codebase-cartography

To generate a codebase map for AI context, you create a codemap.md file that captures project structure, dependencies, and entry points under 2000 tokens. This provides AI agents with instant architectural context for codebase exploration and onboarding.

A codemap is a concise markdown document that reveals project structure, dependencies, and entry points. You need it for onboarding because it gives AI assistants instant architectural context for unfamiliar codebases, reducing exploration time and token waste.

Yes, this codebase mapping approach works with monorepos and single-repo projects. It performs language and framework detection to generate a concise architectural overview suitable for both project structures.

Yes, you can use the generated codemap for architecture planning and code reviews. It provides a concise overview of top dependencies and project structure to guide AI assistants during context-aware analysis and decision making.

The best way to provide AI agents with codebase architectural context is generating a concise codemap.md file. This approach extracts top dependencies and entry points under 2000 tokens, avoiding token waste during unfamiliar codebase exploration.

The generated codemap has a strict limitation of 2000 tokens. It captures codebase structure, dependencies, and entry points concisely to ensure AI agents receive architectural context without exceeding context windows or wasting tokens.