '019c1131-a22f-4792-b9ea-20adf6a02ed0'
order by a.id desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1068 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 882; # 933 - GoGlobal , portalId: 6017093
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 933 and updated_at > '2026-02-06 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 933 and sa.provider = 'hubspot';
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 834; # 882 - AnyVan , portalId: 5468262
SELECT * FROM contacts WHERE crm_configuration_id = 834 and updated_at > '2026-03-30 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and updated_at > '2026-03-04 08:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 882 and sa.provider = 'hubspot';
select * from crm_layouts where crm_configuration_id = 834;
select * from crm_layout_entities where crm_layout_id = 2780;
select * from crm_fields where id IN (321153,321192,321193,321194);
SELECT * FROM opportunities WHERE crm_configuration_id = 834 and id = 10993426;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 988; # 1057 - Teya (543ce4f4-168c-4571-91ea-5b35c253f06f) , portalId: 26651871
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1057 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1057 and sa.provider = 'hubspot';
SELECT * FROM crm_configurations where id = 533; # 559 - Connectd , portalId: 6710988
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 559 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 801; # 852 - Rise Vision , portalId: 2700250
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 852 and updated_at > '2026-02-04 00:00:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 962; # 1034 - evergrowth.io , portalId: 143180990
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1034 and updated_at > '2026-02-04 00:00:00' order by updated_at desc;
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 1037; # 1102 - Jibble , portalId: 6649755
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1102 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 8
SELECT * FROM crm_configurations where id = 1015; # 1049 - Travefy , portalId: 48904401
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1049 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 20
SELECT * FROM crm_configurations where id = 64; # 70 - SalaryFinance , portalId: 3404115
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 70 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 6th last
SELECT * FROM crm_configurations where id = 802; # 853 - Street Group , portalId: 7658438
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 853 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 10
SELECT * FROM crm_configurations where id = 872; # 921 - In Professional Development , portalId: 9238273
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 921 and updated_at > '2026-02-04 12:30:00' order by updated_at desc; # 2
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 550; # 576 - SeedLegals , portalId: 3028661
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 576 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 989; # 1058 - rtaoutdoor.com , portalId: 22371204
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1058 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 896; # 946 - Mintago , portalId: 6621281
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 946 and updated_at > '2026-02-05 14:00:00' order by updated_at desc;
SELECT * FROM crm_configurations where id = 617; # 641 - PCS , portalId: 5244937
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 641 and updated_at > '2026-02-05 14:00:00' order by updated_at desc; # 7th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where id = 649; # 670 - Eventeny , portalId: 4492849
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-18 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 670 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; #
SELECT * FROM crm_configurations where id = 48; # 51 - CleanCloud , portalId: 4373137
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-03-04 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 51 and updated_at > '2026-02-09 08:00:00' order by updated_at desc;
select * from users where team_id = 51; # 7783
SELECT * FROM groups WHERE uuid_to_bin('8a8d2cb6-8b55-4fa3-8b5c-5f0e3d8de59a') = uuid; # 1130
select * from activity_searches where user_id = 7783;
