kit is a production-ready 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.
# Standard installation (all features, including the kit-mcp server)
pip install cased-kitgit clone https://github.com/cased/kit.git
cd kit
uv venv .venv
source .venv/bin/activate
uv pip install -e .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", ...}, ...]kit helps your apps and agents understand and interact with codebases, with components to build your own AI-powered developer tools.
-
Explore Code Structure:
- High-level view with
repo.get_file_tree()to list all files and directories. - Dive down with
repo.extract_symbols()to identify functions, classes, and other code constructs, either across the entire repository or within a single file.
- High-level view with
-
Pinpoint Information:
- Run regular expression searches across your codebase using
repo.search_text(). - Track specific symbols (like a function or class) with
repo.find_symbol_usages().
- Run regular expression searches across your codebase using
-
Prepare Code for LLMs & Analysis:
- Break down large files into manageable pieces for LLM context windows using
repo.chunk_file_by_lines()orrepo.chunk_file_by_symbols(). - Get the full definition of a function or class off a line number within it using
repo.extract_context_around_line().
- Break down large files into manageable pieces for LLM context windows using
-
Generate Code Summaries:
- Use 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
DocstringIndexerand query it withSummarySearcherto find code based on intent and meaning.
- Use LLMs to create natural language summaries for files, functions, or classes using the
-
Analyze Code Dependencies:
- Map import relationships between modules using
repo.get_dependency_analyzer()to understand your codebase structure. - Generate dependency reports and LLM-friendly context with
analyzer.generate_dependency_report()andanalyzer.generate_llm_context().
- Map import relationships between modules using
-
And much more...
kitalso offers capabilities for semantic search on raw code, building custom context for LLMs, and more.
The kit tool includes an MCP (Model Context Protocol) server that allows AI agents and other development tools to interact with a codebase programmatically.
MCP support is currently in alpha. Add a stanza like this to your MCP tool:
The python executable invoked must be the one where cased-kit is installed.
If you see ModuleNotFoundError: No module named 'kit', ensure the Python
interpreter your MCP client is using is the correct one.
Explore the Full Documentation for detailed usage, advanced features, and practical examples.
MIT License
- Local Development: Check out our Running Tests guide to get started with local development.
- Project Direction: See our Roadmap for future plans and focus areas.
To contribute, fork the repository, make your changes, and submit a pull request.
{ "mcpServers": { "kit-mcp": { "command": "python", "args": ["-m", "kit.mcp"] } } }