comP: Code Context Engine for AI Agents

comP is an open-source tool designed to help AI coding agents understand your codebase better. It acts as a local engine that indexes your entire project and builds a smart map of your code. This map allows AI tools to find exactly what they need without reading every single file. The result is faster work and significantly lower costs for using AI models.

Benefits

The main advantage of comP is efficiency. It reduces the number of tokens AI models need to process by up to 60 to 80 percent. This happens because comP provides only the most relevant code snippets instead of dumping the whole project into the AI. It also keeps everything private since all processing happens on your own computer. No data is sent to the cloud. The tool supports over 30 programming languages and includes features like impact analysis to show you what might break if you change a specific piece of code. It also tracks how many tokens you save over time.

Use Cases

comP is built for developers who use AI assistants in their daily workflow. It works inside Visual Studio Code and connects with many popular AI agents like Claude Code, GitHub Copilot, Cursor, and Cline. Developers can use it to get quick answers about their code, analyze the impact of changes before making them, or generate optimized code for specific tasks. It is especially useful for large projects where reading all the code would be too slow or expensive for an AI model. The tool also helps when working with documentation files like Markdown, PDFs, and spreadsheets by indexing them alongside the code.

Pricing

comP is completely free and open-source. It is available under the MIT License, which means anyone can use it without paying. You can install it directly from the Visual Studio Code marketplace or download the source code from GitHub to build it yourself.

Vibes

As an open-source project, comP has not yet received widespread public reviews or testimonials. However, the project maintains an active roadmap with frequent updates. Recent releases have added support for more file types like Word documents and PDFs, as well as better integration with various AI agents. The developers are actively working toward a stable version 1.0 to ensure wider compatibility and reliability for users.

Additional Information

comP was created by an independent developer and is hosted on GitHub. The project relies on open-source technologies like tree-sitter for parsing code and SQLite for storing the index. It uses the Model Context Protocol to communicate with AI agents, which is a standard that allows different tools to work together smoothly. The core of the tool is written in Rust for performance, while the user interface is built for Visual Studio Code. The team welcomes contributions from the community to help improve the tool.