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.