Drop-in AI agent infrastructure
Focus on building intelligent AI agents, not wrestling with servers. Get the reliability, control, and observability required to deploy invincible AI agents that actually work in production.
Agents that live in your codebase
Break less things with totally type-safe agents
Our TypeScript SDK provides end-to-end type safety for your AI agents. Define your tasks with Zod schemas and automatically convert them to type-safe tools for AI SDK. And our atomic versioning ensures that you can safely update your agents without breaking changes.
Built on strong foundations
The 6 pillars of building agents
Long-running without timeouts
Execute your tasks with absolutely no timeouts, unlike AWS Lambda, Vercel, and other serverless platforms.
Durability, retries & queues
Build rock solid agents and AI applications using our durable tasks, retries, queues and idempotency.
Human-in-the-loop
Programmatically pause your tasks until a human can approve, reject or give feedback.
Realtime apps & streaming
Move your background jobs to the foreground by subscribing to runs or streaming AI responses to your app.
Observability & monitoring
Each run has full tracing and logs. Configure error alerts to catch bugs fast. Monitor performance with metrics.
Library, model, and framework agnostic
Plays well with others
It is easy to build and adapt your workflows using the latest libraries, models or frameworks as soon as they're released.
AI SDK
A TypeScript-first library for building AI applications with multi-provider support, streaming responses, tool usage, web search.
Agents SDK (JS/TS)
Build agentic AI applications with function calling, streaming, guardrails, tool usage, and multi-step reasoning.
Agents SDK (Python)
With the Trigger.dev Python extension your can use Python libraries like OpenAI's Agents SDK, Langchain, and more.
Use cases
AI agents in action
From basic prompt chains to sophisticated multi-step workflows, see how developers are building production AI agents. These examples showcase the patterns and techniques that work in real-world applications. All of these examples are open source and have full documentation for you to learn from.
Prompt chaining
Generate and translate
Route through LLMs
Evaluator-optimizer
Translate and refine
Orchestrator
Verify news articles
Parallelization
Moderate content
Autonomous agent
Deep research
Modern agent infrastructure
The building blocks for agents
Write tasks in regular code
Build agents using familiar programming models in native Javascript / Typescript and Python.
Waitpoints
With waitpoints you can add human judgment at critical decision points without disrupting the overall workflow.
Structured inputs / outputs
Define precise data schemas for your agents using SchemaTask to ensure consistent and predictable interactions.
Observability
Monitor every aspect of your agents' performance with comprehensive logging and visualization tools
Machines
Configure the number of vCPUs and GBs of RAM you want the task to use.
Autoscaling
Elastic infrastructure which auto-scales resources up or down as needed.
A real runtime
Control the deployed packages using our build extensions (control browsers, run Python, use FFmpeg, etc).
Trigger.dev Realtime
Subscribe to runs for real-time updates. Stream AI responses directly to users with built-in observability.
Concurrency controls
Control the concurrency of your tasks from one at a time, to parallel, to per-tenant queuing using concurrency keys.
Versioning
We use atomic versioning to ensure that started tasks are not affected by changes to the task code.
Sandboxing (coming soon)
Generate code at runtime and execute it in secure cloud sandboxes.
Interruptions (coming soon)
Interrupt calls from external services mid-flow and clean up allocated resources.