nicktrn
4f69c43e6b
When a run reaches a terminal state, ask the checkpoint service to
reclaim the storage its checkpoints occupied. Storage for finished runs
is not otherwise reclaimed, so nothing frees it today.
**Off by default** behind `DELETE_CHECKPOINTS_ON_COMPLETION`, and the
service-side handler ships separately, so merging this changes no
behaviour.
## Where the tenancy comes from
Addressing a run's checkpoints needs org, project, environment,
deployment version and run id. All five are already in hand at
`attempt.complete`, and three are **signed** by the deployment token:
| Value | Source | Trust |
| -- | -- | -- |
| org | claim `org_id` | signed |
| environment | claim `environment_id` | signed |
| deployment version | claim `deployment_version` | signed |
| project ref | `x-trigger-workload-project-ref` header |
runner-supplied |
| run | route param | runner-supplied |
`authorizeWorkloadRequest` previously returned only `environment_id`,
and only in enforce mode, so it now also returns the verified `claims`.
That difference is deliberate and documented on the method: claims are
used to address a run's **own** resources locally, never to scope the
platform, which is why `environmentId` stays enforce-only.
The two runner-supplied values are safe because the signed ones are
outermost - a runner lying about either can only name something inside
its own org and environment, and a project ref that doesn't pair with
its signed environment matches nothing. The run id is read from
`params.runFriendlyId`, the same value the platform just validated,
rather than from the body or a header. Where both a claim and a header
exist (`deployment_version`), the claim wins.
## Placement
The call sits after `reply.json(...)`, so the runner sees no added
latency - the same shape the suspend route already uses. The service
enqueues and returns 202, so it is one fast local hop.
Terminal means `RUN_FINISHED` **or `RUN_PENDING_CANCEL`** - a run
cancelled mid-execution never restores, and skipping it would leave its
storage behind. Retries are excluded deliberately: reclamation is
per-run, so a retry is covered by the final completion.
Also gated on `!snapshotService`, so it stays inert where checkpoints
aren't the kind this reclaims.
## Observability
`checkpoint_delete_requests_total{result}` counts `sent` **and every
reason we decide not to send**: `disabled`, `not_terminal`, `no_claims`,
`no_project_ref`, `http_error`.
The negative labels are the point - without them, "no requests are
happening" looks identical to the feature being switched off.
`no_claims` is reachable even under enforcement, since enforce only
rejects a *present-but-invalid* token; an absent or legacy id still
passes with no claims attached.
## Notes for review
- **No changeset**: `CheckpointClient` is `core/v3/serverOnly`, an
internal service-to-service API rather than customer-facing surface.
- **No `.server-changes/` note**: there is nothing a dashboard user
would notice here. Happy to add one if you disagree.
- `pnpm run typecheck` can't complete in my checkout -
`@trigger.dev/database` fails to build on a missing `tsc` in the pnpm
store, unrelated to this diff. Verified with `tsc --noEmit` against the
supervisor project instead: **zero errors in `apps/supervisor/src`**.
Worth noting it caught a real bug here - the completion response is
wrapped, so the status is `data.result.attemptStatus`.
refs TRI-12789
Build and deploy fully‑managed AI agents and workflows
Website | Docs | Issues | Example projects | Feature requests | Public roadmap | Self-hosting
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.
-
Long-running without timeouts: Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
-
Durability, retries & queues: Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
-
True runtime freedom: Customize your deployed tasks with system packages – run browsers, Python scripts, FFmpeg and more.
-
Human-in-the-loop: Programmatically pause your tasks until a human can approve, reject or give feedback.
-
Realtime apps & streaming: Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
-
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
We have a large active community in our official Discord server for support, including a dedicated channel for self-hosting.
Development
To setup and develop locally or contribute to the open source project, follow our development guide.