agentautopsy 1.7.0

Post-mortem debugger for AI agents

AgentAutopsy

When your agent fails, this tells you exactly why.

Install

pip install agentautopsy

Features

• Interceptor — Captures OpenAI, Anthropic, and HTTP traffic automatically with zero code changes (agentautopsy.watch())
• Web UI — Browse runs, inspect events, and debug failures in a local dashboard (agentautopsy ui)
• Auto-fix — Generates and applies patch suggestions for failed runs (agentautopsy fix <run_id>)
• GitHub PR — Opens a pull request with the proposed fix (agentautopsy fix <run_id> --create-pr)
• Slack alerts — Posts failure notifications to your channel when a run fails
• Prompt diffing — Compares prompts in the current run against the previous run
• Replay — Step through failed runs event-by-event in the UI and CLI (agentautopsy replay <run_id>)
• Multi-agent graph — Visualizes agent handoffs and parent/child run chains (agentautopsy agents)

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.

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.

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

pip install agentautopsy

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