Build and deploy fully-managed AI agents and background jobs in TypeScript — with no timeouts, durable retries, real-time observability, and elastic scaling built in.
Trigger.dev is an open-source platform designed for developers who need to run long-lived background tasks, scheduled jobs, and AI agent workflows without the constraints of serverless timeouts. It integrates directly into your TypeScript codebase — tasks live alongside your application code in version control, get reviewed like any other code, and deploy through a CLI with zero infrastructure to manage.
The platform is purpose-built for the era of AI workloads. Running an LLM pipeline, orchestrating a multi-step AI agent, processing a large batch of documents, or waiting on a human to approve a decision — these are workflows that can run for minutes or hours. Trigger.dev removes artificial execution limits while adding durable checkpointing, so a task interrupted mid-flight resumes exactly where it left off rather than starting over.
Observability is a first-class citizen. Every run produces a full OpenTelemetry trace, structured logs, and real-time streaming output you can pipe directly into your frontend using React hooks. You can subscribe to live run updates, stream AI-generated responses to end users, and attach metadata that updates as the task progresses — turning background jobs into foreground experiences.
Self-hosting is fully supported via Docker Compose and Kubernetes Helm charts, and a managed cloud tier at cloud.trigger.dev handles scaling automatically. Whether you’re a solo developer needing reliable cron jobs or an enterprise team orchestrating thousands of concurrent AI agents, Trigger.dev grows with the workload.
task(), schedules.task(), and schemaTask() APIs that live in your existing codebaseuseRealtimeRun, useTaskTrigger) to subscribe to live run state and stream AI responses to your frontendTrigger.dev is built on a TypeScript monorepo managed with pnpm workspaces and Turborepo for incremental builds. The SDK (@trigger.dev/sdk) exposes the task(), schemaTask(), and schedules APIs that compile into deployable workers using a custom esbuild-based build pipeline with pluggable extensions. Workers communicate with the Trigger.dev platform over a secure WebSocket protocol managed by the coordinator and supervisor apps.
The runtime uses a checkpoint-resume system backed by Kubernetes checkpointing (or in-memory snapshotting in Docker mode) that serializes task execution state to persistent storage. This is what enables durability across interruptions — a pod eviction or crash does not lose in-flight work. Waitpoints are implemented as distributed tokens stored in a Redis-backed queue; completing a token wakes the paused run without polling.
Observability is wired throughout the stack via OpenTelemetry. Every SDK operation — task start, subtask trigger, wait, retry, log statement — emits a span. These roll up into a hierarchical trace visible in the dashboard. The @trigger.dev/core package exposes the tracer, semantic attributes, and serialization utilities used across all packages.
What sets Trigger.dev apart architecturally is its hybrid execution model: developers write standard TypeScript functions, but the platform wraps them in a durable execution envelope at deploy time. There is no special API to learn for retries or checkpointing — they are ambient properties of every task. The AI layer (ai.ts, chat.ts, streams.ts in the SDK) adds first-class support for streaming LLM responses and composing agent skill graphs directly inside durable tasks.
Trigger.dev Cloud offers managed infrastructure with SOC 2 compliance, SSO, role-based access control, and priority support SLAs. Enterprise teams can also self-host using the official Helm chart for Kubernetes, enabling data residency and integration with internal secret managers. Volume pricing, dedicated infrastructure, and professional services are available — contact the team at trigger.dev for details.
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