karst

Code context for AI dev tools. karst sits between your repo and any AI tool — Cursor, Claude Desktop, a custom agent — and feeds it the right slice of the codebase: graph-grounded, pack-scoped, and cited to file:line. The result is ~60% fewer input tokens per question, answers you can verify, and a blast-radius check before you change anything.

It runs locally, returns context (not answers) over MCP, and never calls an LLM itself — so you don't give karst an API key. Your IDE already has the model; karst just makes what it reads sharp and cheap.

uv tool install karst      # recommended — fast, and puts `karst` on PATH for you
# or
pipx install karst         # isolated install, also handles PATH
# or
pip install karst          # if `karst` isn't found after, use `python -m karst …`

uv and pipx are the cleanest because they put the karst command on your PATH automatically. With plain pip --user (notably Microsoft Store Python) the command may not be on PATH — in that case python -m karst … always works, no PATH setup required.

Why

Most "chat with your codebase" tools dump tens of thousands of vaguely-related tokens into the model on every question. You can't see what was loaded, you can't scope it, and the bill arrives at the end of the month. karst inverts that:

• Scopes — pack-filtered retrieval reads ~200 chunks, not 5,000.
• Cites — every chunk carries an exact file:line. Verify, don't trust.
• Predicts — a real call/import graph answers "what else breaks if I change this?" — which embeddings alone can't.

Measured on a real 246-file NestJS + Next.js repo: 906 chunks indexed, re-index 343s → 2.3s incremental, ~$0.019 per question on Sonnet 4.6 (shown before the call), 60% fewer tokens with packs attached.

Quickstart (CLI)

karst command not found? Your Python Scripts dir isn't on PATH (common with Microsoft Store Python). Everything below works the same with python -m karst … — no PATH setup. (Or install via uv/pipx, which put karst on PATH for you.)

cd your-project

# one command: index + call/import graph + suggested packs
karst quickstart                 #  or:  python -m karst quickstart

# ask questions about the code (defaults to this folder's index)
karst ask "how does checkout charge the user?" --no-llm    # cited code, no API key
karst ask -i                     # interactive: ask many questions

# what breaks if I change a function?
karst impact --target checkout --graph-path ~/.karst/indexes/your-project/graph.pkl

# review a diff with severity-tagged, cited findings
karst review --staged --storage ~/.karst/indexes/your-project

karst examples                   # a copy-paste cheatsheet of everything

karst quickstart prints the exact follow-up commands with your index path filled in. karst ask writes an LLM answer when ANTHROPIC_API_KEY / OPENAI_API_KEY is set; otherwise add --no-llm for cited chunks (no key). The MCP server below needs no key either — your IDE supplies the model.

Use it from your IDE (MCP)

karst ships an MCP server (karst-mcp) exposing five tools — search_code, find_impact, list_packs, index_status, index_repository — over stdio.

Claude Desktop (claude_desktop_config.json) or Cursor (.cursor/mcp.json) — pick whichever launcher you have:

{
  "mcpServers": {
    "karst": { "command": "uvx", "args": ["--from", "karst", "karst-mcp"] }
  }
}

uvx needs nothing pre-installed — it fetches and runs karst on demand. Already installed it? { "command": "karst-mcp" } works too. No PATH at all? Use { "command": "python", "args": ["-m", "karst.mcp_server"] }.

Restart the host, then ask normally — it calls karst's tools when useful and gets back scoped, cited context. Full setup is in docs/MCP.md.

Guides

New here? Start with whichever fits you:

• Why karst? — what it is and what it's for, in plain language. Read this first if you're not sure what problem it solves.
• Quickstart — zero to asking real questions in 5 minutes, no API key, with real output.
• For vibe coders — use karst from Cursor / Claude Desktop with no CLI commands — you just chat.
• Connect your AI tool — copy-paste MCP setup for every client: Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, Zed, JetBrains, plus the web apps.
• Self-hosted & air-gapped — run karst and the AI answers fully on your machine with a local model. For teams whose code can't go to the cloud.
• Cookbook — real scenarios (onboarding, blast radius, cutting token cost, reviewing a diff) with copy-paste commands.
• MCP setup — connect karst to any MCP client.

How it works

1. Index — tree-sitter splits every function, class and method into an AST-aware chunk (Python, JS, TS, Go, Rust, Java); chunks are embedded into a local Qdrant store. Incremental: a SHA manifest + embedding cache skip unchanged files.
2. Graph — a NetworkX knowledge graph of CALLS / IMPORTS / CONTAINS edges powers impact analysis ("what depends on this?").
3. Pack — related files become named, attachable context packs (auth, billing). A query loads only its pack.
4. Serve — the MCP server returns ranked, file:line-cited chunks; your host's model reasons over them.

Everything is local and offline-capable (FastEmbed/ONNX embeddings, Qdrant local mode, sqlite caches — no Docker, no daemon).

Status

Live: AST chunking (6 languages), call/import graph + impact analysis, pack-scoped retrieval, token + cost meter, incremental indexing + embedding cache, diff code review with inline PR posting (review --pr --post-to-pr), and the MCP server over both stdio and remote Streamable-HTTP (karst-mcp --http). Coming next: hosted indexing, team-shared pack libraries, an autonomous GitHub PR review bot, and OAuth for browser connectors (claude.ai / ChatGPT).

License

Apache-2.0. See LICENSE.