Matt Aitken
1114d9d6f9
## Summary Fair queue consumers could leak the per-tenant concurrency slots that gate admission. Slots were freed on some paths and skipped on others, and once enough leaked slots accumulated for a tenant, every queue that tenant owned stopped being served until someone cleared the set by hand. This PR frees slots on every path and, more importantly, makes the remaining failure modes self-healing. ## Design The fix applies one rule uniformly: releasing a concurrency slot is best-effort cleanup and must never block the message's primary state transition. Blocking completion re-delivers the message, which duplicates customer work; blocking a retry loses the attempt increment, so the message can circle forever; blocking a reclaim strands the message in flight. A leaked slot is the better failure in every one of those trades because it is the only one that is recoverable. A failed release is therefore logged and the transition proceeds. Leaked slots then heal through two mechanisms: - `reserve` re-admits a message that is already a member of its own concurrency set, since re-admitting it does not increase concurrency. A message whose earlier release failed can no longer be blocked by its own leftover slot. - A reconcile loop periodically removes any set member with no in-flight record (interval configurable via `reconcileIntervalMs`, default 60s). The check-and-remove is atomic, and it is sound because a message is always registered in flight before its slot is reserved, so a member with no in-flight record can only be a leak. This also covers leaks this PR cannot prevent directly, such as a release that resolves the wrong concurrency group from queue metadata. Ordering hardening from earlier revisions stays: slots are released before the in-flight record needed to describe them is discarded, the release Lua scripts write the message back to the queue before removing it from in-flight (Lua does not roll back on error), and dangling in-flight entries with no payload are dropped instead of being rescanned forever. Every guard test was verified to fail without its specific fix, including the duplicate-execution case: completing a message while its slot release fails used to re-deliver and re-execute it.
Build and deploy fully‑managed AI agents and workflows
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About Trigger.dev
Trigger.dev is the open-source platform for building AI workflows in TypeScript. Long-running tasks with retries, queues, observability, and elastic scaling.
The platform designed for building AI agents
Build AI agents using all the frameworks, services and LLMs you're used to, deploy them to Trigger.dev and get durable, long-running tasks with retries, queues, observability, and elastic scaling out of the box.
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Long-running without timeouts: Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
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Durability, retries & queues: Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
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True runtime freedom: Customize your deployed tasks with system packages – run browsers, Python scripts, FFmpeg and more.
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Human-in-the-loop: Programmatically pause your tasks until a human can approve, reject or give feedback.
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Realtime apps & streaming: Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
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Observability & monitoring: Each run has full tracing and logs. Configure error alerts to catch bugs fast.
Key features:
- JavaScript and TypeScript SDK - Build background tasks using familiar programming models
- Long-running tasks - Handle resource-heavy tasks without timeouts
- Durable cron schedules - Create and attach recurring schedules of up to a year
- Trigger.dev Realtime - Trigger, subscribe to, and get real-time updates for runs, with LLM streaming support
- Build extensions - Hook directly into the build system and customize the build process. Run Python scripts, FFmpeg, browsers, and more.
- React hooks - Interact with the Trigger.dev API on your frontend using our React hooks package
- Batch triggering - Use batchTrigger() to initiate multiple runs of a task with custom payloads and options
- Structured inputs / outputs - Define precise data schemas for your tasks with runtime payload validation
- Waits - Add waits to your tasks to pause execution for a specified duration
- Preview branches - Create isolated environments for testing and development. Integrates with Vercel and git workflows
- Waitpoints - Add human-in-the-loop judgment at critical decision points without disrupting workflow
- Concurrency & queues - Set concurrency rules to manage how multiple tasks execute
- Multiple environments - Support for DEV, PREVIEW, STAGING, and PROD environments
- No infrastructure to manage - Auto-scaling infrastructure that eliminates timeouts and server management
- Automatic retries - If your task encounters an uncaught error, we automatically attempt to run it again
- Checkpointing - Tasks are inherently durable, thanks to our checkpointing feature
- Versioning - Atomic versioning allows you to deploy new versions without affecting running tasks
- Machines - Configure the number of vCPUs and GBs of RAM you want the task to use
- Observability & monitoring - Monitor every aspect of your tasks' performance with comprehensive logging and visualization tools
- Logging & tracing - Comprehensive logging and tracing for all your tasks
- Tags - Attach up to ten tags to each run, allowing you to filter via the dashboard, realtime, and the SDK
- Run metadata - Attach metadata to runs which updates as the run progresses and is available to use in your frontend for live updates
- Bulk actions - Perform actions on multiple runs simultaneously, including replaying and cancelling
- Real-time alerts - Choose your preferred notification method for run failures and deployments
Write tasks in your codebase
Create tasks where they belong: in your codebase. Version control, localhost, test and review like you're already used to.
import { task } from "@trigger.dev/sdk";
//1. You need to export each task
export const helloWorld = task({
//2. Use a unique id for each task
id: "hello-world",
//3. The run function is the main function of the task
run: async (payload: { message: string }) => {
//4. You can write code that runs for a long time here, there are no timeouts
console.log(payload.message);
},
});
Deployment
Use our SDK to write tasks in your codebase. There's no infrastructure to manage, your tasks automatically scale and connect to our cloud. Or you can always self-host.
Environments
We support Development, Staging, Preview, and Production environments, allowing you to test your tasks before deploying them to production.
Full visibility of every job run
View every task in every run so you can tell exactly what happened. We provide a full trace view of every task run so you can see what happened at every step.
Getting started
The quickest way to get started is to create an account and project in our web app, and follow the instructions in the onboarding. Build and deploy your first task in minutes.
Useful links:
- Quick start - get up and running in minutes
- How it works - understand how Trigger.dev works under the hood
- Guides and examples - walk-through guides and code examples for popular frameworks and use cases
Self-hosting
If you prefer to self-host Trigger.dev, you can follow our self-hosting guides:
- Docker self-hosting guide - use Docker Compose to spin up a Trigger.dev instance
- Kubernetes self-hosting guide - use our official Helm chart to deploy Trigger.dev to your Kubernetes cluster
Support and community
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Development
To setup and develop locally or contribute to the open source project, follow our development guide.