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Post-mortem debugger for AI agents

Project description

AgentAutopsy

PyPI version PyPI downloads GitHub stars License: MIT Python 3.8+

When your agent fails, this tells you exactly why.

Why this exists

Every time an AI agent fails, you get a useless stack trace. No context. No reason. No fix. AgentAutopsy gives you the exact failure step, root cause, and a verified fix — automatically.

demo

Python License Zero Config Works with

CLI

agentautopsy runs # see all agent runs agentautopsy replay # replay any failure agentautopsy stats # fix cache stats

GitHub Actions

Add AgentAutopsy to your test workflow so failed Python tests get an automatic root-cause analysis and a suggested fix posted on the pull request.

Create or update .github/workflows/test.yml:

name: Tests

on:
  pull_request:
  push:
    branches: [main]

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - name: Install dependencies
        run: pip install -r requirements.txt

      - name: AgentAutopsy
        uses: Abhisekhpatel/AgentAutopsy@v1
        with:
          anthropic_api_key: ${{ secrets.ANTHROPIC_API_KEY }}
          github_token: ${{ secrets.GITHUB_TOKEN }}
          test_command: pytest

Inputs

Input Required Default Description
anthropic_api_key yes Anthropic API key for analysis
github_token yes Token with pull-requests: write (use secrets.GITHUB_TOKEN)
test_command no pytest Shell command run before analysis

On test failure the action runs agentautopsy analyze (via the bundled entrypoint), then posts root cause and fix as a PR comment. Store ANTHROPIC_API_KEY in repository secrets.

Install

pip install agentautopsy

Examples

# Basic usage
import agentautopsy
agentautopsy.watch()

# LangChain
from agentautopsy import get_callback_handler
handler = get_callback_handler()
agent.run(input, config={"callbacks": [handler]})

# LangGraph
from agentautopsy import get_langgraph_handler
handler = get_langgraph_handler()
graph.invoke(input, config={"callbacks": [handler]})

# CrewAI
from agentautopsy import get_crewai_handler
handler = get_crewai_handler()
crew = Crew(agents=[...], callbacks=[handler])
# Slack alerts
export AGENTAUTOPSY_SLACK_WEBHOOK=https://hooks.slack.com/...

# Web UI
agentautopsy ui

# CLI
agentautopsy runs
agentautopsy replay <run_id>
agentautopsy stats

LangGraph

import agentautopsy
from agentautopsy import get_langgraph_handler

agentautopsy.watch()
handler = get_langgraph_handler()

# Pass the handler into LangGraph invoke config
result = graph.invoke(
    {"messages": [("user", "research competitors")]},
    config={"callbacks": [handler]},
)

The handler records node start/end, edge traversals, state updates between nodes, tool and LLM activity, and any graph errors in agentautopsy.db.

CrewAI

import agentautopsy
from agentautopsy import get_crewai_handler
from crewai import Crew

agentautopsy.watch()
handler = get_crewai_handler()

crew = Crew(agents=[researcher, writer], tasks=[...], callbacks=[handler])
crew.kickoff()

# Or use step_callback on Crew / Agent (supported by current CrewAI releases)
crew = Crew(agents=[...], step_callback=handler.step_callback)

The handler records task start/end, tool usage, agent handoffs, final crew output, and errors.

Usage

import agentautopsy
agentautopsy.watch()
# your existing agent code here — nothing else changes

AgentAutopsy automatically intercepts every LLM call, detects failures, finds root cause, outputs a verified fix, and caches it for next time.

Why AgentAutopsy vs LangSmith / Helicone?

Feature AgentAutopsy LangSmith Helicone
Works offline
Zero config
Replay failed runs partial
AI debug assistant
Prompt diffing partial
Divergence detection
Free and open source partial
No cloud required

Setup

Windows: set ANTHROPIC_API_KEY=your-key-here Mac/Linux: export ANTHROPIC_API_KEY=your-key-here Get your free key at console.anthropic.com

Set AGENTAUTOPSY_SLACK_WEBHOOK=your-webhook-url and AgentAutopsy will automatically alert your Slack channel when any agent fails.

Quick start

Create test_agent.py and paste this:

import agentautopsy
agentautopsy.watch()

Run: python test_agent.py

Works with

OpenAI, Anthropic, LangChain, LangGraph, CrewAI, any framework using openai or anthropic

Requirements

Python 3.11+, ANTHROPIC_API_KEY

License

Apache 2.0

Roadmap

  • VS Code extension
  • GitHub Actions integration
  • Multi-agent tracing
  • Auto-fix applier
  • LangChain support
  • LangGraph support
  • CrewAI support
  • Slack alerts
  • Web UI
  • Prompt diffing
  • Divergence detection

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