Two new subpath exports:
@trigger.dev/sdk/chat (frontend, browser-safe):
- TriggerChatTransport — ChatTransport implementation for useChat
- createChatTransport() — factory function
- TriggerChatTransportOptions type
@trigger.dev/sdk/ai (backend, adds to existing ai.tool/ai.currentToolOptions):
- chatTask() — pre-typed task wrapper with auto-pipe
- pipeChat() — pipe StreamTextResult to realtime stream
- CHAT_STREAM_KEY constant
- ChatTaskPayload type
- ChatTaskOptions type
- PipeChatOptions type
Co-authored-by: Eric Allam <eric@trigger.dev>
Use the already-resolved token when creating ApiClient instead of
calling resolveAccessToken() again through getApiClient().
Co-authored-by: Eric Allam <eric@trigger.dev>
The accessToken option now accepts either a string or a function
returning a string. This enables dynamic token refresh patterns:
new TriggerChatTransport({
taskId: 'my-task',
accessToken: () => getLatestToken(),
})
The function is called on each sendMessages() call, allowing fresh
tokens to be used for each task trigger.
Co-authored-by: Eric Allam <eric@trigger.dev>
ChatSessionState is an implementation detail of the transport's
session tracking. Users don't need to access it since the sessions
map is private.
Co-authored-by: Eric Allam <eric@trigger.dev>
Adds 3 additional test cases:
- Abort signal gracefully closes the stream
- Multiple independent chat sessions tracked correctly
- ChatRequestOptions.body is merged into task payload
Co-authored-by: Eric Allam <eric@trigger.dev>
Tests cover:
- Constructor with required and optional options
- sendMessages triggering task and returning UIMessageChunk stream
- Correct payload structure sent to trigger API
- Custom streamKey in stream URL
- Extra headers propagation
- reconnectToStream with existing and non-existing sessions
- createChatTransport factory function
- Error handling for API failures
- regenerate-message trigger type
Co-authored-by: Eric Allam <eric@trigger.dev>
New package that provides a custom AI SDK ChatTransport implementation
bridging Vercel AI SDK's useChat hook with Trigger.dev's durable task
execution and realtime streams.
Key exports:
- TriggerChatTransport class implementing ChatTransport<UIMessage>
- createChatTransport() factory function
- ChatTaskPayload type for task-side typing
- TriggerChatTransportOptions type
The transport triggers a Trigger.dev task with chat messages as payload,
then subscribes to the task's realtime stream to receive UIMessageChunk
data, which useChat processes natively.
Co-authored-by: Eric Allam <eric@trigger.dev>
The global rate limiter was being applied at the FairQueue claim phase,
consuming 1 token per queue-claim-attempt rather than per item
processed.
With many small queues (each batch is its own queue), consumers burned
through tokens on empty or single-item queues, causing aggressive
throttling well below the intended items/sec limit.
Changes:
- Move rate limiter from FairQueue claim phase to BatchQueue worker
queue
consumer loop (before blockingPop), so each token = 1 item processed
- Replace the FairQueue rate limiter with a worker queue depth cap to
prevent unbounded growth that could cause visibility timeouts
- Add BATCH_QUEUE_WORKER_QUEUE_MAX_DEPTH env var (optional, disabled by
default)
Input streams enable sending typed data to executing tasks from external
callers — backends, frontends, or other tasks. This unlocks interactive
use cases like approval UIs, cancel buttons, chat interfaces, and
human-in-the-loop AI workflows where the task needs to receive data
while running.
