slurp — token-budget-aware graph navigation for AI agents

// install

Quick install reference

Use case	Command
Any project rec	`uv tool install slurp-graph`
Python project with uv	`uv add slurp-graph`
With pip	`pip install slurp-graph`

Using a non-Python project (JS, Go, TypeScript...)? Use uv tool install or pip install — not uv add.

// the problem

Your LLM doesn't need the whole bowl

graphify maps your entire codebase into a knowledge graph. Great. But injecting every node into every query is wasteful — your LLM spends most of its context budget on nodes it'll never use.

slurp scores each node against your query using PageRank + TF-IDF — zero LLM calls, zero extra tokens spent — and serves only what fits inside your budget.

69,418 full graph (tokens)

→

4,000 slurp budget (tokens)

94.2% saved

Benchmark: PrismaStats codebase · 2,111 nodes · "player stats" query

// benchmark

Real numbers on a real codebase

PrismaStats: 2,111 nodes, 69,418 tokens. No mocks.

Query	Budget	Tokens used	Full graph	Saved
"auth flow"	2,000	2,000	69,418	97.1%
"player stats"	4,000	4,000	69,418	94.2%
"database schema"	8,000	8,000	69,418	88.5%

93.3% mean savings

97.1% best case

0 extra LLM calls

// zero dependencies

No external tools required

slurp index analyzes your codebase using pure static analysis — no LLMs, no external APIs, zero cost. Python uses the ast stdlib, TypeScript/JS uses tree-sitter, Go uses a regex parser.

Run it once and start querying immediately. No accounts, no API keys, no cloud.

# Index your project locally
slurp index .
# Output: 1473 nodes · 1443 edges · 30 files
# Next: slurp 'your query' --graph graphify-out/graph.json

// demo

See it in action

The interactive visualizer shows the subgraph slurp selected — and how much you saved.

slurp "auth flow" --graph graphify-out/graph.json --budget 4000 --viz
Opens an interactive subgraph explorer with a live token-savings badge.

// how it works

Three steps to zero wasted tokens

Install slurp

One command, no dependencies beyond Python.

# install
pip install slurp-graph

Index your project locally

slurp index . analyzes your code with pure static analysis — no LLMs, no APIs, no cost.

# generates the graph locally
slurp index .
# Output: 1473 nodes · 1443 edges · 30 files

ℹKeep the graph fresh: re-run slurp index . after big changes · slurp index . --watch for background auto-indexing · add as a GitHub Actions step for CI/CD.

Configure MCP once, done forever

Add slurp to your .mcp.json. Claude Code calls it automatically on every query.

Global install:

{
  "mcpServers": {
    "slurp": {
      "command": "slurp",
      "args": ["serve", "--graph", "graphify-out/graph.json"]
    }
  }
}

Python project with uv:

{
  "mcpServers": {
    "slurp": {
      "command": "uv",
      "args": ["run", "slurp", "serve", "--graph", "graphify-out/graph.json"]
    }
  }
}

Fully automatic after step 3. Claude Code calls slurp on every query — no extra commands, no prompt engineering. You work exactly as before, just cheaper.

Already using graphify? slurp works with your existing graph.json out of the box.

// features

Built different.

slurp index

Zero dependencies. No APIs. No cost.

# index your project locally
slurp index .
# 1473 nodes · 1443 edges · 30 files

MCP server

Configure once. Claude Code handles the rest.

--viz

Interactive subgraph explorer

slurp diff

Compare graph snapshots

slurp benchmark

Measure token savings

--inject-code

Attach source code to nodes