select * from activity_search_filters where activity_search_id IN (32291, 32292);
SELECT asf.activity_search_id, asf.id, asf.value
FROM activity_search_filters asf
WHERE asf.filter = 'group_id'
AND asf.value IN (
SELECT CONCAT(
HEX(SUBSTR(uuid, 5, 4)), '-',
HEX(SUBSTR(uuid, 3, 2)), '-',
HEX(SUBSTR(uuid, 1, 2)), '-',
HEX(SUBSTR(uuid, 9, 2)), '-',
HEX(SUBSTR(uuid, 11))
)
FROM groups
WHERE deleted_at IS NOT NULL
);
SELECT * FROM crm_configurations where id = 272; # 290 - Bonham & Brook , portalId: 5705856
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-05 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 290 and updated_at > '2026-02-09 08:00:00' order by updated_at desc; # 6th
# [PASSWORD_DOTS]
SELECT * FROM crm_configurations where provider = 'hubspot';
SELECT * FROM crm_configurations where id = 1056; # 1119 - Chromatic , portalId: 45602133
SELECT * FROM opportunities WHERE team_id = 1119 and remotely_created_at > '2026-02-01 00:00:00' order by updated_at desc;
SELECT * FROM opportunities WHERE team_id = 1119 and updated_at > '2026-02-09 09:00:00' order by updated_at desc; # null
# [PASSWORD_DOTS]
select * from contacts where crm_provider_id = '003Uu00000ojD4NIAU';
select
cp.*
# DISTINCT t.id
# cp.id, cp.user_id, t.id, cp.crm_configuration_id, cp.contact_fields
FROM crm_profiles cp
JOIN crm_configurations crm on crm.id = cp.crm_configuration_id
JOIN users u on u.id = cp.user_id
JOIN teams t ON t.id = crm.team_id
WHERE crm.provider = 'salesforce' and t.status = 'active'
and cp.archived_at IS NULL and u.deleted_at IS NULL
and t.id NOT IN (1093)
and t.id = 2
and cp.contact_fields IS NULL;
# and c.crm_provider_id = '003Uu00000ojD4NIAU';
SELECT * FROM users WHERE id = 26484;
SELECT * FROM crm_profiles WHERE user_id = 26484;
SELECT * FROM social_accounts WHERE sociable_id = 26484;
SELECT * FROM crm_configurations where provider = 'salesforce';
select * from users where id IN (10022, 10403);
select * from users where team_id IN (526);
select * from teams where id IN (526, 532);
select * from crm_configurations where id IN (500, 516);
select * from crm_profiles where crm_configuration_id IN (500, 516) and user_id IN (10022, 10403);
select * from contacts where crm_configuration_id IN (500, 516) and crm_provider_id = '003Uu00000ojD4NIAU';
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 526 and sa.provider = 'salesforce';
select * from team_settings where team_id IN (526, 532);
select * from users where id IN (22824);
select * from crm_profiles where crm_configuration_id IN (1026);
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1093 and sa.provider = 'salesforce';
select * from teams where id = 1099;
select * from users where id = 29643
select * from activity_processing_states;
SELECT * FROM teams where name LIKE '%Fare%'; # 233
SELECT * FROM opportunities where crm_configuration_id = 215
# and crm_provider_id = 'oppo_ogESZf2P50nDrd1nGPvKDXeA6sSaTN5v51Lp4ayVzKR'
;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1088 and sa.provider = 'hubspot';
SELECT * FROM teams order by updated_at DESC
SELECT * FROM crm_configurations WHERE id = 1019; # SimpleConsign 1088 - no social account
select * from crm_configurations where provider = 'pipedrive';
select * from teams where id = 957;
select * from crm_configurations where id = 957;
SELECT * FROM teams WHERE name LIKE '%Prolific%'; # 544, 518, 10743
SELECT * FROM opportunities where crm_configuration_id = 518 order by id desc;
select * from users where team_id = 1; # 26726 - Gabriela Dureva
SELECT * FROM opportunities where user_id = 26726; # 16834447 - Prolific
select * from activities where user_id = 26726 order by id desc;
select * from contacts where crm_configuration_id = 1
and email IN ('[EMAIL]', '[EMAIL]'); # 2094416, 2093620
SELECT * FROM contacts WHERE id = 6284931;
SELECT p.* FROM activities a JOIN participants p ON a.id = p.activity_id
WHERE a.user_id = 26726 and p.lead_id IN (2094416, 2093620) and a.created_at > '2026-01-01 00:00:00' order by p.email;
select * from activities where id IN (75509259,75509261,75509261,75511034,75026464,75517602,75517605);
select * from crm_configurations where id = 1;
43801692-1aeb-32ce-acba-5b80a479701a
44c3c9cf-6f5e-75f3-8179-bc9f75dd2b1b
405975c0-b3d0-7aaa-821f-09d59cae6dd1
4caf848d-4bed-2299-b248-7788d41f9fca
49bedc3f-f196-eef3-89c3-dea6a3b4aa63
43420989-a09d-b8f8-9806-c8bbf7a02aac
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
SELECT * FROM activities WHERE id = 75461988;
