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MagicReview

Review code like magic.

MagicReview is a local-first, self-hostable AI code review platform for CLI, Docker, Dashboard, MCP tools, and GitHub App workflows.

MagicReview defaults are intentionally conservative and offline by default:

  • Runs local static analysis by default.
  • Does not call real LLM providers by default.
  • Does not upload source code unless network access is explicitly enabled.
  • Uses MGREVIEW_ environment variables and .magicreview/ data by default.

Quick Start

pip install magicreview
mgreview --help
mgreview project examples/multi_agent_project --agents --format terminal

From source:

git clone https://github.com/KrisMagical/review-agent.git
cd review-agent
pip install -e ".[all,dev]"
mgreview --help

CLI

mgreview --help
mgreview --version
mgreview file app/main.py
mgreview diff
mgreview project .
mgreview project examples/multi_agent_project --agents --format json
mgreview dashboard init-db
mgreview dashboard serve
mgreview mcp
mgreview github-app serve

Optional standalone entry points are also packaged for service runners:

mgreview-mcp
mgreview-dashboard
mgreview-github-app

Docker

docker build -t magicreview .
docker run --rm magicreview mgreview --help
docker run --rm -v "$PWD:/workspace" magicreview mgreview project /workspace --format json
docker compose up dashboard
docker compose up github-app

Docker Compose persists SQLite data in ./.magicreview.

Configuration

MagicReview uses MGREVIEW_ as its environment variable prefix:

MGREVIEW_DB_PATH=.magicreview/magicreview.db
MGREVIEW_LLM_PROVIDER=none
MGREVIEW_NETWORK_ENABLED=false
MGREVIEW_ALLOW_LLM=false
MGREVIEW_CODE_SHARING_MODE=none
MGREVIEW_AUTH_ENABLED=false

Default data lookup order:

  1. MGREVIEW_DB_PATH
  2. .magicreview/magicreview.db

GitHub App

MagicReview supports diff_only and full_project review modes. The generated pull request summary ends with:

Generated by MagicReview.

License

MagicReview is released under the MIT License. See LICENSE.

About

An agent-native software review platform for local static review, enterprise policy checks, LLM architecture review, MCP integration, and multi-agent project analysis.

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