GitAgent offers a powerful, GitHub-native framework for integrating AI into software development workflows. It enables any AI agent to effectively manage projects while providing full transparency and human oversight. By leveraging GitHub's features, GitAgent supports flexible methodologies and ensures that important development steps are not overlooked.
GitAgent is a GitHub-native, model-agnostic framework designed to facilitate AI-assisted software development. It allows various AI agents such as Claude Code, Gemini, and GitHub Copilot to seamlessly manage project development by leveraging GitHub's native features as the primary source of truth.
GitAgent addresses several challenges in AI-assisted development:
| Problem | GitAgent Solution |
|---|---|
| AI agents lack project context | An AGENT.md file provides a complete protocol for understanding project specifications. |
| No visibility into AI actions | Transparency is achieved through GitHub Issues and Projects, offering full visibility into AI activities. |
| AI can skip important steps | A gate system enforces human approval at crucial checkpoints. |
| Different AI tools, different workflows | The model-agnostic design ensures compatibility across various AI tools and workflows. |
| Difficulty tracking AI-generated work | Automated state management through labels and actions enables easier tracking of progress. |
/approve and /verify-pass.To begin using GitAgent, follow these steps:
Create a new repository using the GitAgent template:
gh repo create my-project --template MauricioPerera/gitagent --public --clone
cd my-project
Run the setup workflow to initialize project labels and issues:
gh workflow run setup.yml
Configure the project by editing files in the .gitagent/context/ directory. Here is a snippet for defining the product vision:
## Product Vision
Build a task management app for remote teams...
Engage with the AI by passing commands like:
claude
> Read AGENT.md and help me build a user authentication feature
GitAgent enables an organized development cycle facilitated by AI and human collaboration. Below are the phases of interaction:
AGENT.md protocol and context files, generating a specification.Human engagement is crucial in this process and is facilitated through simple commands:
| Command | Effect |
|---|---|
/approve | Approves the specification or plan, triggering automation of the gate process. |
/verify-pass | Verifies a phase checkpoint, allowing the project to continue. |
/verify-fail {reason} | Marks a checkpoint as failed, preventing further progress until resolved. |
GitAgent's structure is organized for easy navigation and understanding, featuring templates and configuration files necessary for effective collaboration:
your-project/
├── AGENT.md # AI agent protocol entry point
├── .gitagent/
├── docs/ # Additional documentation
└── specs/ # Generated specifications and plans
GitAgent streamlines the integration of AI in development while ensuring human oversight and approval, making it an invaluable tool in modern software development practices.
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