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 When benchmark, DeepDebug achieves the best strict attribution accuracy among the evaluated methods on both tested open-weight backbones, reaching 28.8% exact agent-and-step accuracy on qwen3.5-9b, compared with 21.7% for the strongest single-pass baseline. On GAIA, DeepDebug repairs 13 of 73 failed tasks in a single rerun, compared with 4–6 for three decoupled self-correction baselines, improving overall accuracy from 55.8% to 63.6%. AgentDebugX exposes this workflow through a Python library, CLI, web console, and installable agentic skill. It also provides an opt-in Error Hub for sharing scrubbed failure–diagnosis–repair bundles and reusing them as debugging memory.