At a Glance:
Trigger.dev is an open-source platform for building and deploying long-running AI agents and workflows in TypeScript, providing durable tasks with retries, queues, observability, elastic scaling, and support for human-in-the-loop patterns.
Overview:
Trigger.dev is an open-source platform for building and deploying AI agents and background workflows in TypeScript. It is designed to handle long-running, resource-heavy tasks without timeouts, using durable execution with automatic retries, queues, and idempotency. Developers can write tasks in their codebase using the JavaScript and TypeScript SDK, run system packages like browsers or FFmpeg, and integrate human approvals programmatically through waitpoints. The platform supports multiple environments for testing and production, offers real-time streaming and observability, and can be deployed to managed cloud infrastructure or self-hosted.
Key Decision Points:
Runtime and timeout handling: Tasks execute with absolutely no timeouts, making it suitable for long-running AI and resource-intensive jobs that exceed typical serverless platform limits.
Deployment model: The platform offers a managed cloud service with auto-scaling and no infrastructure to manage, and also supports self-hosting for users who need data control or custom infrastructure.
Human-in-the-loop support: Built-in waitpoints allow programmatic pauses for human approval or feedback without breaking the workflow, which is critical for regulated or review-heavy AI agent steps.
Task environment customization: Developers can customize the runtime environment with system packages like browsers, Python scripts, and FFmpeg, enabling non-JavaScript execution steps within a task.
Development workflow integration: Preview branch support integrates with Vercel and git workflows to create isolated testing environments, and the SDK allows localhost development with version control.
Core Features:
JavaScript and TypeScript SDK: Write background tasks using standard programming models with structured input and output schemas, runtime payload validation, and versioned deployments.
Durable long-running tasks: Execute tasks with checkpointing, automatic retries on uncaught errors, idempotency, and no timeout limits.
Waitpoints and Waits: Pause task execution for a set duration or until a human provides approval, rejection, or feedback at critical decision points.
Realtime streaming and subscriptions: Subscribe to run updates and stream AI responses to frontend applications, with React hooks for API interaction.
Bulk operations and batch triggering: Initiate multiple runs with custom payloads using batchTrigger(), and perform replay or cancel actions on multiple runs simultaneously.
Observability and monitoring: Full logging, tracing, and a trace view for every run, configurable real-time alerts, and tagging for filtering across dashboard, realtime, and SDK.
Use Cases:
Developers building AI agents that require durable task execution: Build agents using familiar TypeScript tooling and frameworks, then execute tasks that run longer than serverless timeouts with built-in retries and queues.
Developers integrating human approval steps into automated workflows: Use waitpoints to pause AI-driven workflows for manual review at specific decision points before continuing execution.
Self-hosters deploying background task infrastructure: Deploy Trigger.dev on self-hosted infrastructure to run and monitor AI workflows without relying on a managed cloud.
Developers executing non-JavaScript runtime dependencies: Run Python scripts, headless browsers, or media processing tools like FFmpeg as part of a TypeScript task by customizing the build process and runtime environment.
Open-Source Alternative Value:
Trigger.dev provides an open-source, TypeScript-native approach to building and deploying AI agents and durable background workflows. Unlike typical serverless environments that enforce timeout limits, Trigger.dev is explicitly designed for long-running tasks with built-in retries, queues, and human-in-the-loop patterns. Developers can use the SDK to write tasks within their codebase, customize runtime environments with system-level packages, and choose between a managed cloud service or self-hosting. The platform's waitpoints, preview environments, and real-time streaming directly support AI agent development patterns without requiring separate task orchestration and observability infrastructure.
