cased-kit 0.1.1

A modular toolkit for LLM-powered codebase understanding.

kit 🛠️ Code Intelligence Toolkit

kit is a modular, production-grade Python toolkit for codebase mapping, symbol extraction, code search, and building LLM-powered developer tools, agents, and workflows.

Use kit to build things like code reviewers, code generators, even IDEs, all enriched with the right code context.

Quick Installation

Install from PyPI

# Installation (includes all features)
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 .

Basic Usage

from kit import Repository

# Load a local repository
repo = Repository("/path/to/your/local/codebase")

# Load a remote public GitHub repo
# repo = Repository("https://github.com/owner/repo")

# Explore the repo
print(repo.get_file_tree())
# Output: [{"path": "src/main.py", "is_dir": False, ...}, ...]

print(repo.extract_symbols('src/main.py'))
# Output: [{"name": "main", "type": "function", "file": "src/main.py", ...}, ...]

Key Features & Capabilities

kit helps your apps and agents deeply understand and interact with codebases, providing the core components to build your own AI-powered developer tools. Here are just a few of the things you can do:

• Explore Code Structure:

  • Get a bird's-eye view with repo.get_file_tree() to list all files and directories.
  • Dive into specifics with repo.extract_symbols() to identify all functions, classes, and other code constructs, either across the entire repository or within a single file.

• Pinpoint Information:

  • Perform precise textual or regular expression searches across your codebase using repo.search_text().
  • Track down every definition and reference of a specific symbol (like a function or class) with repo.find_symbol_usages().

• Prepare Code for LLMs & Analysis:

  • Break down large files into manageable pieces for LLM context windows using repo.chunk_file_by_lines() or repo.chunk_file_by_symbols().
  • Instantly grab the full definition of a function or class just by knowing a line number within it using repo.extract_context_around_line().

• Generate Code Summaries (Alpha):

  • Leverage LLMs to create natural language summaries for files, functions, or classes using the Summarizer (e.g., summarizer.summarize_file(), summarizer.summarize_function()).
  • Build a searchable semantic index of these AI-generated docstrings with DocstringIndexer and query it with SummarySearcher to find code based on intent and meaning.

• And much more... kit also offers capabilities for semantic search on raw code, building custom context for LLMs, and more.

Explore the Full Documentation for detailed usage, advanced features, and practical examples.

License

MIT License

Contributing

We welcome contributions! Please see our Roadmap for project directions.