I’ve been building an open-source project called TraceMotive.
It started from a problem I kept running into with AI agents: When an agent run fails, the place where the error appears isn’t always where the execution first started going wrong.
That makes debugging agent workflows harder than it looks.
So I built TraceMotive, a local-first tracing and debugging tool for AI agent execution.
What TraceMotive does The current v0.1 includes: Python SDK canonical traces and spans a local Collector backed by SQLite a React UI for inspecting agent runs optional OpenAI Agents SDK integration TraceMotive is local-first, and content capture is disabled by default.
I’m intentionally keeping the first version small.
I’m not trying to add replay, automatic root-cause analysis, cloud sync, or support for every agent framework yet.
Why?
I’d rather get real feedback before adding a lot of features.
Right now I want people who actually build AI agents to try it and tell me: where setup is confusing what breaks what information is missing from traces what feels awkward in the API The longer-term direction is: “The causal debugger for AI agents.” Eventually, I want TraceMotive to help identify where an agent execution first started going in the wrong direction, instead of only showing where the final error appeared.
But first, I want to make the basic observation and debugging layer solid.
Try it PyPI: pip install tracemotive GitHub: https://github.com/doraemonfv-glitch/tracemotive If you build AI agents, I’d really appreciate you trying it for a few minutes and telling me what you run into.
Even small feedback is useful.