SELECT * FROM activities WHERE uuid_to_bin('d6c5052e-e972-49e9-8912-26f2f7d6c5f6') = uuid;
select * from contacts where id = 17900517;
select * from contact_roles cr join crm_configurations crm on cr.crm_configuration_id = crm.id
where crm.provider != 'salesforce';
select * from users where id = 21047;
SELECT * FROM crm_configurations WHERE id = 892;
SELECT * FROM teams WHERE id = 942;
select * from opportunities where team_id = 942 order by updated_at desc;
select * from contacts where team_id = 942 order by updated_at desc;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 942 and sa.provider = 'hubspot';
SELECT * FROM opportunities where team_id = 1 and crm_provider_id IN ('006Pq00000NeH6XIAV', '006Pq000007z8kdIAA'); # 10697889, 6621430
SELECT * FROM crm_configurations WHERE id = 1;
SELECT * FROM teams WHERE crm_id = 1;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1 and sa.provider = 'salesforce';
select id, user_id, opportunity_fields from crm_profiles where crm_configuration_id = 1
SELECT * FROM opportunities where team_id = 1 order by updated_at desc; # 10697889, 6621430
select * from teams where id = 852;
select * from groups where id = 2286;
select * from sidekick_settings where team_id = 852;
select * from default_activity_types where team_id = 852;
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id AND p.id IS NULL -- no profile
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active' -- team is active
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1 AND u.deleted_at IS NULL
AND u.crm_required = 1
AND u.team_id = 1
ORDER BY u.team_id;
SELECT * FROM crm_profiles cp where cp.crm_configuration_id = 1 and cp.user_id IN (
18481
);
SELECT cc.provider, cc.id, p.id, u.*
FROM users u
LEFT JOIN crm_profiles p ON u.id = p.user_id
INNER JOIN teams t ON u.team_id = t.id AND t.status = 'active'
INNER JOIN crm_configurations cc ON t.crm_id = cc.id
WHERE u.status = 1
AND u.deleted_at IS NULL
AND u.crm_required = 1
# AND u.team_id = 1
AND p.id IS NULL -- Move this condition to WHERE clause
ORDER BY u.team_id;
SELECT * FROM opportunities WHERE id = 20002609;
select * from teams where id = 1122; # Velatir, 29953 - [EMAIL]
select * from crm_configurations where id = 1060;
select * from crm_layouts where crm_configuration_id = 1060;
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3596;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 1122 and sa.provider = 'hubspot';
select * from opportunities where team_id = 1122 order by updated_at desc;
select * from crm_field_data where object_type = 'contact';
SELECT * FROM activities WHERE uuid_to_bin('374fc8ed-3315-4c9f-9b25-318b7fd2928f') = uuid; # 76584262
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 248 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles where user_id = 24115; # 005QF000002CswMYAS
SELECT * FROM users where id = 24115;
SELECT * FROM accounts where id = 4002896;
SELECT * FROM teams WHERE name LIKE '%adswerve%';
SELECT * FROM opportunities where crm_configuration_id = 230 AND crm_provider_id IN ("0069N000003GIQ9QAO","0061r000019yGP9AAM","0066900001S2KWlAAN","0066900001TDpj2AAD","0066900001b8uEwAAI","0069N000001rQi0QAE","006QF00000KD40mYAD","006QF00000LzpRJYAZ","0069N000002uomtQAA","0069N000002xlMLQAY","0066900001NV6ubAAD","0061r00001HJp45AAD","006QF00000uTlUoYAK","006QF00000v0bZqYAI");
SELECT * FROM opportunities WHERE crm_configuration_id = 230 AND crm_provider_id = '0069N000003GIQ9QAO'; # 6272203
SELECT u.id, u.email, ac.name, a.* FROM activities a
JOIN users u ON a.user_id = u.id
JOIN accounts ac ON a.account_id = ac.id
WHERE
uuid_to_bin('e3269598-b562-44fb-b5e9-9d2694dc63e0') = a.uuid or
uuid_to_bin('66ddc3ab-4e15-45aa-af0c-248c1eece593') = a.uuid or
uuid_to_bin('826bd328-e1cc-4213-b8d8-572454cacc07') = a.uuid;
select * from users where id = 5825;
SELECT * FROM activities WHERE uuid_to_bin('e56aa2e8-231a-421b-ab1f-cb38ed2bf573') = uuid;
select * from activities where uuid_to_bin('91e13b2f-2d1b-45f8-b1fd-1141b6563782') = uuid;
19594, 862
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 862 and sa.provider = 'salesforce';
select * from automated_reports where id = 36;
select ar.frequency, r.*, ar.* from automated_report_results r
join automated_reports ar on r.report_id = ar.id
where ar.frequency != 'one_off';
select s.* from activity_searches s join users u ON s.user_id = u.id where u.team_id = 882;
