Trigger.dev is an open-source platform for building AI workflows in TypeScript and deploying fully managed AI agents and workflows. Its core positioning is AI agent development, allowing developers to build agents using the frameworks, services, and LLMs they are already familiar with, then deploy them to Trigger.dev to gain durable, long-running task capabilities, along with out-of-the-box features such as retries, queues, observability, and elastic scaling.
In terms of main capabilities, Trigger.dev supports long-running tasks with no timeout limit, unlike serverless platforms such as AWS Lambda and Vercel; it provides durability, retries, queues, and idempotency for building stable agents and AI applications; it supports free runtime customization, allowing browsers, Python scripts, FFmpeg, and more to be run through system packages; it supports human-in-the-loop, allowing tasks to be paused programmatically to wait for human approval, rejection, or feedback; it supports real-time applications and streaming, allowing subscription to run status or streaming AI responses to applications; it provides observability and monitoring, with complete traces and logs for every run, and configurable error alerts.
Feature highlights include: JavaScript and TypeScript SDK, using a familiar programming model to build background tasks; long-running tasks, handling resource-intensive tasks without timing out; durable cron scheduling, allowing recurring schedules of up to one year to be created and attached; Trigger.dev Realtime, triggering, subscribing to, and receiving real-time updates for runs, with support for LLM streaming; build extensions, which can connect directly to the build system and customize the build process, running Python scripts, FFmpeg, browsers, and more; React hooks, using the React hooks package on the frontend to interact with the Trigger.dev API; batch triggering, using batchTrigger() to launch multiple task runs with custom payloads and options; structured input/output, defining precise data schemas for tasks through runtime payload validation; wait, adding a pause of a specified duration to a task; preview branches, creating isolated testing and development environments and integrating with Vercel and git workflows; Waitpoints, adding human judgment at critical decision points without interrupting the workflow; concurrency and queues, setting concurrency rules to manage how multiple tasks execute; multiple environments, supporting DEV, PREVIEW, STAGING, and PROD environment; no infrastructure management required, auto-scaling infrastructure eliminates timeouts and server management; automatic retries, tasks automatically run again when they encounter uncaught errors; checkpoints, tasks are naturally durable through the checkpoint feature; version control, atomic version control allows deploying new versions without affecting running tasks; machine configuration, configurable number of vCPUs and GB of memory used by tasks; observability and monitoring, monitor every aspect of task performance through comprehensive logs and visualization tools; logs and tracing, comprehensive logs and tracing for all tasks; tags, attach up to ten tags per run, filterable through the dashboard, real time, and SDK; run metadata, attach metadata to runs, updated as the run progresses and available in the frontend for real-time updates; bulk actions, perform actions on multiple runs at the same time, including replay and cancel; real-time alerts, choose preferred notification methods for run failures and deployments.
In terms of usage, developers can write tasks in their own codebase, use the SDK to write tasks, without managing infrastructure, tasks automatically scale and connect to the cloud, or they can choose self-hosting. The platform supports Development, Staging, Preview, and Production environments, allowing tasks to be tested before deploying to production. Every task run has a complete trace view, allowing you to see what happened at each step. The quickest way to get started is to create an account and project in the web app, follow the guided instructions, and build and deploy your first task within minutes. Official documentation is also provided, including quick start, how it works, guides, and examples. If you prefer self-hosting, you can refer to the Docker self-hosting guide (using Docker Compose to start a Trigger.dev instance) or the Kubernetes self-hosting guide (using the official Helm chart to deploy to a Kubernetes cluster).
Applicable scenarios include: building AI agents and AI workflows that need to run for a long time with no timeout limits; stable AI applications that need durability, retries, queues, and idempotency; processes that need human-in-the-loop approval or feedback; applications that need real-time updates and LLM streaming; tasks that need to run custom runtimes such as browsers, Python scripts, FFmpeg, and more; background tasks that need full observability and monitoring.
In terms of limitations, the material does not provide specific pricing, costs, platform support scope, or performance data; this information is not currently available.
