AgentDebugX/AgentDebugX
AgentDebugX
A debugging framework for agentic AI systems: diagnose failures, attribute root causes, recover with evidence, and validate fixes through reruns.
Install
npx @deepseek-ai/dsh plugin --profile web add dsh-agentdebugxRestart `dsh web` after install. Bundle APIs can change during the developer preview.
README badge
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From the README
Excerpt from AgentDebugX/AgentDebugX, cleaned of badges and images.
AgentDebugX
A local-first debugging framework for agentic AI systems: diagnose failures, attribute root causes, recover with evidence, and validate fixes through reruns.
AgentDebugX turns failed agent runs into structured, auditable debugging artifacts. It ingests a live or exported trajectory, detects visible failure signals, attributes them to responsible steps or agents, proposes recovery actions, and prepares controlled reruns so fixes can be validated instead of guessed.
The project is designed for researchers and engineers building complex LLM agents: multi-agent systems, tool-using agents, computer-use agents, benchmark runners, and local agent development workflows. AgentDebugX is local-first by default: traces stay on your machine, sharing is opt-in, and recovery proposals carry explicit policy and approval metadata into the Rerun boundary.
📰 News
- 🔌 2026-08-25 — Released
dsh-agentdebugxv0.1.0, the AgentDebugX plugin for DeepSeek Harness. - 📄 2026-07-31 — Released CUADebug, our framework for diagnosing and repairing computer-use agent failures.
- 📄 2026-07-21 — Released the AgentDebugX paper, presenting our open-source toolkit for failure observability, attribution, recovery, and rerun in LLM agents.
- 📦 2026-05-16 — Released AgentDebugX on PyPI.
- 📄 2025-09-29 — Released Where LLM Agents Fail and How They Can Learn From Failures, introducing AgentErrorTaxonomy, AgentErrorBench, and AgentDebug.
System Overview
AgentDebugX follows the two-stage loop used by the project paper:
Diagnose = Detect -> Attribute -> Recover
Rerun = checkpoint -> retry directive -> branch execution -> evaluation
Diagnose explains what failed and why. Rerun tests whether the proposed
recovery actually improves the agent behavior.
Why AgentDebugX
Tracing tools show what happened. AgentDebugX focuses on the debugging step that usually comes next:
- Which earlier decision caused the visible failure?
- Which agent, tool call, memory read, handoff, or GUI action was responsible?
- What evidence supports that diagnosis?
- What concrete recovery should be tried?
- Did the rerun branch improve the outcome?
The output is a portable diagnostic report that can be inspected in a local UI, used by a CLI workflow, stored in an Error Hub bundle, or invoked from an agentic skill.
Core Capabilities
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