Built a local-first debugger for AI agent execution (TraceMotive) — with no formal software engineering background

Hi everyone,
I don’t have a formal software engineering background. I’ve been learning by building tools with AI-assisted development — designing, prompting, iterating, testing, and reviewing rather than writing every line by hand.
My latest project is TraceMotive, a local-first tracing and debugging tool for AI agent execution.
I built it because I wanted a clearer way to see how an agent execution unfolded step by step, instead of only seeing the final error.
Highlights:
Local-first Trace and Span collection with SQLite
Trace List, Span hierarchy, Timeline, and Inspector
OpenAI Agents SDK integration
Privacy-first content capture defaults
Bounded local transport and failure isolation
TraceMotive’s own tracing data stays local by default. Model-provider traffic still depends on how your agent itself is configured.
Install:
pip install tracemotive
I’ll put the GitHub repo in the comments.
This is v0.1, so it’s intentionally focused on observation and debugging rather than automatic diagnosis.
The longer-term goal is to move from:
“Where did the error happen?”
toward:
“Where did the execution first start going wrong?”
I’d really appreciate feedback from people building agents:
What information do you usually wish you had when an agent behaves incorrectly?