Ouroboros traces program execution for LLM debugging, boosting small-model accuracy by up to 34 points
The_Homeless_God · reddit · 2026-09-11
A new open-source tool, Ouroboros, gives LLMs execution traces before code hits your codebase: it records every function call, its arguments, and results across 8 languages, tackling hallucinated code.
How it works: the tool parses where functions begin and end, splices in recording code, runs in an isolated copy so your worktree stays clean, and returns only the traced debug output to the LLM. Installable via uv/brew, ships with an MCP server, and agents like Claude Code or Codex can set up a skill to use it continuously.
Benchmark results (600 questions each) are striking:
- qwen3.5:4b: 44.0% → 78.3% (+34.3)
- qwen2.5:14b: 61.0% → 84.7% (+23.7)
- qwen3:32b: 66.7% → 90.3% (+23.6)
- A Claude Opus 5 subagent: 95.0% → 98.3% (+3.3)
Takeaway: execution traces help weaker models most; strong models gain less but still improve. Tool, docs, and dataset are all open-sourced.
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