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Give your AI assistant a brain for your codebase. Octocode transforms your project into a navigable knowledge graph that Claude, Cursor, and other AI agents can search, understand, and navigate.

🚀 Quick Start🤖 MCP Integration📖 Documentation🌐 Website


🤖 Built for AI Agents

The Problem: AI assistants are blind to your codebase. They can't search your files, understand dependencies, or remember context across sessions.

The Solution: Octocode's MCP server gives AI agents:

  • 🔍 Semantic search — Find code by meaning, not keywords

  • 🕸️ Knowledge graph — Navigate imports, calls, and dependencies

  • 📝 Code signatures — View structure without reading entire files

  • 🧠 Persistent memory — Remember decisions across conversations

Works with: Claude Desktop • Cursor • Windsurf • Any MCP-compatible AI

// Add to your AI assistant config
{
  "mcpServers": {
    "octocode": {
      "command": "octocode",
      "args": ["mcp", "--path", "/your/project"]
    }
  }
}

Now your AI assistant can:

You: "Where is authentication handled?"
AI: *searches your codebase* "Authentication is in src/middleware/auth.rs,
    which imports jwt.rs for token validation and calls user_store.rs for lookup."

You: "What files depend on the payment module?"
AI: *queries knowledge graph* "src/api/handlers/payment.rs imports payment/mod.rs,
    which is also used by src/workers/refund.rs and src/cron/billing.rs"

You: "Remember this bug fix for future reference"
AI: *stores in memory* "Got it. I'll remember this authentication bypass fix
    and apply similar patterns when reviewing security code."

🤔 Why Octocode?

Standard RAG treats your code as flat text chunks. It finds similar-sounding snippets but has no idea that auth_middleware.rs imports jwt.rs, calls user_store.rs, and is wired into router.rs. Octocode understands structure.

# Semantic search finds the right code
octocode search "authentication middleware"
→ src/middleware/auth.rs | Similarity 0.923

# GraphRAG reveals the full dependency chain
octocode graphrag get-relationships --node_id src/middleware/auth.rs
Outgoing:
  imports → jwt (src/auth/jwt.rs): token validation logic
  calls   → user_store (src/db/user_store.rs): user lookup by token
Incoming:
  imports ← router (src/router.rs): wires auth into the request pipeline

Octocode uses tree-sitter AST parsing to extract real symbols (functions, imports, dependencies), builds a GraphRAG knowledge graph of relationships between files, and exposes everything via MCP — so AI tools can navigate your project architecture, not just search it.

🔬 How It Works

Source Code → Tree-sitter AST → Symbols & Relationships → Knowledge Graph
                                        ↓
                    Embeddings + Hybrid Search + Reranking → MCP Server
  1. AST Parsing — tree-sitter extracts real code symbols (functions, classes, imports), not arbitrary text chunks

  2. Knowledge Graph — GraphRAG maps relationships between files: imports, calls, implements, extends, configures, and 9 more types — each with importance weighting

  3. Hybrid Search — semantic similarity + BM25 full-text search + reranking — not just vector embeddings

  4. MCP Server — exposes semantic_search, view_signatures, and graphrag tools to any MCP-compatible client

✨ What Makes It Different

Standard RAG

Doc Lookup Tools

Octocode

Indexes

Text chunks

External library docs

Your codebase structure (AST)

Understands

Similar text

API specs & usage

Functions, imports, dependencies

Cross-file

No

No

Yes — navigates the dependency graph

Relationships

No

No

imports, calls, implements, extends...

AI integration

Varies

MCP

Native MCP server + LSP

Doc tools give AI the manual for libraries you use. Octocode gives AI the blueprint of how you put them together.

Built with Rust for performance. Local-first for privacy. Open source (Apache 2.0) for transparency.

🚀 Quick Start

1. Install

# Universal installer (Linux, macOS, Windows)
curl -fsSL https://raw.githubusercontent.com/Muvon/octocode/master/install.sh | sh

# macOS with Homebrew
brew install muvon/tap/octocode
# Cargo (build from source)
cargo install --git https://github.com/Muvon/octocode

# Download binary from releases
# https://github.com/Muvon/octocode/releases

See Installation Guide for platform-specific instructions.

