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ai-engineering — AI governance framework

Open-source AI governance framework

License: MIT PyPI Python 3.11+ CI Quality Gate Coverage Snyk

AI governance that ships. Turn any repository into a governed AI workspace: policies, skills, agents, runbooks, and specs as versioned files. No hosted control plane, no vendor lock-in. One canonical chain across Claude Code, GitHub Copilot, OpenAI Codex, Gemini CLI, and Antigravity.

Install

Prerequisites: Python 3.11+ and Git.

# pipx (recommended)
pipx install ai-engineering

# uv
uv tool install ai-engineering

# pip (inside a venv)
python -m venv .venv && source .venv/bin/activate
pip install ai-engineering

Verify:

ai-eng version

Update later with pipx upgrade ai-engineering (or uv tool upgrade / pip install --upgrade), then ai-eng update in each project followed by ai-eng doctor to verify.

Quick Start

cd your-project
ai-eng install .
ai-eng doctor

install scaffolds the governance root, detects your stack, mirrors skills to every configured IDE, and wires the secrets-gate. doctor validates the result.

Run ai-eng install --help for the full options matrix; the doctor output names every step that has to pass.

Telemetry is strict-opt-in (default disabled). The audit chain is local NDJSON; external emitters require explicit operator opt-in via .ai-engineering/manifest.yml > telemetry.*.

Optional: Engram (third-party memory)

ai-engineering ships without a built-in memory layer. Engram is a peer product maintained by Gentleman-Programming/engram; install it separately if you want cross-session memory (spec-132 D-132-06; the installer no longer wires Engram automatically). See the Optional: Engram section of CLAUDE.md for OS-specific install commands and the IDE setup matrix.

How AI Works Here

This framework defines a single canonical chain that every supported IDE follows identically. The full ruleset, principles, surface index, and chain definition live in:

The skill catalogue, agent roster, runbook list, quality-gate thresholds, and CLI command reference all live in AGENTS.md (canonical) and .ai-engineering/reference/cli-reference.md. They are not duplicated here.

Standing on the shoulders of...

ai-engineering builds on ideas, patterns, and principles from these projects:

Project What we learned
Superpowers Brainstorm hard-gate, TDD-for-skills patterns
review-code Handler-as-workflow architecture, parallel specialist agents, finding-validator
dotfiles/ai Agent matrix, SDLC coverage patterns
autoresearch Radical simplicity as a design principle
Emil Kowalski Motion principles, spring physics, easing strategy
SpecKit Spec-driven workflow inspiration
GSD Autonomous execution patterns
Anthropic Skills Frontend-design, canvas, skill-creator — absorbed and extended

Contributing

Contributions are welcome. See CONTRIBUTING.md for development setup, code style, testing, and the pull request process.

Code of conduct

This project follows the Contributor Covenant Code of Conduct. See CODE_OF_CONDUCT.md.

License

MIT. See LICENSE.

About

Turn any repo into a governed AI workspace. Quality gates, security scanning, and risk management — enforced locally via git hooks. Works with Claude Code, GitHub Copilot, Cursor, Gemini & Codex.

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