repo2kg 0.5.11

Build a code Knowledge Graph from any repository (Python/JS/TS/Java/Go/Rust/C/C++/Ruby/C#) for token-efficient AI agent context. Supports JSON and TOON output formats. Agent-friendly: generates CLAUDE.md, CODEBASE.md, and supports zero-dep queries.

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Turn any repository into a Knowledge Graph for token-efficient AI agent context.

---

The Problem

Every time you ask Claude Code, Copilot, or Codex to work on your project, it reads hundreds of files — burning through your context window before it even starts.

Without repo2kg                      With repo2kg
────────────────────                 ─────────────────────
Agent reads 229 files                Agent reads CODEBASE.md
→ ~53,000+ tokens used               → ~4,000 tokens (85%-95% saved)
→ Slow, hits context limits          → Fast, precise, relevant
→ New session = start over           → KG persists forever

repo2kg builds a searchable graph of your entire codebase — functions, classes, call edges — so agents query exactly what they need instead of everything.

---

Token Efficiency: Real Numbers

Tested on a project with 229 source files, 2,909 nodes, and 4,615 call edges. The agent understood the full architecture without opening a single raw source file.

With repo2kg

The agent ran four commands:

repo2kg list
repo2kg stats --kg kg.json
repo2kg query-lite "main app entry" --kg kg.json
repo2kg query-lite "authentication login jwt token" --kg kg.json
repo2kg export --kg kg.json

Step	Tokens
stats output	~100
4 × query-lite results	~2,400
CODEBASE.md overview	~1,500
Total	~4,000 tokens

Without repo2kg

# Agent must search and read files manually
glob file listing → grep functions/classes → read source files

Step	Tokens
File listing	~3,000
Reading 10–20 source files	~15,000–40,000
Grep results	~5,000–10,000
Total	~23,000–53,000 tokens

Result

Method	Tokens	Quality
repo2kg	~4,000	High — structured graph, signatures, call edges
Normal search	~23,000–53,000	Medium — raw code scanning

Reduction:  83–92% fewer tokens during codebase discovery
Full session savings (including messages):  ~40–60%

The larger and more interconnected your repo, the bigger the gain:

Project Size	Files	Token Savings
Small	< 20	10–20%
Medium	20–100	40–70%
Large	100–500	70–90%
Monorepo	500+	85–95%

---

Quick Start

pip install repo2kg

# 1. One-time global setup (runs in seconds, no heavy deps)
repo2kg user-setup

# 2. Build a KG for your project
cd /your/project
repo2kg build --repo . --out kg.json

# 3. Generate agent instruction files
repo2kg agent-setup --kg kg.json --dir .

# 4. Query it
repo2kg query "how does authentication work" --kg kg.json

---

Installation

pip install repo2kg

Requirements: Python 3.10+

Dependencies auto-installed:

Package	Size	When Used
numpy	~20 MB	build, query
faiss-cpu	~60 MB	build, query (CPU only, no GPU/CUDA needed)
sentence-transformers	~2 GB (PyTorch)	build, query
tree-sitter + 9 grammars	~10 MB	build (AST parsing)

Note: query-lite, user-setup, export, agent-setup, list, and register all start instantly — they never load PyTorch or FAISS. Only build and query load the heavy dependencies.

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How It Works

Your Repository
       │
       ▼
┌──────────────────────────────────────┐
│  Parse all source files              │  ← tree-sitter (accurate) or regex (fallback)
│  Python/JS/TS/Java/Go/Rust/C/C++    │
│  Ruby/C#  (10 languages)            │
└──────────────────────────────────────┘
       │
       ▼
┌──────────────────────────────────────┐
│  Build Knowledge Graph               │
│  • Nodes: functions, classes, methods│
│  • Edges: who calls whom             │
│  • Data: signatures, docstrings,     │
│    8-line body previews              │
└──────────────────────────────────────┘
       │                    │
       ▼                    ▼
  kg.json / kg.toon     FAISS index
  (structured data)     (semantic vectors)
       │                    │
       └────────┬───────────┘
                ▼
         query / query-lite
         → token-minimal context for your agent

Two query modes

Command	How it searches	Deps needed	Speed
query	Semantic similarity (FAISS + embeddings)	PyTorch + FAISS	Slower start
query-lite	Keyword matching + graph expansion	None (stdlib only)	Instant

Both do graph expansion — they find entry-point nodes, then follow call edges to return related code automatically.