select * from nudges n where n.activity_search_id
select * from teams where created_at > '2026-03-09';
SELECT * FROM crm_layouts WHERE crm_configuration_id = 1065; # 1065
SELECT * FROM crm_layout_entities WHERE crm_layout_id = 3617;
select * from users where team_id = 1 and name like '%Lukas%'; # 7160
SELECT * FROM teams WHERE id = 575;
select * from opportunities where team_id = 575;
SELECT * FROM teams WHERE name LIKE '%Integrum ESG%'; # 1126, 1065,
select * from opportunities where team_id = 1126;
SELECT * FROM teams WHERE name LIKE '%Base%'; # 1125, 1063,
select * from opportunities where team_id = 1125;
select * from contacts c
where c.team_id = 882;
SELECT * FROM activities WHERE id = 76822967;
SELECT * FROM crm_profiles WHERE user_id = 15440;
SELECT * FROM crm_profiles WHERE crm_configuration_id = 555;
SELECT * FROM crm_configurations WHERE id = 555;
SELECT * FROM users WHERE id = 15440; # team. 581, gr. 15440, pl. 3911, act. field 162182
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 581 and sa.provider = 'salesforce';
SELECT * FROM automated_report_results order by id desc;
select * from features;
select * from team_features where feature_id = 40;
select * from teams where id = 556;
select * from automated_reports;
where id = 54; # 4fdd41f6-dcf0-30d0-b339-7345381b6044 , ["pdf","podcast"]
SELECT * FROM automated_report_results WHERE uuid_to_bin('822fa41b-afd3-43a9-a248-86b0e36f3131') = uuid;
select * from automated_report_results order by id desc;
SELECT * FROM automated_report_results WHERE id = 1919;
select * from automated_report_results WHERE report_id = 54;
select * from opportunities where id = 7594349;
SELECT * FROM teams WHERE name LIKE '%Les%'; # 711, 692, 16067 - [EMAIL]
select * from playbooks where team_id = 711; # event 226147
SELECT * FROM playbook_categories WHERE playbook_id = 5515;
SELECT * FROM crm_fields WHERE crm_configuration_id = 692 and object_type = 'event';
SELECT * FROM crm_fields WHERE id = 226147;
SELECT * FROM crm_field_values WHERE crm_field_id = 226147;
SELECT * FROM crm_configurations WHERE id = 692;
SELECT
CONCAT(u.id, CASE WHEN u.id = t.owner_id THEN ' (owner)' ELSE '' END) AS user_id,
u.email,
sa.*,
t.owner_id FROM social_accounts sa
JOIN users u on u.id = sa.sociable_id
JOIN teams t on t.id = u.team_id
WHERE u.team_id = 711 and sa.provider = 'salesforce';
SELECT * FROM crm_profiles cp JOIN users u on u.id = cp.user_id WHERE u.team_id = 711;
select * from leads;
select * from calendars;
SELECT
t.id AS team_id,
t.name,
LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1)) AS calendar_domain
FROM teams t
JOIN users u ON u.team_id = t.id
JOIN calendars c ON c.user_id = u.id AND c.status = 'active' AND c.calendar_provider_id LIKE '%@%'
LEFT JOIN team_domains td
ON td.team_id = t.id
AND td.deleted_at IS NULL
AND td.domain = LOWER(SUBSTRING_INDEX(c.calendar_provider_id, '@', -1))
GROUP BY t.id, t.name, calendar_domain
ORDER BY t.name, calendar_domain;
select * from users u join calendars c on c.user_id = u.id
where u.team_id = 882;
select * from activities where id = 74049485; # team 563 crm 537
select * from activities where id = 73272382; # team 563 crm 537
select * from activities where id = 64400389; # team 563 crm 537
select * from activities where id = 58081273; # team 563 crm 537
select * from activities where id = 54520297; # team 563 crm 537
select * from participants where activity_id = 58081273;
select * from activities where crm_configuration_id = 537 and provider = 'aircall'
and account_id = 19003658 order by updated_at desc;
select * from contacts where crm_configuration_id = 537 and id = 35957759;
select * from accounts where crm_configuration_id = 537 and id = 19003658;
select * from automated_report_results where id = 1976;
select * from automated_reports where id = 583;
select * from activity_searches where id = 87714;
select * from activity_search_filters where activity_search_id = 87714;
SELECT * FROM activities WHERE uuid_to_bin('8827f672-202d-4162-9d04-73ff5f0566a9') = uuid
or uuid_to_bin('47842446-af51-4bcb-854f-cc6560290101') = uuid;
SELECT * FROM crm_configurations WHERE provider = 'hubspot';
select * from rate_limits;
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<?php
declare(strict_types=1);
namespace Jiminny\Models;
use Illuminate\Database\Eloquent\Relations\MorphTo;
use Jiminny\Component\Eloquent\Builder;
use Jiminny\Contracts\Http\RateLimitInterface;
/**
* Jiminny\Models\RateLimit
