English
agentic-cursorrules is a Python-based tool that manages multiple AI agents in large code bases through strict file tree partitioning and domain boundaries. It prevents merge conflicts and maintains the code base consistency by partitioning the code base, generating domain-specific Markdown files, and providing AI agents with clear context and access rules. The background of this tool is to improve project management efficiency in AI-assisted development, especially in complex projects, by reducing conflicts and improving code maintainability. The main advantages of this tool include reducing code conflicts, improving development efficiency and maintainability, and are suitable for large projects that require AI assistance in different code parts.
Demand population:
"The target audience is software developers and teams, especially when developing large projects, where AI aid is needed to improve efficiency and reduce code conflicts. This tool is suitable for large projects that require AI aid in different code parts, helping developers maintain a clear structure of code and reduce conflicts in development."
Example of usage scenarios:
In a large web development project, front-end developers use agentic-cursorrules to manage their code parts separately, reducing conflicts during merges.
Database developers use this tool to focus on the code at the database layer without affecting the development of other service layers.
In a project that conducts microservice architecture, each service is managed by a dedicated AI agent, ensuring that the code between services does not conflict with agentic-cursorrules .
Product Features:
- Codebase partition: define clear boundaries through YAML configuration and generate separate file trees for each field.
- Access control: Each agent only receives its specific domain file tree information and operates within defined boundaries.
- Conflict prevention: physical separation is achieved through smart file tree partitioning, significantly reducing the risk of modification overlap.
- Flexible domain configuration: Flexible domain configuration through YAML files to adapt to different project needs.
- Support up to 4 concurrent agents: optimized support for multi-proxy environments to improve performance and management efficiency.
- Markdown-based instruction set: provides detailed operation instructions and context information for the agent.
- File tree context awareness: The agent can understand the file tree structure and provide more accurate code assistance.
Tutorials for use:
1.Clone the project to local: Use the `git clone` command to clone the agentic-cursorrules project to the local directory.
2. Install dependencies: Create a virtual environment in the project directory and activate it, and then use `pip install -r requirements.txt` to install the required dependencies.
3. Configure .cursorrules file: Copy and rename the .example configuration file to .cursorrules, and adjust the configuration as needed.
4. Configuration domain: Define the domain and architecture boundaries of the project in the config.yaml file.
5. Run the proxy generator: execute `python main.py` to generate the proxy file, or use the `--recurring` option for regular updates.
6. Reference proxy file: Reference the generated proxy Markdown file in the development environment to enable domain-specific AI proxy functionality.
