octocode

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🤖 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

<strong>Other installation methods</strong>

# 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)

<strong>Other embedding providers</strong>

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, .cc, .cxx, .c++, .c, .h, .hpp, .hxx, .cppm, .ixx, .mxx, .ccm, .cxxm	Include analysis, class/function extraction, C++20 module support
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

• Getting Started — First steps and basic workflow
• Installation Guide — Detailed methods and building from source
• MCP Client Setup — Connect to Claude, Cursor, Windsurf, and 15+ clients
• MCP Integration — MCP server details and advanced configuration
• Commands Reference — Complete CLI reference
• Configuration — Templates and customization
• API Keys — Provider setup guide
• Architecture — How it works under the hood
• Contributing — Development setup

🔒 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

<strong>📊 Retrieval Quality Benchmark</strong>

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).

<strong>Documentation search</strong> (<code>--mode docs</code>) — Hit@10: 0.953, MRR: 0.776

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

<strong>Code search</strong> (<code>--mode code</code>) — Hit@10: 0.992, MRR: 0.895

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

• ⭐ Star us on GitHub — It really helps!
• 🐛 Report Issues
• 💬 Discussions
• 📧 [email protected]
• 🌐 muvon.io

⚖️ License

Apache License 2.0 — See LICENSE for details.

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Built with 🦀 Rust by Muvon in Hong Kong

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