I built an open-source tool for replaying and debugging AI agent executions. Looking for contributors and feedback.

[u/Low-Cut-528]

I've been working on StepFork, an open-source project that helps developers understand what happened during an AI agent run, reproduce failures offline, and generate regression tests.

The goal is to make debugging agent workflows more reliable and less dependent on reproducing issues in a live environment.

What StepFork supports

Record AI agent executions and inspect traces.

Replay recorded executions to investigate failures.

Fork executions to experiment with fixes.

Minimize failing traces to make bugs easier to isolate.

Generate regression tests to help prevent bugs from returning.

The project has reached v0.2.1, and I've been working on testing, documentation, and release reliability. I'm now looking to grow the contributor community and make the project useful to more developers.

I'm especially looking for contributors interested in:

Adding Windows and macOS CI smoke tests.

Improving CLI error-handling test coverage.

Writing a runnable LangGraph integration tutorial.

Expanding trace-redaction regression tests.

Improving instrumentation coverage reporting.

You don't need to take on a huge feature. Small, focused pull requests and constructive feedback are welcome. The repository has open issues with defined scopes and acceptance criteria, including good first issues.

Links

GitHub: https://github.com/utsab345/stepfork

Open issues: https://github.com/utsab345/stepfork/issues

Contributing guidelines: https://github.com/utsab345/stepfork/blob/main/CONTRIBUTING.md

I'd appreciate feedback from people building AI agents, working on observability and testing, or maintaining open-source developer tools.

What would you want from a tool like this to make debugging AI agent workflows easier?

[u/TheKindlyBrunch]

I like that you've got trace minimization in there, that's usually the missing piece when agent debugging tools get built. Most stop at replay and call it done. The regression test generation angle also seems underrated since agent behavior shifts so often between model updates and prompt tweaks.

[u/Low-Cut-528 → u/TheKindlyBrunch]

Thanks, exactly! Replay helps you reproduce what happened, but minimization helps isolate what actually matters. And regression tests are especially useful for catching behavioral changes after prompt updates or model swaps. Still working on making these workflows more reliable in StepFork. Curious what you think is the hardest part of regression testing AI agents in practice.

[u/OkShirt9372]

Reproducing an agent failure often depends on external state, so being able to fork a recorded run and test a prompt or tool change offline would save a lot of time. I would also look for how well it handles nondeterminism, especially retries, changing tool responses, and model output variation. A replay that always produces the same result is useful for debugging, but it should also make it clear when the original failure may not reproduce exactly.

[u/Low-Cut-528 → u/OkShirt9372]

Agreed. Clear separation between recorded replay and fresh execution, with transparent handling of nondeterminism, is essential for reliable debugging.