AI agents are getting more capable, but we're increasingly in the dark
about what they're actually doing. They run complex multi-step workflows,
call dozens of tools, reason through problems - and we just watch the
output scroll by. It's a black box, and humans end up being led around
by the agent rather than understanding it. I wanted to flip this. The key insight: all these agents (Claude Code, Codex, Gemini) already write detailed logs. The problem is they're in different locations, different formats, incompatible schemas. agtrace normalizes this "observation layer" across providers: - Auto-discovers logs from Claude, Codex, Gemini - Converts them into a unified event timeline - Exposes this via CLI, TUI dashboard, and MCP The MCP part is what makes it interesting for agents themselves. An agent can now query its own past sessions: - "What approach did we take when we refactored auth last week?" - "Show me errors from yesterday's session" - "How did we handle this edge case before?" This enables agent self-reflection - using execution history to inform current decisions. Built in Rust for safety and speed. 100% local, no cloud dependencies. The database is just a pointer index to original logs - rebuilable anytime. Happy to discuss the architecture or use cases. |