cartog

Map your codebase. Navigate by graph, not grep.

cartog is a code graph indexer that gives LLM coding agents (Claude Code, Cursor, etc.) instant structural understanding of a codebase — replacing repeated grep/cat with targeted graph queries.

Why

Code is a graph of relationships (calls, imports, inherits, type references). cartog pre-computes this graph with tree-sitter, stores it in SQLite, and lets you query it instead of re-discovering structure from scratch every time.

Before (6+ tool calls, ~2000 tokens, might still miss references):

grep -r 'validate_token' .
cat src/auth/tokens.py
cat src/services/user.py
cat src/middleware/auth.py

After (2-3 calls, ~200 tokens, complete picture):

cartog refs validate_token
cartog outline src/auth/tokens.py
cartog impact validate_token

Install

cargo install cartog

Usage

cartog index .                              # Build the graph
cartog outline src/auth/tokens.py           # File structure without reading it
cartog refs validate_token                  # Who references this? (calls, imports, inherits, types)
cartog refs validate_token --kind calls     # Filter: only call sites
cartog callees authenticate                 # What does this call?
cartog impact SessionManager --depth 3      # What breaks if I change this?
cartog hierarchy BaseService                # Inheritance tree
cartog deps src/routes/auth.py              # File-level imports
cartog stats                                # Index summary

All commands support --json for structured output.

Example: outline

$ cartog outline auth/tokens.py
from datetime import datetime, timedelta  L3
from typing import Optional  L4
import hashlib  L5
class TokenError  L11-14
class ExpiredTokenError  L17-20
function generate_token(user: User, expires_in: int = 3600) -> str  L23-27
function validate_token(token: str) -> Optional[User]  L30-44
function lookup_session(token: str) -> Optional[Session]  L47-49
function refresh_token(old_token: str) -> str  L52-56
function revoke_token(token: str) -> bool  L59-65

Example: impact analysis

$ cartog impact validate_token --depth 3
  calls  get_current_user  auth/service.py:40
  calls  refresh_token  auth/tokens.py:54
    calls  impersonate  auth/service.py:52

How It Works

1. Index — walks your project, parses each file with tree-sitter, extracts symbols (functions, classes, methods, imports, variables) and edges (calls, imports, inherits, raises, type references)
2. Store — writes everything to a local .cartog.db SQLite file
3. Resolve — links edges by name with scope-aware heuristic matching (same file > same directory > unique project match)
4. Query — instant lookups against the pre-computed graph

Re-indexing is incremental: only files with changed content hashes are re-parsed.

Supported Languages

Design Trade-offs

• Structural, not semantic — name-based resolution (~90% accuracy), not full type analysis. Good enough for navigation; LSP can be layered on later.
• Zero dependencies — single 3.5MB binary + SQLite file. No language server, no embedding model, no graph DB.
• Incremental — SHA256 hash per file, only re-indexes what changed.

Agent Skill

Install cartog as an Agent Skill for Claude Code, Cursor, Copilot, and other compatible agents:

npx skills add jrollin/cartog

Or install manually:

cp -r skills/cartog ~/.claude/skills/

The skill teaches your AI agent to use cartog for code navigation instead of grep/cat. See Claude Code Integration for details.

Documentation

• Usage
• Claude Code Integration
• Product Overview
• Technology Stack
• Project Structure

License

MIT