Two output formats

Format	Extension	Best for
JSON	.json	Universal, default
TOON	.toon	LLM context windows (~40% fewer tokens)

---

Complete Workflow

Step 1 — Global setup (once ever)

repo2kg user-setup

Writes agent instructions to:

• ~/.claude/CLAUDE.md — Claude Code reads this at the start of every session
• ~/.codex/AGENTS.md — Codex equivalent
• ~/.repo2kg/GLOBAL_AGENTS.md — reference doc

From this point on, any registered project is automatically discovered by your agent.

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Step 2 — Discover & register existing projects

repo2kg scan                     # scans your entire home directory
repo2kg scan --root ~/projects   # or a specific folder

Finds every KG file (by detecting companion .faiss files) and registers them in ~/.repo2kg/registry.json. Re-run any time.

---

Step 3 — Build a KG for your project

cd /your/project

repo2kg build --repo . --out kg.json     # JSON (default)
repo2kg build --repo . --out kg.toon     # TOON (40% fewer tokens for LLMs)

Output:

KG ready: 421 nodes, 892 call edges from 63 files
Languages: javascript: 38, python: 25
Saved to kg.json [JSON] (421 nodes)

Three files are created: kg.json, kg.json.faiss, kg.json.idx

---

Step 4 — Generate agent files

repo2kg agent-setup --kg kg.json --dir .

Creates five files:

File	Purpose
CODEBASE.md	Full overview — file map, classes, call graph, all signatures
CLAUDE.md	Tells Claude Code to use the KG instead of reading files
.copilot-instructions.md	Same, for GitHub Copilot
.github/copilot-instructions.md	Same, for Copilot agent mode
AGENTS.md	Node schema + query strategies for multi-agent systems

# Commit everything
git add CLAUDE.md CODEBASE.md AGENTS.md kg.json kg.json.faiss kg.json.idx
git commit -m "Add repo2kg knowledge graph"

---

Step 5 — Query it

# Keyword search — zero deps, instant
repo2kg query-lite "authentication" --kg kg.json
repo2kg query-lite "database connection" --kg kg.json --k 10 --depth 2

# Semantic search — more accurate, needs FAISS
repo2kg query "how does auth work" --kg kg.json
repo2kg query "payment processing flow" --kg kg.json --depth 2

# Structured JSON output (for scripts/agents)
repo2kg query-lite "auth" --kg kg.json --format json

Sample output:

# Query: authentication
# Nodes returned: 8  |  ~210 tokens
# Entry points: verify_token, JWTService, authenticate_user
# ─────────────────────────────────────────────

# METHOD: verify_token
# file: auth/jwt.py  class: JWTService
def JWTService.verify_token(self, token: str) -> dict:
"""Verify and decode a JWT token."""
    decoded = jwt.decode(token, self.secret, algorithms=["HS256"])
    return self.get_user_from_cache(decoded["sub"])
# calls: decode, get_user_from_cache
# called_by: authenticate_user, AuthMiddleware.process_request

---

Tree-Sitter Parsing

repo2kg uses tree-sitter for all non-Python languages — installed automatically with pip install repo2kg. No extra steps needed.

Advantage over regex	Detail
Multi-line signatures	Captured correctly
Nested classes	Exact parent class
Code in strings/comments	Ignored (no false matches)
Generic types List<T>	Handled correctly
Arrow functions in classes	Correct method detection

Python always uses Python's own ast module. If tree-sitter fails for any file, repo2kg silently falls back to regex.

---

TOON Format

TOON (Token-Oriented Object Notation) is a compact line-oriented format optimised for LLM context windows. It uses ~40% fewer tokens than JSON.

repo2kg build --repo . --out kg.toon     # Save as TOON
repo2kg query-lite "auth" --kg kg.toon   # Query TOON directly

Example TOON output for a single node:

nodes[788]:
  -
    id: "src/auth.ts::AuthService"
    name: AuthService
    kind: class
    file: src/auth.ts
    signature: export class AuthService
    docstring: Handles JWT authentication
    calls[2]: verifyToken,refreshToken
    callers[0]:

All commands (build, query, query-lite, export, agent-setup, stats) work with .toon files identically to .json.

