agentautopsy 2.3.0

Post-mortem debugger for AI agents

The post-mortem debugger for AI agents. When your agent fails silently — AgentAutopsy tells you exactly why.

Lifecycle

Install  →  Watch  →  Fail  →  Trace  →  Replay  →  Fix
   │          │        │         │          │        │
 pip      watch()   silent   SQLite     rewind    auto-fix
install              failure   log      + fork    + cache

Stage	What happens
Install	pip install agentautopsy — no cloud account, no config file
Watch	agentautopsy.watch() intercepts LLM, HTTP, MCP, and tool calls
Fail	Agent errors or returns bad output; run is marked in local SQLite
Trace	Full event timeline: prompts, tools, schemas, tokens, timestamps
Replay	Rewind to any step, fork with new input, diff original vs replay
Fix	Root-cause report, AI chat on trace, verified fix cached for next run

Commands

What you're doing	Command	Key principle
Zero config setup	agentautopsy.watch()	One import catches everything
View trace UI	agentautopsy ui	Visual timeline of every step
Replay a failure	agentautopsy replay --run-id <id> --from-step <n>	Rewind to exact failure point
Detect schema drift	SchemaDriftDetector().watch()	Catch API changes before production
Trace MCP servers	MCPAutopsy().watch_mcp_server()	Every MCP call recorded
Fork and fix	DVRReplay().fork(run_id, at_step=3)	Branch from any step
Generate tests from failures	agentautopsy generate-evals	Never fix the same bug twice

Quick Start

pip install agentautopsy

import agentautopsy

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

Features

Feature	What It Does	Use When
MCP Post-Mortem Tracing	Traces every MCP server call, shows bad data source	Using Claude Code, Cursor, any MCP client
Schema Drift Detector	Detects renamed/removed/added fields automatically	After any API or tool upgrade
DVR Fork and Replay	Rewind, replay, fork from any step	Debugging production failures
Swarm Tracing	Causality tracing across 50+ agents	Multi-agent workflows
AI Chat on Trace	Ask questions about your failure	When root cause is unclear
Prompt Diffing	Compare runs side by side	Optimizing agent behavior
Contract Checkpointing	Enforces Pydantic schemas and checkpoints valid states	Preventing late-stage failures caused by early bad data
Automatic Eval Generation	Generates pytest test cases from every failure automatically	When you want to make sure the same failure never happens again

Framework Support

Works with: LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, OpenAI, Anthropic, MCP

Module Quick Start

Basic

import agentautopsy

agentautopsy.watch()

MCP

from agentautopsy import MCPAutopsy

MCPAutopsy().watch_mcp_server("my-mcp-server")

Schema Drift

from agentautopsy import SchemaDriftDetector

SchemaDriftDetector().watch()

DVR Replay

from agentautopsy import DVRReplay

dvr = DVRReplay()
dvr.replay(run_id, from_step=3)
dvr.fork(run_id, at_step=3, new_input={"query": "fixed prompt"})

Auto Eval Generation

from agentautopsy import EvalGenerator

gen = EvalGenerator()
gen.generate_all()  # generates tests from all recorded failures

Contract Checkpointing

from agentautopsy import ContractCheckpointer
from pydantic import BaseModel

class OutputSchema(BaseModel):
    __rules_version__ = "1.0"  # Optional: bump to force cache invalidation on external dependency drift
    summary: str

checkpointer = ContractCheckpointer(run_id)
# Fails immediately if invalid, otherwise saves state to SQLite for resuming.
# Automatically normalizes and hashes the AST (Abstract Syntax Tree) of the validator, global constants, and helper functions to detect logic drift without false positives!
validated = checkpointer.enforce("step_1", raw_output, OutputSchema)

Contributing

Fork the repo, open a PR with tests. See CONTRIBUTING.md for setup and CI commands.

License

MIT — see LICENSE.