agentdebugx 0.2.0

Portable error analysis, tracing, and recovery framework for agentic AI systems. Import as `agentdebug`.

AgentDebugX

AgentDebugX is an open-source framework for tracing, diagnosing, and recovering from failures in agentic AI systems. It is packaged as agentdebug so existing agent stacks can add structured debugging with a small import:

from agentdebug import AgentDebug, EventType, SQLiteTraceStore

debugger = AgentDebug(store=SQLiteTraceStore(".agentdebug/errors.sqlite"))

with debugger.trace(goal="Book a refundable flight", framework="my-agent") as trace:
    trace.record(EventType.PLAN, agent_name="planner", output="Search flights first")
    trace.record(
        EventType.TOOL_RESULT,
        agent_name="browser",
        error="Timeout while loading checkout page",
        step_index=3,
    )
    report = trace.analyze()

print(report.summary)

Vision

Most observability tools show what an agent did. AgentDebugX aims to explain why it failed, who or which module caused the issue, when the first critical error occurred, and what should be changed next.

The long-term scope is:

• A portable trajectory IR for single-agent, multi-agent, tool-use, UI, and multimodal runs.
• An agentic error database for storing traces, diagnostic reports, taxonomy labels, artifacts, and recovery outcomes.
• Automated failure localization across step, module, agent, and run levels.
• Automated taxonomy generation that clusters new failures and proposes new labels when existing taxonomies are insufficient.
• Recovery suggestions that can be used by humans, CI workflows, or self-healing agents.
• UI and notebook experiences for timeline debugging, attribution, replay, and dataset curation.

Current Status

This repository now contains the first framework skeleton:

• agentdebug.models: normalized event, artifact, trajectory, taxonomy, and diagnostic report schemas.
• agentdebug.recorder: high-level AgentDebug and TraceSession APIs.
• agentdebug.analyzers: deterministic baseline analyzer for immediate local feedback.
• agentdebug.taxonomy: seed taxonomy inspired by AgentDebug, MAST, Who&When, AgentRx, AgentSight, and multimodal extensions.
• agentdebug.storage: JSONL and SQLite trace stores.
• agentdebug.instrumentation: lightweight function/tool tracing helper.
• agentdebug.cli: initial agentdebug analyze command.

Many advanced modules are intentionally not implemented yet. The design docs spell out the proposed path before we lock into heavy abstractions.

Install

# From PyPI (distribution name `agentdebugx`; the short import is `agentdebug`)
pip install agentdebugx

# With the optional local dashboard
pip install 'agentdebugx[ui]'

# With LangChain/LangGraph callback adapter
pip install 'agentdebugx[langgraph]'

# With OpenTelemetry GenAI export shim
pip install 'agentdebugx[otel]'

# With Hugging Face Datasets backend for the Error Hub
pip install 'agentdebugx[hub-hf]'

# With OpenHands EventStream bridge
pip install 'agentdebugx[openhands]'

# Everything
pip install 'agentdebugx[all]'

From source:

pip install -e .         # or: poetry install

The PyPI distribution name is agentdebugx because PyPI's name policy (PEP 503 normalization) collides agentdebug with the existing agent-debug package. The Python import is always import agentdebug.

Quick Start

from agentdebug import AgentDebug, EventType
from agentdebug.models import model_to_json

debugger = AgentDebug()
trajectory = debugger.start_trace(
    goal="Find a paper and summarize the method",
    framework="custom-react-agent",
)

debugger.record_event(
    trajectory,
    EventType.LLM_CALL,
    agent_name="researcher",
    module="planning",
    step_index=1,
    input="Find the latest AgentDebug paper",
)
debugger.record_event(
    trajectory,
    EventType.TOOL_RESULT,
    agent_name="search",
    module="action",
    step_index=2,
    error="JSON schema validation failed: missing parameter query",
)
debugger.finish_trace(trajectory, success=False)

report = debugger.analyze(trajectory)
print(model_to_json(report, indent=2))

CLI:

# Run the rule-based analyzer (works offline, no LLM)
agentdebug analyze examples/sample_trace.json --out .agentdebug/report.json

# List traces in a store
agentdebug list --store-sqlite .agentdebug/errors.sqlite

# Run the LLM judge (requires AGENTDEBUG_LLM_BASE_URL + AGENTDEBUG_LLM_API_KEY)
agentdebug judge examples/sample_trace.json --attribute

# Launch the local dashboard at http://127.0.0.1:7777
agentdebug serve --store-sqlite .agentdebug/errors.sqlite

# DeepDebug — iterative multi-turn analysis (plan -> hypothesize -> verify -> refine)
agentdebug deep <trajectory.json>

# Error Hub: package + push a trace to a Git remote or HF dataset
agentdebug hub push <trace_id> \
    --to git:[e-mail]:your-org/agentdebug-bundles.git#bundles \
    --store-sqlite .agentdebug/errors.sqlite

# Pull a bundle someone else shared
agentdebug hub pull git:... --bundle bundle_01ABCD --into .agentdebug/hub_pulls

# Host integrations: generate a Claude Code Skill, or an OpenHands microagent
agentdebug integrations skill --target ~/.claude/skills
agentdebug integrations openhands-microagent --target .openhands/microagents

# Diagnose which adapters / integrations are available
agentdebug doctor

End-to-end demo (judge + attribution + Reflexion recovery, all live against an LLM): examples/llm_judge_demo.py.

Benchmark

scripts/eval_v0_1.py runs the rule analyzer + LLM judge + All-at-Once attributor across six realistic failing traces (action/format, planning/loop, verification, system/tool error, multi-agent handoff loss, memory retrieval). Latest results: docs/benchmarks/v0_1_smoke.md.

Documentation

Narrative (paper-style):

• Research Survey
• Open-Source Development Plan
• Seed Error Taxonomy

Engineering spec (numbered docs 00–18):

• docs/README.md — full doc index
• docs/00_overview.md — vision + north-star UX
• docs/02_architecture.md — 7-layer architecture
• docs/18_comparison_codex_vs_design.md — Codex scaffold ↔ design-spec reconciliation that drove v0.1

Development

pytest
mypy --strict ./
ruff check .

License

MIT License — see the LICENSE file. Original template copyright Haofei Yu, 2024; additions copyright ULab @ UIUC and contributors, 2026.