Hi everyone,
I just released TraceMotive v0.1, an open-source, local-first tracing and debugging tool for AI agent execution.
I built it because I kept running into the same problem: when an agent behaves incorrectly, the final error often doesn’t explain how the execution actually got there.
TraceMotive records agent runs as traces and spans so you can inspect the execution step by step.
v0.1 currently includes:
- Trace and Span collection
- Parent/child Span hierarchy
- Execution Timeline
- Span Inspector
- SQLite-backed local Collector
- OpenAI Agents SDK integration
- Privacy-first content capture defaults
- Bounded local transport and failure isolation
Install:
pip install tracemotive
GitHub:
PyPI:
TraceMotive’s own tracing data stays local by default. Model-provider traffic still depends on how the agent itself is configured.
Right now, v0.1 is intentionally focused on observation and debugging rather than automatic diagnosis.
The longer-term direction is to move from:
“Where did the error happen?”
toward:
“Where did the execution first start going wrong?”
That is the idea behind the long-term goal:
“The causal debugger for AI agents.”
TraceMotive currently has an OpenAI Agents SDK integration, not LangChain/LangGraph integration yet.
I’m sharing it here because I’d really like feedback from people building agentic systems:
What execution data or debugging capabilities do you usually wish you had when an agent behaves incorrectly?
