The cased/kit toolkit provides Python tools for mapping, analyzing, and searching codebases, enabling the development of AI-powered developer tools like code reviewers and generators. It targets developers and AI engineers seeking to integrate deep code understanding into their applications, offering efficient code context retrieval and LLM-friendly preparation.

How It Works

Kit operates by parsing codebases to extract structural information like file trees and symbols (functions, classes). It employs techniques for text and symbol searching, dependency analysis, and code chunking for LLM context windows. A key differentiator is its ability to generate AI-powered code summaries and build semantic indexes for intent-based code search, facilitating richer interactions with code.

Quick Start & Requirements

• Install from PyPI: pip install cased-kit
• Install from source: git clone https://github.com/cased/kit.git && cd kit && uv venv .venv && source .venv/bin/activate && uv pip install -e .
• Requires Python.
• Documentation: https://docs.cased.com/kit

Highlighted Details

• Codebase mapping, symbol extraction, and text/symbol search capabilities.
• Tools for preparing code for LLMs, including file chunking and context extraction.
• AI-powered code summarization and semantic search via DocstringIndexer and SummarySearcher.
• Dependency analysis for understanding module relationships.
• MCP (Model Context Protocol) server for programmatic interaction with codebases (alpha).

Maintenance & Community

• Project direction and future plans are available on the roadmap.
• Contributions are welcomed via pull requests on forked repositories.

Licensing & Compatibility

• MIT License.
• Permissive license suitable for commercial use and integration into closed-source projects.

Limitations & Caveats

The MCP server functionality is currently in alpha, indicating potential instability or incomplete features.