*
* @property int $id
* @property \Illuminate\Support\Carbon|null $created_at
* @property \Illuminate\Support\Carbon|null $updated_at
* @property int $quota
* @property int $window
* @property int $limited_id
* @property string $limited_type
* @property-read \Illuminate\Database\Eloquent\Model $limited
*
* @method static Builder|RateLimit chunkByIdDesc($count, callable $callback, $column = null, $alias = null)
* @method static Builder|RateLimit newModelQuery()
* @method static Builder|RateLimit newQuery()
* @method static Builder|RateLimit query()
* @method static Builder|RateLimit whereCreatedAt($value)
* @method static Builder|RateLimit whereId($value)
* @method static Builder|RateLimit whereLimitedId($value)
* @method static Builder|RateLimit whereLimitedType($value)
* @method static Builder|RateLimit whereQuota($value)
* @method static Builder|RateLimit whereUpdatedAt($value)
* @method static Builder|RateLimit whereWindow($value)
*
* @mixin \Eloquent
*/
class RateLimit extends Model implements RateLimitInterface
{
protected $fillable = [
'quota',
'window',
];
public function limited(): MorphTo
{
return $this->morphTo();
}
public function getQuota(): int
{
return $this->attributes['quota'];
}
public function getWindow(): int
{
return $this->attributes['window'];
}
public function getKey(): string
{
$key = sprintf(
'%s.%s.%s.%s',
$this->limited_id,
$this->limited_type,
$this->getQuota(),
$this->getWindow()
);
return base64_encode($key);
}
}
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HubSpot rate limit implementation strategy, rename chat
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘...
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HubSpot rate limit implementation strategy, rename chat
HubSpot rate limit implementation strategy
More options for HubSpot rate limit implementation strategy
Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
Edit
Copy
Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
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You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
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Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
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You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
Show more
10:42
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
hubspot
hubspot
hubspot
hubspot
The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
hubspot
hubspot
hubspot
hubspot
Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
hubspot
hubspot
hubspot
hubspot
So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
Copy to clipboard
hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —
POST /crm/v3/objects/deals/batch/read
with 100 IDs per call → 6 calls. Burst weight 6.
Compute diff
in your code (no API).
Batch update
—
POST /crm/v3/objects/deals/batch/update
with 100 per call → 6 calls.
Associations
—
POST /crm/v4/associations/{from}/{to}/batch/create
if needed → another few batch calls.
Total: ~15-20 API calls for 600 deals. Burst budget consumed: 20/190 over ~5s. Daily impact: trivial.
Recognition: pre-check denies if
<weight>
slots aren't free; on 429 the response surfaces
policyName
(DAILY/SECONDLY) — log it, push the job back with
Retry-After
delay, increment a metric so you can alarm on sustained 429 rate.
Pitfalls & bottlenecks worth pre-empting
Pitfalls & bottlenecks worth pre-empting
Daily quota is shared across the whole portal
, including any n8n flows, other private apps, marketing automation calling APIs, etc. Budget for ~70-80% of the documented limit, not 100%. Add an internal per-app daily cap so a runaway sync can't starve other apps.
Search is uniquely fragile
: separate 5/s bucket, no headers to read, 10k-result cap per query. If you ever hit 10k+ deals matching a filter, partition by
hs_object_id
ranges or by
lastmodifieddate
windows.
Retry storms after HubSpot incidents
— always jitter, always cap total retries (e.g., 5 attempts, then dead-letter).
Webhook → sync → webhook loops
— when your update fires a HubSpot webhook that triggers another sync. Tag your own writes (e.g., a custom property
jy_last_sync_id
) and ignore webhooks where it just changed.
Token refresh
— OAuth refresh itself counts against burst. Cache the access token in Redis with TTL =
expires_in - 60s
, single-flight the refresh (only one worker refreshes; others wait on a lock).
