Build AI infrastructure automation with kit

TL;DR

kit gives any LLM-powered app IDE-grade knowledge of a repo. Search, extract symbols, build dependency graphs, or ask for AI summaries in two lines of Python—or expose everything as JSON-schema tools the model can call by itself.

from kit import Repository
repo = Repository("./")
print(repo.search_text("TODO"))
print(repo.get_summarizer().summarize_file("api/app.py"))

Prefer MCP? Run python -m kit.mcp and you’re all set.

Why we built it

We use kit to power Cased’s infrastructure automation agent. We think the building blocks for developer tools should be free and open source.

Quick tour

Capability	One-liner	Use-case
File tree	`repo.get_file_tree()`	tree-view in chat
Text search	`repo.search_text("AuthError")`	”grep inside ChatGPT”
Symbols	`repo.extract_symbols("services/payments.py")`	jump-to-definition w/o LSP
Dependencies	`repo.get_dependency_analyzer("python").build_dependency_graph()`	import spaghetti viz
Summaries	`repo.get_code_summary(file_path="api/app.py")`	PR descriptions

Deep dive – a multi-turn session

Open repo

{
  "name": "open_repository",
  "arguments": { "path_or_url": "/workspace/my-app" }
}

User: “Where is calculate_price defined?”
Model ➜ search_code → finds services/pricing.py:120.
Model ➜ get_file_content for that file, responds with snippet.
User: “Why does it return NaN?”
Model ➜ extract_symbols → isolates the function → get_code_summary → explains in plain English.

No if/else orchestration in your backend—just register the schema.

Running in-process vs. server

Server mode – python -m kit.mcp; great for polyglot stacks, isolates heavy indexing.
In-process – import Repository, wrap what you need, register those wrappers as tools; zero IPC overhead.

Get started

pip install cased-kit   # core + FastAPI server
python -m kit.mcp       # starts on localhost:8000

Docs: https://kit.cased.com | GitHub: https://github.com/cased/kit

Happy hacking!