LLM-powered local Git commit review CLI tool for Android engineers.
commit-intel runs a local AI agent pipeline using Ollama and LangGraph to review staged or committed code diffs, providing feedback on:
- 🔍 Summary
- 🧪 Code critique
- 💡 Suggestions
- 🔐 Security issues
- 📐 Architecture
- 🧪 Test coverage
- 🖼️ UI changes (Jetpack Compose or XML)
- 📦 Dependency usage
- ⚡ Performance
- 🔤 Readability
- Uses Ollama and local models like
codellama:7b(no cloud API calls required)
- Run via simple terminal commands:
review commit review commit --diff HEAD~2 review staged
- Optional hook setup:
review install-prehook
- Diff is passed through a LangGraph of LLM-powered agents in sequence
- Skips agents that are not applicable (e.g., no UI or test files)
-
Clone the repo:
cd commit-intel -
Set up a virtual environment:
python -m venv .venv source .venv/bin/activate pip install -e .
-
Pull the model using Ollama:
ollama pull codellama:7b
-
Run the CLI:
review --help
review commit
🧠 Commit Summary:
- Updated login flow to use new auth provider
- Removed legacy authentication code
🧪 Critique:
- Missing error handling for network failure
- Redundant logging in auth callback
💡 Suggestions:
- Extract token parsing into a separate functioncommit-intel/
├── cli/ # Typer CLI entrypoints
├── core/ # LangGraph, Ollama, agents
├── .pre-commit/ # Hook logic
├── tests/ # Unit tests (TBD)
└── README.md
- ✅ Export feedback to Markdown (
--markdown) - ✅ CLI flags to enable/disable agents
- ⚙️ Gradle plugin integration
- 📈 Coverage integration from JaCoCo reports
- 🔁 Streaming token-based output
- 🔧 Model config via
.commit-intel.yml - 🧠 Model fallback: use OpenAI if Ollama is down
- 🛠️ Workspace scoring: code quality % per commit
- 🧪 Automated fix suggestions (optional auto PRs)
Built by Android engineers, for Android engineers.
Inspired by LangChain, LangGraph, Ollama, and open-source LLM tooling.
MIT License.