Search latency for fresh writes
— newly created/updated objects don't appear in search instantly. Don't write-then-search; use returned IDs directly.
Clock skew
— Redis is authoritative. Don't use
time()
from each worker for window scoring; use
redis.call('TIME')
inside the Lua script.
Dead workers with held tokens
— sliding-window-log handles this naturally because tokens expire by score; token-bucket implementations need explicit release-on-crash logic.
The 5% error rule
counts every 429 toward your error budget, even successfully retried ones. If your pre-check is working well, 429s should be near zero — if they're not, your pre-check is wrong, not just slow.
Phantom contention with Laravel
RateLimited
middleware
: it's per-job-class by default, not per-portal. If your
SyncDealJob
and
UpdateActivityJob
are different classes both calling HubSpot, they don't share a Laravel-level limiter — you need a single
HubspotRateLimiter
service that everything routes through.
If you want, I can sketch the Lua script for the sliding-window acquire and the Laravel service wrapper around it.
Copy
Give positive feedback
Give negative feedback
You said: What will happen if there are two different types of rate limits?
You said: What will happen if there are two different types of rate limits?
What will happen if there are two different types of rate limits?
Search requests that have 5 requests per second
The other rate limit that has, let's say, 100 requests per 10 seconds How do these work together? Is there some conflict? Can I reach the limit with one request and block either as well, or are these separated? Can I still continue making search requests once I hit the normal requests limits?
13:40
Edit
Copy
Claude responded: They're independent buckets — with one shared bucket on top.
Claude responded: They're independent buckets — with one shared bucket on top.
Analyzed independent rate limit buckets and their practical interactions
Analyzed independent rate limit buckets and their practical interactions
They're independent buckets — with one shared bucket on top.
The three buckets and what each call hits
The three buckets and what each call hits
Copy to clipboard
┌──────────────────┐
Search request ──→ │ Search: 5/sec │ ──┐
└──────────────────┘ │
├──→ ┌─────────────────┐
┌──────────────────┐ │ │ Daily: 250k–1M │
Non-search call ──→ │ Burst: 190/10s │ ──┘ │ (per portal) │
└──────────────────┘ └─────────────────┘
A search call is only metered against the search bucket and the daily bucket. A non-search call (batch read/update, single PATCH, associations, etc.) is only metered against the burst bucket and the daily bucket. The two short-window buckets do not share counters.
What this means practically
What this means practically
Hitting search secondly limit (5/s):
Only further
/search
calls 429. Your batch reads, batch updates, single PATCHes, association calls — all keep working at full burst speed. This is actually useful: if your sync is bottlenecked on Search, you can pivot to using
lastmodifieddate
filters via batch endpoints, or use the IDs you already have to do batch reads, while Search cools off.
Hitting burst limit (190/10s):
Only non-search calls 429. You can still issue Search calls at 5/s. Useful for: continuing to identify deltas for queueing while in-flight updates drain, or refreshing a small set of records via Search even though your write pipeline is paused.
Hitting daily limit:
Everything stops. Search and non-search alike. Doesn't unblock until midnight in your portal's configured timezone — and remember this is shared across every private app in the portal, plus n8n flows, marketing tools, anything else hitting the API with that account.
So can one type of request block the other?
So can one type of request block the other?
Search → Burst:
No.
Saturating Search at 50 calls per 10s leaves your full 190/10s burst untouched.
Burst → Search:
No.
Saturating burst leaves Search's 5/s untouched.
Either → Daily:
Yes
, but only by contributing to the same daily counter. It's volume that kills you here, not the type.
In theory you can sustain ~50 search + ~190 other =
~240 calls per 10 seconds
indefinitely (until daily runs out), because the two buckets bill independently.
Implementation consequence
Implementation consequence
Your rate limiter needs to know which bucket a request hits before it acquires. Cleanest pattern:
Copy to clipboard
php
$limiter
->
acquire
(
'search'
,
$weight
=
1
)
;
// for search endpoints only
$limiter
->
acquire
(
'burst'
,
$weight
=
1
)
;
// for everything else
$limiter
->
acquire
(
'daily'
,
$weight
=
1
)
;
// every call, both kinds
In Redis: three separate keys, three separate Lua acquire scripts (or one script with a bucket parameter). A search call calls
acquire('search')
+
acquire('daily')
. A batch update calls
acquire('burst')
+
acquire('daily')
.
