Autopsy CLI

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AI-powered incident diagnosis • zero-trust

AI-powered incident diagnosis for engineering teams. Pull production error logs and recent deploys, send them to an LLM, and get a structured root cause analysis in the terminal in under a minute. Zero-trust: your data never leaves your environment.

Demo

Watch a short walkthrough of the TUI and diagnosis flow:

https://github.com/user-attachments/assets/78ed3c52-1fe1-4ae2-844b-e0ed1848c06c

Prerequisites

Before running Autopsy, you need:

Quick checklist: python --version → 3.10+ · aws sts get-caller-identity → OK · GitHub PAT with repo (and/or GitLab PAT with read_api) · OpenAI or Anthropic API key · pip install autopsy-cli · autopsy init · autopsy diagnose.

You do not need: a server, Docker, a separate cloud account for Autopsy, a database, or admin/root; no changes to your AWS or app code.

Install

pip install autopsy-cli          # core CLI (no TUI)
pip install "autopsy-cli[tui]"   # + interactive terminal UI (requires textual)

Or from source:

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

Quick Start

autopsy           # Launch interactive TUI (menu, then run Diagnose or Setup)
autopsy init      # Or: interactive config wizard (~/.autopsy/config.yaml)
autopsy diagnose  # Collect (parallel) → AI prompt (labeled by source) → panels / --json (includes `sources`)

Interactive TUI — Run autopsy with no arguments to open the interactive terminal UI:

• AUTOPSY logo and tagline (AI-powered incident diagnosis • zero-trust)
• Arrow-key menu: Diagnose, History, Setup, Validate, Show config
• Shortcuts: d Diagnose, i Init, v Validate, c Config, q Quit, Esc Back
• Choosing Diagnose runs the full pipeline inside the TUI (progress steps, then 4-panel result). Errors are shown inline; press Esc to return to the menu.
• Choosing Setup / Validate / Show config exits the TUI and runs the corresponding CLI command in your terminal.

Three steps: install → init → diagnose (via TUI or direct commands).

Configuration

After autopsy init, edit ~/.autopsy/config.yaml or re-run the wizard. The init wizard stores credentials in ~/.autopsy/.env — no manual env var exports needed.

Credentials are loaded from ~/.autopsy/.env automatically. If you prefer env vars, export them in your shell — they take precedence over the .env file.

Security: Add ~/.autopsy/.env to .gitignore if you ever copy the config directory. Never commit credentials. If your home directory is backed up or synced (e.g. OneDrive, Time Machine, Google Drive), the .env file may be included — consider excluding ~/.autopsy/ from sync or use env vars instead.

autopsy config show       # Print config (secrets masked)
autopsy config validate   # Check env vars and connectivity

How It Works

┌─────────────┐     ┌──────────────────┐     ┌─────────────┐     ┌──────────────┐
│   Config    │────▶│  Data Collectors │────▶│  AI Engine  │────▶│  Renderers   │
│ ~/.autopsy  │     │  CloudWatch      │     │  (Claude /   │     │  Terminal or │
│ config.yaml │     │  Datadog (opt.)  │     │   OpenAI)    │     │  JSON        │
│             │     │  GitHub / GitLab │     │              │     │              │
└─────────────┘     └──────────────────┘     └─────────────┘     └──────────────┘
                           │                          │
                           ▼                          ▼
                    Logs + recent commits      Structured diagnosis:
                    (deduped, truncated)       root cause, deploy, fix, timeline

1. Collect — CloudWatch Logs Insights (error-level), optionally Datadog Logs, and GitHub/GitLab (last N commits + diffs).
2. Reduce — Log dedup and token budget; diff filters (code files only, cap per file).
3. Diagnose — Single prompt with logs + deploys; LLM returns JSON (root cause, correlated deploy, suggested fix, timeline).
4. Render — Rich panels in the terminal or --json for piping.

CLI Reference

If textual is not installed, autopsy with no arguments prints help instead of starting the TUI.

Contributing

See CONTRIBUTING.md for setup, code style, and how to submit changes. In short:

1. Fork the repo and create a branch.
2. Install dev deps: pip install -e ".[dev]".
3. Run lint and tests: ruff check . && pytest.
4. Open a PR against main.

We follow the layout and conventions in the repo (collectors, AI engine, renderers, no business logic in cli.py).

License

MIT. See LICENSE.