AgentDebugX：用於大型語言模型智能體失敗可觀測性、歸因與恢復的開源工具套件

摘要

LLM代理的故障難以除錯，因為錯誤顯現的步驟通常並非導致錯誤的根源。現有的可觀測性工具會重播執行軌跡，但在識別根本原因或將診斷轉化為復原方面提供的支援有限。我們提出AgentDebugX，一個開源除錯框架，將除錯組織為偵測、歸因、復原與重新執行的閉環。其核心DeepDebug透過全局軌跡理解、結構導向調查與交叉檢驗，執行多輪根本原因診斷。在Who and When基準測試中，DeepDebug在兩個受測的開放權重骨幹模型上，均達到評估方法中最高的嚴格歸因準確率，在qwen3.5-9b上達到28.8%的精確代理與步驟準確率，而最強的單次通過基準為21.7%。在GAIA上，DeepDebug在單次重新執行中修復了73個失敗任務中的13個，而三個去耦自我修正基準僅修復4到6個，將整體準確率從55.8%提升至63.6%。AgentDebugX透過Python函式庫、命令列介面、網頁控制台與可安裝的代理技能公開此工作流程，並提供選擇加入的錯誤中心，用於分享已清洗的故障-診斷-修復套件，並將其重複使用作為除錯記憶。

English

LLM agent failures are difficult to debug because the step where an error surfaces is often not the one that caused it. Existing observability tools replay execution traces but provide little support for identifying the root cause or translating diagnosis into recovery. We present AgentDebugX, an open-source debugging framework that organizes debugging as a closed loop of Detect, Attribute, Recover, and Rerun. At its core, DeepDebug performs multi-turn root-cause diagnosis through global trajectory understanding, structure-guided investigation, and cross-examination. On the Who and When benchmark, DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8 percent exact agent-and-step accuracy on qwen3.5-9b versus 21.7 percent for the strongest single-pass baseline. On GAIA, DeepDebug repairs 13 of 73 failed tasks in a single rerun, compared with 4 to 6 for three decoupled self-correction baselines, improving overall accuracy from 55.8 percent to 63.6 percent. AgentDebugX exposes this workflow through a Python library, CLI, web console, and installable agentic skill, and provides an opt-in Error Hub for sharing scrubbed failure-diagnosis-repair bundles and reusing them as debugging memory.