Trigger.dev is an open-source platform that lets you build and deploy long-running background jobs and AI agent workflows in TypeScript. You write tasks as async functions in your codebase, and Trigger.dev handles queuing, retries, concurrency control, observability, and elastic scaling. It removes serverless timeouts and infrastructure headaches, making it a fit for AI pipelines, document processing, scheduled data syncs, and human-in-the-loop agents. Use the managed cloud or self-host under Apache 2.0.

• No timeouts on tasks: Run jobs for hours or days without hitting serverless limits. Ideal for LLM chains, video processing, and long AI agent loops.
• Automatic retries and checkpointing: Failed tasks retry with configurable backoff, and state is saved so runs resume where they left off after crashes or waits.
• Queues and concurrency control: Set per-task concurrency limits, named queues, priority queues, and debouncing to protect downstream APIs and LLM rate limits.
• AI agent support with waitpoints: Build durable agents that pause for human approval, external callbacks, or long waits, then resume without holding open connections.
• Realtime API and observability: Stream run status and LLM output to your frontend with React hooks, and inspect live traces powered by OpenTelemetry.
• Multi-environment deployment: Dev, staging, prod, and per-branch preview environments with GitHub and Vercel integration for automatic deploys.
• Configurable machines: Pick vCPUs and RAM per task, and run system packages like FFmpeg, browsers, or Python scripts.
• Open source and self-hostable: Apache 2.0 license with Docker Compose or Kubernetes self-hosting for full control over data and runs.

• Strong developer experience: Write jobs as plain TypeScript functions with type safety, local dev mode, and clear docs instead of clicking through a UI builder.
• Removes ops burden: Managed queues, workers, retries, and scaling let small teams ship reliable background infrastructure without a dedicated DevOps engineer.
• Purpose-built for AI workloads: Long execution times, streaming, human-in-the-loop, and Vercel AI SDK integration fit real agent patterns like routing, chaining, and orchestrator-workers.
• Generous free tier and open source: Free plan supports real workloads, and self-hosting is free under Apache 2.0 if you want full control.
• Solid observability: Live run traces, comprehensive logs, and real-time status views make debugging long-running jobs practical.
• Responsive community support: Users mention active Discord support and clear documentation as a plus over rolling your own stack.

• Cloud costs can grow with heavy workloads: Trigger.dev Cloud charges for compute time and task run invocations. Long-running jobs that use larger machines with more CPU and RAM can increase costs as usage grows.
• Cold start latency: Cloud runs take about 3 seconds on average to spin up a machine, which adds up for latency-sensitive workflows.
• Learning curve on advanced features: Waitpoints, checkpointing, and the v3 model take time to master, and past major version migrations included breaking changes.
• Limited team permissions: All team members currently have admin access, so you cannot restrict who can view API keys or edit environment variables.
• Cost scales with compute: Long, CPU-heavy jobs on large machines can get expensive quickly since you pay per second of execution plus per-run fees.

• AI agent orchestration: Run multi-step agent loops for tasks like phishing detection, fraud review, or compliance evidence gathering. Waitpoints let agents pause for human approval and resume without losing state.
• Document processing pipelines: Extract text from PDFs, classify content, run named entity recognition, and update databases as chained subtasks. Teams process thousands of documents per month with per-step retries.
• Long-running LLM workflows: Chain prompts for marketing copy generation, translation, and evaluation loops that run for minutes or hours without hitting HTTP timeouts.
• Scheduled data syncs: Pull data from third-party APIs on a cron schedule, transform it, and load it into your warehouse with automatic retries on failure.
• Media processing: Handle video transcoding, audio manipulation, or image generation using FFmpeg and system packages on machines sized to the workload.
• Webhook and event processing: Handle bursts of incoming events with debouncing, concurrency limits, and idempotency keys to prevent duplicate work.