AUTOPSY: The Open-Source CLI for 30-Second Production Incident Diagnosis

I Built an Open-Source CLI That Diagnoses Production Incidents in 30 Seconds — Looking for Contributors

Engineer Zeel Patel developed AUTOPSY, an open-source Python CLI that automates the root cause analysis of production failures. The tool reduces investigation time from 45 minutes to 30 seconds by orchestrating CloudWatch logs and GitHub diffs through LLMs.

Why This Matters

In technical reality, the diagnosis gap between alert detection and understanding remains a manual process of grepping logs and checking commits, even with expensive platforms like Datadog or Grafana. While enterprise tools focus on coordination or detection, individual engineers often lack free, local-first tools that can ingest data directly into AI models without sending sensitive logs through third-party infrastructure. This tool addresses the one phase of the incident lifecycle that remains unautomated at most companies: the transition from alert to understanding.

Key Insights

• Log reduction pipeline: AUTOPSY uses regex filtering, SHA256 deduplication, and a 6,000-token budget to fit CloudWatch data into LLM context windows (2026).
• Modular architecture: Every data source implements a BaseCollector interface, allowing easy integration with Datadog, ELK, or GCP Cloud Logging via single new classes.
• Local-first security: Logs move from AWS directly to AI providers using local credentials, ensuring no data touches the tool’s infrastructure.
• Tool validation: AI responses are validated against a Pydantic v2 schema with automatic retries on malformed output to ensure structured terminal rendering.
• Technology Stack: The project utilizes Python 3.10–3.13, Click, Boto3, PyGitHub, and Rich for terminal-based data visualization.

Working Examples

Basic installation and execution command for AUTOPSY.

pip install autopsy-cli
autopsy diagnose

Development environment setup and testing commands for contributors.

git clone https://github.com/zaappy/autopsy.git
cd autopsy
pip install -e ".[dev]"
pytest
ruff check .

Practical Applications

• Incident Response: On-call engineers use ‘autopsy diagnose’ to instantly detect configuration errors, such as a Redis connection pool reduction. Pitfall: Relying on manual grep and intuition leads to prolonged downtime during 3 AM outages.
• Security-Sensitive Diagnosis: Teams use the tool’s local-only architecture to send logs directly to Claude or GPT-4o using their own API keys. Pitfall: Using centralized SaaS platforms often involves complex data privacy agreements and log ingestion fees.
• Automated Post-Mortems: The tool can be extended to generate post-mortem documents based on the AI-analyzed root cause. Pitfall: Manual documentation after an incident is often delayed or lacks technical precision.

References:

• https://dev.to/zeel_patel/i-built-an-open-source-cli-that-diagnoses-production-incidents-in-30-seconds-looking-for-4p63
• https://github.com/zaappy/autopsy
• https://github.com/zaappy/autopsy/issues