Three consumption patterns inside a task:
* `.wait()` — Suspend the task until data arrives (process freed, most
efficient)
* `.once()` — Wait for the next message (process stays alive)
* `.on()` — Subscribe to a continuous stream of messages
One send pattern from outside:
* `.send(runId, data)` — Send typed data to a specific run's input
stream
## User-facing API
### Define a typed input stream
```ts
import { streams, task } from "@trigger.dev/sdk";
const approval = streams.input<{ approved: boolean; reviewer: string }>({ id: "approval" });
```
### Consume inside a task
```ts
export const myTask = task({
id: "my-task",
run: async () => {
// Pattern 1: Suspend until data arrives (most efficient — frees the process)
const result = await approval.wait({ timeout: "5m" });
// Pattern 2: Wait for next message (process stays alive)
const data = await approval.once().unwrap();
// Pattern 3: Subscribe to multiple messages
approval.on((data) => { /* handle each message */ });
},
});
```
### Send from outside
```ts
// From a backend (using secret API key)
await approval.send(runId, { approved: true, reviewer: "alice" });
// From a frontend (using public JWT token from trigger response)
const { send } = useInputStreamSend("approval", runId, { accessToken });
send({ approved: true, reviewer: "alice" });
```
---------
Co-authored-by: Claude <noreply@anthropic.com>
- New User onboarding questions added and stored in a new
`onboardingData` col
- Keeps the same Org creation screen and stores the data in the same
format in same DB column
- New Org onboarding questions addded and stored in a new
`onboardingData` col
https://github.com/user-attachments/assets/244e4bae-f74d-4ed4-a545-92c9b927e98b
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Pins vouch actions to `c6d80ead49839655b61b422700b7a3bc9d0804a9`
(v1.4.2) in favor of security practices. We were previously using the
`@main` tag.
Also removes the checkout steps as they're not needed in these
workflows.
There's a bug in react-window-splitter on Firefox. When trying to expand
the inspector panel in the query editor, it checks if the main panel has
space but gets an object instead of a number for the auto-sized
query-main panel. This causes the expand calculation to fail and it
snaps it back to collapsed.
I've removed this behavior for now as it's not an important feature.
Gracefully handle oversized batch items instead of aborting the stream.
When an NDJSON batch item exceeds the maximum size, the parser now emits
an error marker instead of throwing, allowing the batch to seal
normally. The oversized item becomes a pre-failed run with
`PAYLOAD_TOO_LARGE` error code, while other items in the batch process
successfully. This prevents `batchTriggerAndWait` from seeing connection
errors and retrying with exponential backoff.
Also fixes the NDJSON parser not consuming the remainder of an oversized
line split across multiple chunks, which caused "Invalid JSON" errors on
subsequent lines.
Replace flat master queue index with two-level tenant dispatch to fix
noisy neighbor problem. When a tenant has many queues at capacity, the
scheduler now iterates tenants (Level 1) not queues, then fetches
per-tenant queues (Level 2) only for eligible tenants.
Single-deploy migration: new enqueues write to dispatch indexes only,
consumer drains old master queue alongside new dispatch path until
empty.
Clarifies in the Query docs that run metadata is not available on the
Query page and that the output column is JSON, so dot notation (e.g.
output.externalId) should be used for selecting and filtering. Adds an
example that filters by an output field in WHERE
Split the CTE in blockRunWithWaitpoint so the pending waitpoint check
is a separate SQL statement. In READ COMMITTED isolation, each statement
gets its own snapshot, so a separate SELECT sees the latest committed
state from concurrent completeWaitpoint calls.
Previously, the CTE did INSERT + pending check in one statement (one
snapshot). If completeWaitpoint committed between the CTE start and
the SELECT, the SELECT would still see PENDING due to the stale
snapshot. Neither side would enqueue continueRunIfUnblocked, leaving
the run stuck forever.
Closes #
## ✅ Checklist
- [ ] I have followed every step in the [contributing
guide](https://github.com/triggerdotdev/trigger.dev/blob/main/CONTRIBUTING.md)
- [ ] The PR title follows the convention.
- [ ] I ran and tested the code works
---
## Testing
Verified the pricing definition displays the correct tier amount on the
plan selection page.
---
## Changelog
Fixed incorrect pricing tier for additional realtime connections from
$10/month per 100 to $10/month per 1000.
---
## Screenshots
N/A
💯https://claude.ai/code/session_015QrZZJHPWta3QCBnhX2Pff
Co-authored-by: Claude <noreply@anthropic.com>
Fix slow fair queue processing by removing spurious cooloff on
concurrency blocks and fixing a race condition where retry attempt
counts were not atomically updated during message re-queue.