One subtle pitfall: when you backoff on a 429,
inspect
policyName
in the response body
to know which bucket actually fired. If you see
SECONDLY
from a search endpoint, you only need to delay search-bound jobs — burst-bound jobs in the queue can keep running. If you blanket-pause everything on any 429, you waste capacity on the bucket that's still healthy. Two queues (or two throttle keys) make this clean; one global pause is the lazy version.
The other subtlety: the burst response gives you
X-HubSpot-RateLimit-Remaining
to clamp local state against, but search doesn't return those headers. So your search counter is unverified — be slightly more conservative there (e.g., budget 4/s instead of 5) to absorb the unaccounted-for calls.
Copy
Give positive feedback
Give negative feedback
You said: Ok, so what will happen each time I make a request?
You said: Ok, so what will happen each time I make a request?
Ok, so what will happen each time I make a request? Let's say I have 100,000 requests to make that are instant. Do I need to increment each request based on the type? For example, if there is a search request, I need to increment both the search bucket and also the daily bucket. If it's a burst, then I can go to the daily and burst buckets. If I need to check if it can make a call, do I need to see both the daily and search, or burst, respectively? Isn't that too heavy for the infrastructure, adding additional load to each request?
13:44
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Quantifying Redis overhead versus HubSpot bottleneck constraints
Quantifying Redis overhead versus HubSpot bottleneck constraints
Quantifying Redis overhead versus HubSpot bottleneck constraints
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HubSpot rate limit implementation strategy, rename chat
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Claude is responding
You said: So just a solution for rate limit implementation.
You said: So just a solution for rate limit implementation.
So just a solution for rate limit implementation. The situation currently is that there are some daily and secondary limits for HubSpot. I would like to make sure I never reach a 429 error rate limit. The requests can come from any flow. It can be:
updating activity (engagement, meeting, call, note)
searching for a company or contact
updating deal
patching IDs
Review what the limit is at the moment in the HubSpot documentation and then think of the solution:
where to store it
how to read it
how to work with multiple jobs
how to work with paginated requests Let's consider a situation when there are 600 opportunities to sync. What would be the flow? How would the rate limit be recognized? How would it be handled? What are the potential issues? [API usage guidelines and limits - HubSpot docs](
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
https://developers.hubspot.com/docs/developer-tooling/platform/usage-guidelines
). Example of such a error Client error:
POST https://api.hubapi.com/crm/v3/objects/contact/search
resulted in a
429 Too Many Requests
response: {"status":"error","message":"You have reached your secondly limit.","errorType":"RATE_LIMIT","correlationId":"019dffc4-4 (truncated...). Think carefully about the implementation and potential issue and bottlenecks.
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Claude responded: Current HubSpot limits (Apr 2026)
Claude responded: Current HubSpot limits (Apr 2026)
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Synthesized HubSpot rate limit architecture with Redis storage and sliding window algorithms
Current HubSpot limits (Apr 2026)
Current HubSpot limits (Apr 2026)
For privately distributed apps (which is what Jiminny presumably uses with HubSpot), the limits are layered: a per-app burst window of 100/10s on Free/Starter, 190/10s on Pro/Enterprise, or 250/10s with the API Limit Increase add-on
, plus a per-account daily cap of 250k / 625k / 1M depending on tier — shared across all private apps in the account
. The burst window is per app; the daily quota is per portal.
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The Search API is special: five requests per second per account, capped at 200 records per page, with a hard 10,000-result ceiling per query
. That's the limit the 429 in your example is hitting (
secondly limit
on
/crm/v3/objects/contact/search
). It's a separate bucket from the 190/10s, and critically, search responses do not include the
X-HubSpot-RateLimit-*
headers
— you can't read remaining capacity off the response, you have to track it yourself.
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Non-search endpoints return
X-HubSpot-RateLimit-Remaining
,
-Max
, and
-Interval-Milliseconds
. The daily headers are not returned for OAuth-authenticated calls
, so for those you check the account-info endpoint or maintain your own counter. There's also a soft rule: errors should stay under 5% of total daily requests
— that's enforced for marketplace listing, but it's also a sane operational target.
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So you have
four buckets
to track at minimum: app-burst (10s sliding), search-secondly (1s sliding), account-daily (24h fixed, midnight in HubSpot's TZ), and per-app daily share (your own budgeting on top of the account cap).
