Open-source background job platform for AI agents providing long-running task execution, retries, and scheduling with serverless deployment.
Run long-running tasks in the background reliably — perfect for AI workflows, data processing, and automation that takes time.
Trigger.dev is an open-source background job platform that has become increasingly popular for running AI agent workloads. It provides the infrastructure for executing long-running tasks — exactly the kind of work AI agents do — with built-in retries, scheduling, concurrency control, and observability, all deployable as serverless functions.
The platform solves a fundamental problem in agent deployment: AI agent tasks often take seconds to minutes (or even hours for complex workflows), far exceeding typical serverless function timeouts and HTTP request limits. Trigger.dev provides long-running execution environments with configurable timeouts up to hours, automatic retries with exponential backoff, and real-time status updates.
Tasks are defined as TypeScript functions with decorators that specify retry behavior, timeout limits, concurrency settings, and scheduling. The developer experience is excellent — write your agent logic as a normal function, and Trigger.dev handles the infrastructure complexity of reliable background execution.
For AI agent use cases, Trigger.dev offers several critical features: task queuing for handling bursts of agent requests, fan-out/fan-in patterns for parallel agent execution, scheduled triggers for recurring agent tasks, and webhook triggers for event-driven agent activation. The platform includes built-in integrations with popular services and APIs.
The observability dashboard shows real-time task execution with detailed traces, making it easy to debug agent workflows. You can see exactly what each task did, how long each step took, and where failures occurred. This is essential for production agent systems where understanding execution flow is critical.
Trigger.dev offers both a cloud-hosted version and self-hosted deployment via Docker. The cloud version provides managed infrastructure with generous free tiers, while self-hosting gives full data control.
The platform has found strong adoption in AI agent deployments because it addresses the gap between 'my agent works in a notebook' and 'my agent runs reliably in production.' It handles the infrastructure concerns — execution duration, retries, scheduling, scaling, monitoring — that are orthogonal to agent logic but essential for production reliability.
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Execute tasks lasting minutes to hours with configurable timeouts, far beyond serverless function limits.
Use Case:
Running a multi-step research agent that searches, analyzes, and synthesizes information over 10-15 minutes.
Configurable retry policies with exponential backoff, jitter, and max retry limits for resilient agent execution.
Use Case:
Handling intermittent LLM API failures in a production agent by automatically retrying failed steps.
Trigger multiple parallel tasks and wait for all results, enabling concurrent agent execution and aggregation.
Use Case:
Running 10 research agents in parallel to gather information from different sources, then combining results.
Cron-based scheduling for recurring agent tasks with timezone support and overlap prevention.
Use Case:
Running a daily competitive analysis agent that checks competitor websites every morning.
Observability interface showing task execution traces, timing, status, and failure details for debugging.
Use Case:
Investigating why an agent workflow failed by examining the detailed execution trace in the dashboard.
Limit parallel executions per queue or globally to manage resource usage and API rate limits.
Use Case:
Limiting concurrent agent executions to stay within LLM API rate limits and control costs.
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View Pricing Options →Production agent deployment
Scheduled agent workflows
Parallel agent execution
Event-driven agent activation
We believe in transparent reviews. Here's what Trigger.dev doesn't handle well:
AI agents often need long-running execution, retries, scheduling, and concurrency control — exactly what Trigger.dev provides. It handles infrastructure complexity so you can focus on agent logic.
Trigger.dev is TypeScript-native. Python agents can be triggered via HTTP/webhooks, but the task definition layer is TypeScript. For Python-native alternatives, consider Temporal or Prefect.
Temporal is more powerful for complex workflow orchestration. Trigger.dev is simpler to get started with and better for teams that want quick deployment with less infrastructure overhead.
Yes, Trigger.dev is open-source and can be self-hosted via Docker with all features available.
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