Removed cooloff entirely from the batch queue
- Fix for series color assignment being out of sync with the graph
(ensures added series colors match their graph representation)
- Reload widgets when returning to screen if props changed (prevents
stale widgets after filtering and scrolling)
- Add .server-changes/ convention for tracking server-only changes
- Create scripts/enhance-release-pr.mjs to deduplicate and categorize
changeset PR body
- Create scripts/generate-github-release.mjs to format unified GitHub
release body
- Change release.yml to create one unified GitHub release instead of
per-package releases
- Add update-release job to patch Docker image link after images are
pushed to GHCR
- Update changesets-pr.yml to trigger on .server-changes, enhance PR
body, and clean up consumed files
- Document server changes in CLAUDE.md, CONTRIBUTING.md, CHANGESETS.md,
and RELEASE.md
What changed
- Fixed some functions like dateAdd, toString, ifNotFinite
- Removed all functions that accept lambdas as they're not supported
(yet)
- Added tests for all TRQL functions that use ClickHouse
Adds OpenAPI specs and sidebar pages for four previously undocumented
public endpoints: retrieve
run result, per-task batch trigger, retrieve batch, and retrieve batch
results.
This PR implements a new run TTL system and queue size limits to prevent
unbounded queue growth which should help prevent situations where queues
enter a "death spiral" where the queue will never be able to catch up.
The main/correct way to battle this situation is to enforce a maximum
TTL on all runs (e.g. up to 14 days) where runs that have been queued
for that maximum TTL will get auto-expired, making room for newer runs
to execute. This required creating a new TTL system that can handle
higher workloads and is now deeply integrated into the RunQueue. When
runs are enqueued with a TTL, they are added to their normal queue as
well as to the TTL queue. When runs are dequeued, they are removed from
both their normal queue and the TTL queue. If runs are dequeued by the
TTL system, they are removed from their normal queue. Both these
dequeues happen automatically so there is no race condition.
The TTL expiration system is also made reliable by expiring runs via a
Redis worker, which is enqueued to atomically inside the TTL dequeue lua
script.
### Optional associated waitpoints
Additionally, this PR implements an optimization where runs that aren't
triggered with a dependent parent run will no longer create an
associated waitpoint. Associated waitpoints are then lazily created if a
dependent run wants to wait for the child run post-facto (via debounce
or idempotency), which is a rare situation but is possible. This means
fewer waitpoint creations but also fewer waitpoint completions for runs
with no dependencies.
### Environment Queue Limits
Prevents any single queue growing too large by enforcing queue size
limits at trigger time.
- Queue size checks happen at trigger time - runs are rejected if queue
would exceed limit
- Dashboard UI shows queue limits on both the Queues page and a new
Limits page
- In-memory caching for queue size checks to reduce Redis load
### Batch trigger fixes
Currently when a batch item cannot be created for whatever reason (e.g.
queue limits) the run will never get created, which means a stalled run
if using `batchTriggerAndWait`. We've updated the system to handle this
differently: now when a batch item cannot be triggered and converted
into a run, we will eventually (after retrying 8 times up to 30s) we
will create a "pre-failed" run with the error details, correctly
resolving the batchTriggerAndWait.
Without doing an expensive query we can’t tell if it’s definitely a v3
projects – like getting run counts.
So let’s just assume if the project hasn’t been upgraded to v4 (by
running dev/deploy CLI with v4) AND the project is older than the v4
release then it’s v3.
Small fixes and improvements to the logs page:
- Clicking the Run ID didn't open inspector
- Swapped the "open link in tab" icon with Runs icon
- Prevent tooltip hovering on Level info
<img width="350" height="206" alt="CleanShot 2026-02-20 at 10 00 37@2x"
src="https://github.com/user-attachments/assets/3e82f24a-c0a1-4c01-a8e9-9e06a8af982a"
/>
Cursor Agent