---

How Agents Use It (Zero Dependencies)

The global ~/.claude/CLAUDE.md (installed by user-setup) tells the agent to run this at the start of every task:

import json
from pathlib import Path

# Walk up from cwd to find the closest registered project
registry = json.load(open(Path.home() / ".repo2kg" / "registry.json"))
check = Path.cwd()
while check != check.parent:
    if str(check) in registry["projects"]:
        kg = json.load(open(registry["projects"][str(check)]["kg"]))
        break
    check = check.parent

# Search — no FAISS, no embeddings, pure stdlib
matches = [n for n in kg.values() if "auth" in n["name"].lower()]
for n in matches[:10]:
    print(n["signature"], "—", n.get("docstring", "")[:100])
    print("calls:", [c.split("::")[-1] for c in n.get("calls", [])])

The agent only reads actual source files when the 8-line body_preview is not enough.

---

CLI Reference

repo2kg build        Build KG from a repository
repo2kg query        Semantic search (requires FAISS)
repo2kg query-lite   Keyword search (zero dependencies)
repo2kg export       Export as CODEBASE.md
repo2kg agent-setup  Generate all agent instruction files
repo2kg user-setup   Install global agent instructions (run once)
repo2kg scan         Auto-discover and register all KGs under a directory
repo2kg register     Register a single project manually
repo2kg list         Show all registered projects
repo2kg stats        Show KG node/edge statistics
repo2kg info         Print machine-readable tool info (for agents)

Flags

Flag	Default	Description
--repo	.	Repository root to scan
--out	kg.json	Output path (.json or .toon)
--kg	kg.json	Path to a saved KG file
--k	5	Number of top results
--depth	1	Graph traversal depth (1 = direct calls, 2 = calls of calls)
--format	text	Output format: text or json
--dir	.	Target directory for agent-setup
--root	~	Root directory for scan
-v	off	Verbose/debug logging

Auto-excluded directories

__pycache__  .git  node_modules  .tox  .venv  venv  env
.mypy_cache  .pytest_cache  dist  build  site-packages

Add more: repo2kg build --repo . --exclude migrations fixtures

---

Python API

# Full mode (FAISS + sentence-transformers)
from repo2kg import RepoKG

kg = RepoKG().build("./my_project")
kg.save("kg.json")                                # JSON
kg.save("kg.toon")                               # TOON (fewer tokens)

kg = RepoKG.load("kg.json")
print(kg.query("auth flow", k=5, depth=2))        # text output
data = kg.query_json("payment", k=3)              # structured dict

# Lightweight mode — stdlib only, safe for agents
from repo2kg import RepoKGLite

kg = RepoKGLite("kg.json")
print(kg.query("auth", k=5, depth=1))             # text output
result = kg.query_json("database", k=3)           # structured dict
callers = kg.get_callers("auth/service.py::AuthService.login")
callees = kg.get_callees("auth/service.py::AuthService.login")

---

Node Schema

Every node in kg.json / kg.toon:

{
  "auth/jwt.py::JWTService.verify_token": {
    "id":           "auth/jwt.py::JWTService.verify_token",
    "name":         "verify_token",
    "kind":         "method",
    "file":         "auth/jwt.py",
    "parent_class": "JWTService",
    "signature":    "def JWTService.verify_token(self, token: str) -> dict:",
    "docstring":    "Verify and decode a JWT token.",
    "body_preview": "    decoded = jwt.decode(token, self.secret, ...)\n    ...",
    "calls":        ["auth/jwt.py::JWTService.decode", "cache/redis.py::get_user"],
    "callers":      ["auth/service.py::authenticate_user"],
    "imports":      ["jwt", "datetime"]
  }
}

---

Supported Languages

Language	Extensions	Parser
Python	.py	ast module (always accurate)
JavaScript	.js .jsx .mjs	tree-sitter / regex
TypeScript	.ts .tsx .mts	tree-sitter / regex
Java	.java	tree-sitter / regex
Go	.go	tree-sitter / regex
Rust	.rs	tree-sitter / regex
C	.c .h	tree-sitter / regex
C++	.cpp .cc .cxx .hpp	tree-sitter / regex
Ruby	.rb	tree-sitter / regex
C#	.cs	tree-sitter / regex

---

Limitations

• Static analysis only — captures defined calls, not dynamic dispatch or runtime-generated calls
• Name-based call resolution — same-file preferred; no full type inference
• No incremental updates — rebuilds the full graph each time (30–60s for large repos)

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Contributing

Contributions welcome! Open an issue first for major changes.

git clone https://github.com/mugenGH/repo2kg.git
cd repo2kg
pip install -e .

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License

MIT