Code Atlas

A code intelligence graph that gives AI coding agents deep, token-efficient understanding of your codebase — structure, docs, and dependencies in one searchable graph.

Map your codebase. Search it three ways. Feed it to agents.

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The Problem

Every time an AI agent touches your codebase, it burns tokens just figuring out where things are. Grep for a function name. Read five files to understand the call chain. Search docs for context. Repeat — across every task, every session. On a large project, agents can spend 30–50% of their context window on orientation before they write a single line of code.

Many tools solve one piece of this: semantic search, or graph traversal, or keyword lookup. But a developer doesn't understand a codebase through one lens — they build a mental model that connects structure, meaning, and names simultaneously. Agents need the same thing.

Code Atlas is that mental model, externalized as a graph.

What Is This?

Code Atlas builds a graph database of your entire codebase — code structure, documentation, and dependencies — and exposes it via MCP tools that AI coding agents can use to understand, navigate, and reason about your code.

Three search types, one system:

• Graph traversal — follow relationships: who calls this function? What does this class inherit from? What services depend on this library?
• Semantic search — find code by meaning: "authentication middleware" finds relevant code even if it's named verify_token_chain
• BM25 keyword search — exact matches: find that specific error message, config key, or function name

All powered by Memgraph as a single backend.

Key Features

• Monorepo-native — auto-detects sub-projects, tracks cross-project dependencies, scoped queries
• Documentation as first-class — indexes markdown docs, ADRs, and READMEs with links to the code they describe
• AST-level incremental indexing — only re-indexes the entities that actually changed, not entire files
• Pattern detection — pluggable detectors for decorator routing, event handlers, DI, test→code mappings, and more
• Library awareness — lightweight stubs for external dependencies, full indexing for internal libraries
• Self-hosted — runs locally with Docker. No data leaves your machine
• No additional API costs — agent-first design means all intelligence runs through your existing subscription; local embeddings via TEI, no extra API keys
• Token-efficient — budget-aware context assembly that prioritizes what matters most
• Pluggable AI — TEI for embeddings, LiteLLM for LLM calls, or bring your own
• MCP server — works with Claude Code, Cursor, Windsurf, or any MCP-compatible client

How Does This Compare?

Several excellent tools exist in this space — graph-based analyzers, semantic search engines, wiki generators, and IDE-integrated indexers. Code Atlas builds on their ideas while addressing a gap: no single tool combines graph traversal, semantic search, and BM25 keyword search with documentation intelligence and MCP exposure.

For a detailed comparison covering DeepWiki, Cursor, Sourcegraph Cody, Kit, code-graph-rag, codegraph-rust, and more, see wiki/landscape.md.

MCP Tools

15 tools exposed via the Model Context Protocol, designed to minimize context window overhead.

| Tool | What it does