agentautopsy 1.5.0

When your agent fails, this tells you exactly why.

AgentAutopsy

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.

CLI

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

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