Agent workflows belong in version control

Define, test, and ship agent DAGs like infrastructure code—with retry logic, gates, and streaming built in.

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Stop reinventing the same patterns

Every project hardcodes agent workflows in scripts or config files. No testing. No reuse. No history.

PipelineAsCode treats agent DAGs as first-class infrastructure. Git-friendly. Testable locally. Built for teams.

What you get

Python/TypeScript SDK

Define complex agent workflows with a clean DSL. Native support for branching, parallelism, and conditional logic.

Built-in primitives

Retry, exponential backoff, gates, streaming, and error handling—no boilerplate. Test workflows locally before shipping.

Version control ready

DAGs as code mean readable diffs, full git history, and code review workflows for your agent logic.

Template marketplace

Share and discover battle-tested pipelines. Start with 'analyze-patch-test-pr' or build your own.

How it works

Write your DAG in Python or TypeScript. Test locally with our CLI. Push to git. Deploy to our cloud or self-host. Monitor execution, iterate.

See the roadmap

For teams standardizing on agents

Whether you're building internal AI platforms or shipping agent-driven workflows, PipelineAsCode gives you the structure and reusability that manual scripts never will.

FAQ

Do I need to use your cloud hosting?

No. The SDK is open source and works locally. We offer optional cloud hosting for execution and monitoring; you can self-host if you prefer.

Can I share pipelines across teams?

Yes. The marketplace lets you version, tag, and share templates. Teams can fork and customize them.

What if I'm already using Airflow or Prefect?

Those tools excel at data pipelines. PipelineAsCode is built for agent workflows—streaming, gates, and dynamic branching without the overhead of a full orchestration platform.

How do I test workflows before production?

Our CLI includes a local test runner. Define fixtures, run your DAG end-to-end, assert outputs.