# I built TraceMotive: a local-first debugger for AI agent execution

> Source: <https://dev.to/ruca_ai/i-built-tracemotive-a-local-first-debugger-for-ai-agent-execution-2bh1>
> Published: 2026-08-13 18:32:50+00:00

[I’ve been building an open-source project called TraceMotive.](https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Ff7mou6nx6vucjfo70t34.gif)

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.

The current v0.1 includes:

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.

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:

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.

PyPI:

pip install tracemotive

GitHub:

[https://github.com/doraemonfv-glitch/tracemotive](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.