2. Set Up API Keys

# Required: Embedding provider (Voyage AI has 200M free tokens/month)
export VOYAGE_API_KEY="your-voyage-api-key"

# Optional: LLM for commit messages, code review
export OPENROUTER_API_KEY="your-openrouter-api-key"

Get your Voyage API key: voyageai.com (free tier available)

Octocode supports multiple embedding providers:

# OpenAI
export OPENAI_API_KEY="your-key"
octocode config --code-embedding-model "openai:text-embedding-3-small"

# Jina AI
export JINA_API_KEY="your-key"
octocode config --code-embedding-model "jina:jina-embeddings-v3"

# Google
export GOOGLE_API_KEY="your-key"
octocode config --code-embedding-model "google:text-embedding-005"

See API Keys guide for all supported providers.

3. Index Your Codebase

cd /your/project
octocode index
# → Indexed 12,847 blocks across 342 files

4. Search Your Code

# Natural language search
octocode search "authentication middleware"

# Multi-query for broader results
octocode search "auth" "middleware" "session"

# Filter by language
octocode search "database connection pool" --lang rust

# Search commit history
octocode search "authentication refactor" --mode commits

5. Connect Your AI Assistant

Add to your MCP client config (Claude Desktop, Cursor, Windsurf):

{
  "mcpServers": {
    "octocode": {
      "command": "octocode",
      "args": ["mcp", "--path", "/your/project"]
    }
  }
}

Done! Your AI assistant now understands your codebase structure.

🔌 MCP Server Integration

Octocode includes a built-in MCP server that exposes your codebase as tools to AI assistants. This is the primary way to use Octocode — give your AI assistant direct access to search and navigate your code.

Available Tools

Tool

What It Does

semantic_search

Find code by meaning — "authentication flow", "error handling", "database queries"

view_signatures

View file structure — function signatures, class definitions, imports

graphrag

Query relationships — "what calls this function?", "what does this module import?"

structural_search

AST pattern matching — find .unwrap() calls, new instantiations, specific patterns

Conversational AI Examples

Once connected, your AI assistant can answer questions about your codebase:

You: "Where is user authentication implemented?"
AI: *uses semantic_search* "Found in src/auth/login.rs. The authenticate() function
    validates credentials against the database, generates a JWT token, and stores
    the session in Redis."

You: "What files depend on the payment module?"
AI: *uses graphrag* "src/api/handlers/payment.rs imports payment/mod.rs, which is also
    used by src/workers/refund.rs and src/cron/billing.rs. The payment module exports
    process_payment() and validate_transaction() functions."

You: "Show me all error handling in the API layer"
AI: *uses structural_search* "Found 23 error handling patterns in src/api/:
    - 15 use Result<T, ApiError> with explicit error types
    - 8 use .unwrap() (potential panics in handlers/user.rs:42, handlers/auth.rs:87)
    - 3 use .expect() with custom messages"

Quick Setup

Octomind (Recommended) — Zero setup, Octocode pre-configured:

curl -fsSL https://raw.githubusercontent.com/muvon/octomind/master/install.sh | bash
octomind run developer:rust

Claude Code (CLI) — Command-line setup:

claude mcp add octocode -- octocode mcp --path /path/to/your/project

Claude Desktop / Cursor / Windsurf — Add to config:

{
  "mcpServers": {
    "octocode": {
      "command": "octocode",
      "args": ["mcp", "--path", "/path/to/your/project"]
    }
  }
}

Config locations:

  • Claude Desktop: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)

  • Cursor: ~/.cursor/mcp.json or Settings → MCP Servers

  • Windsurf: Settings → MCP

📖 Complete MCP Client Setup Guide — Detailed instructions for 15+ clients including VS Code (Cline/Continue), Zed, Replit, and more.

🎯 What Can You Do With It?

New developer onboarding:

You: "How does the authentication system work?"
AI: *searches and navigates* "Authentication starts in src/middleware/auth.rs which
    validates JWT tokens. It calls src/auth/jwt.rs for token verification, which uses
    the public key from config. Failed auth returns 401 via src/errors/auth_error.rs.
    Sessions are stored in Redis via src/cache/session.rs."

