Show HN: AgTrace – Observability for AI Coding Agents via MCP (Claude Code etc.) (opens in new tab)

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.