AgentDebugX is an open-source closed-loop debugger for LLM agents

UIUC-CS · hf · 2026-07-22

AgentDebugX is an open-source toolkit for debugging LLM agents as a closed loop of Detect, Attribute, Recover, Rerun.
Its key component, DeepDebug, performs multi-turn root-cause diagnosis using global trajectory understanding, structure-guided investigation, and cross-examination.
On the Who and When benchmark, it reaches 28.8% exact agent-and-step accuracy on qwen3.5-9b, beating the strongest single-pass baseline at 21.7%.
On GAIA, it repairs 13 of 73 failed tasks in one rerun, versus 4–6 for decoupled self-correction baselines, lifting overall accuracy from 55.8% to 63.6%.
The project ships as a Python library, CLI, web console, and installable agentic skill, plus an opt-in Error Hub for sharing scrubbed failure bundles as debugging memory.

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