Code archaeology:

You: "Find all places we handle database errors"
AI: *structural search* "Found 47 error handling patterns:
    - 32 use Result<T, DbError> with proper error types
    - 15 use .unwrap() (potential issues in src/db/user.rs:23, src/db/order.rs:156)
    - Recommend adding proper error handling to those locations"

Refactoring with confidence:

You: "What depends on the PaymentProcessor trait?"
AI: *queries graph* "src/api/handlers/checkout.rs, src/workers/refund_worker.rs,
    and src/cron/billing.rs all depend on PaymentProcessor. The trait is defined
    in src/domain/payment.rs and implemented by src/infrastructure/stripe.rs
    and src/infrastructure/paypal.rs."

Code review assistance:

You: "Review this PR for security issues"
AI: *analyzes changes* "The PR adds password hashing in src/auth/hash.rs. However,
    it uses SHA256 which is fast and vulnerable to brute force. Recommend using
    bcrypt or argon2 instead. Also found 3 instances of .unwrap() that could panic
    in production."

🌐 Supported Languages

Language

Extensions

Features

Rust

.rs

Full AST parsing, pub/use detection, module structure

Python

.py

Import/class/function extraction, docstring parsing

TypeScript/JavaScript

.ts, .tsx, .js, .jsx

ES6 imports/exports, type definitions

Go

.go

Package/import analysis, struct/interface parsing

PHP

.php

Class/function extraction, namespace support

C++

.cpp, .hpp, .h

Include analysis, class/function extraction

Ruby

.rb

Class/module extraction, method definitions

Java

.java

Import analysis, class/method extraction

JSON

.json

Structure analysis, key extraction

Bash

.sh, .bash

Function and variable extraction

Markdown

.md

Document section indexing, header extraction

Plus: CSS, Lua, Svelte, and more via tree-sitter

📚 Documentation

🔒 Privacy & Security

  • 🏠 Local-first — local embedding models available on supported platforms (macOS ARM default builds); cloud providers on all platforms

  • 🔐 Secure — API keys stored locally, env vars supported

  • 🚫 Respects .gitignore — Never indexes sensitive files

  • 🛡️ MCP security — Local-only server, no external network for search

  • 📤 Cloud-safe — Embeddings process only metadata, never source code

We measure semantic search quality using a hand-annotated ground truth dataset of 254 queries (127 code + 127 docs) with precise line-range annotations. Each query has 1–3 expected results scored by relevance.

Tested on commit b1771ba with benchmark config (contextual retrieval, Voyage reranker, RaBitQ quantization).

Metric

Score

Hit@5

0.929 (118/127)

Hit@10

0.953 (121/127)

MRR

0.776

NDCG@10

0.801

Recall@5

0.902

Recall@10

0.921

Missed queries (6 of 127):

#

Query

Expected

Got (top 1)

43

how to set up MCP proxy for managing multiple repositories

doc/MCP_INTEGRATION.md:286-311

doc/MCP_INTEGRATION.md:286-4

51

what are the prerequisites before using octocode

doc/GETTING_STARTED.md:6-12

doc/CONTRIBUTING.md:7-33

59

what to do when hitting API rate limits

doc/GETTING_STARTED.md:209-216

doc/PERFORMANCE.md:304-356

75

typical performance metrics for small medium and large projects

doc/PERFORMANCE.md:4-13

doc/PERFORMANCE.md:414-14

112

how to install octocode on different operating systems

INSTALL.md:4-14

INSTALL.md:49-70

115

how to fix macOS Gatekeeper blocking the binary

INSTALL.md:199-206

INSTALL.md:198-119

Metric

Score

Hit@5

0.992 (126/127)

Hit@10

0.992 (126/127)

MRR

0.895

NDCG@10

0.906

Recall@5

0.962

Recall@10

0.974

Missed queries (1 of 127):

#

Query

Expected

Got (top 1)

105

how does the system ensure two developers get the same database path

src/storage.rs:60-83

src/mcp/proxy.rs:631-644

Metrics: Hit@k (did the answer appear?), MRR (how high?), NDCG@10 (are best results ranked first?), Recall@k (how many found?). See benchmark/ for methodology, scoring script, and the full dataset.

🤝 Community & Support

⚖️ License

Apache License 2.0 — See LICENSE for details.


Built with 🦀 Rust by Muvon in Hong Kong

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