Where to store it
Where to store it
Redis. It's the only realistic option once you have multiple queue workers — DB-backed counters serialize too much, and in-process state can't coordinate across workers. Laravel already speaks Redis natively, and you get atomic
INCR
/
EXPIRE
plus Lua scripting for true CAS semantics.
Key layout I'd use:
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hubspot:rl:burst:{portalId}:{appId} ZSET (sliding 10s)
hubspot:rl:search:{portalId} ZSET (sliding 1s)
hubspot:rl:daily:{portalId} STRING + TTL to midnight TZ
hubspot:rl:meta:{portalId} HASH (last seen headers)
The two short-window buckets are sliding-window-log (Redis ZSET, score =
microtime
, member = unique request id). Lua script removes entries older than the window, counts remaining slots, and only adds the new one if there's room — all atomic. Fixed windows are simpler but allow 2× the limit at the boundary, which on a 5/s window is brutal.
The daily bucket is a plain
INCR
with TTL set to seconds-until-midnight in
HubSpot's account timezone
, not yours — Sofia is UTC+2/+3 but your portal might be set to US Eastern. Get this once and cache it.
How to read it
How to read it
Two-phase: optimistic pre-check, authoritative post-update.
Before a call, run the Lua acquire script. If it returns "denied," sleep until a slot frees up (the script can return ms-until-next-slot) or push the job back to the queue with a delay. Don't busy-loop.
After the call, parse
X-HubSpot-RateLimit-Remaining
/
-Max
/
-Interval-Milliseconds
and store them in
hubspot:rl:meta:{portalId}
. This is your reality check — if your local counter says 50 remaining but HubSpot's header says 5, you trust HubSpot and clamp your counter. This handles clock drift, missed accounting (e.g., crashed worker that took a token but never made the request), and other apps in the same account consuming the daily budget invisibly.
For search specifically, since headers don't come back, the local counter
is
the source of truth — meaning if you ever crash mid-flight, you've under-counted. Always release tokens on connection-level failure but
not
on 429 (you really did make that request).
How to work with multiple jobs
How to work with multiple jobs
The queue layer needs to enforce concurrency separately from the rate limiter. Both work together:
Concurrency cap
via
Queue::throttle()
or a Redis semaphore — limits how many sync workers run in parallel against HubSpot. Without this, you can have 50 workers all blocked waiting for tokens, which is wasteful and creates retry storms.
Per-tenant fairness
— if you sync many Jiminny customers into different HubSpot portals, each portal has its own bucket but you still want one slow portal not to starve others. Either separate queues per portal or a fair-share scheduler.
Priority lanes
— webhook-driven updates (user-visible latency) should outrank background batch syncs. Two queues:
hubspot-priority
and
hubspot-bulk
, with priority workers taking 70% of the burst budget and bulk taking 30% (enforce via separate sub-buckets if you really want hard separation, otherwise just pull from priority first).
Backoff on 429
— respect the
Retry-After
header. If absent, exponential with jitter (e.g.,
min(2^attempt * 250ms, 30s) ± 20%
). The jitter is non-negotiable; without it, simultaneous 429s retry in lockstep and you get thundering-herd 429s on retry.
How to work with paginated requests
How to work with paginated requests
The trap is treating "fetch all" as one logical operation. Each page is its own API call and competes for tokens with everything else.
Two practical rules:
Don't hold the worker idle while paginating.
If page N takes 500ms and page N+1 needs to wait 800ms for a token, you've burned 800ms of worker time doing nothing. Instead, fetch page N, dispatch the
processing
of page N as a separate job, and queue a "fetch page N+1" job with a delay equal to the wait time. Each page becomes its own atomic unit.
Always batch where HubSpot offers it.
POST /crm/v3/objects/{type}/batch/{read|update|create}
accepts up to 100 IDs per call. For your "patching IDs" flow this is the difference between 600 calls and 6.
Walkthrough: 600 opportunities
Walkthrough: 600 opportunities
Naive flow (one PATCH per deal): 600 calls. At 190/10s that's ~32s of API time, well under daily. You'd hit burst easily without throttling. Plus likely 1-3 search calls upfront and N association calls.
Sane flow:
Identify deltas
— if you can avoid Search at all, do. Use
lastmodifieddate
filter on a single search if you must, paginate at limit=200. For 600 deals that's 3 search calls @ 5/s = ~600ms naturally throttled.
Batch read
current state —...
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Claude
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