Octocode— agent skill

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<img src="https://raw.githubusercontent.com/Muvon/octocode/master/logo.svg" width="240" alt="Octocode">

Structural Code Intelligence for AI Agents — MCP Server + Knowledge Graph + Semantic Search

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

<a href="https://glama.ai/mcp/servers/Muvon/octocode"> <img width="300" src="https://glama.ai/mcp/servers/Muvon/octocode/badge" alt="Octocode MCP server" /> </a>

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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

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

<details> <summary><strong>Other installation methods</strong></summary>

# 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. </details>

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)

<details> <summary><strong>Other embedding providers</strong></summary>

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. </details>

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

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](doc/MCP_CLIENTS.md) — 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

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

📚 Documentation

• [Getting Started](doc/GETTING_STARTED.md) — First steps and basic workflow
• [Installation Guide](INSTALL.md) — Detailed methods and building from source
• [MCP Client Setup](doc/MCP_CLIENTS.md) — Connect to Claude, Cursor, Windsurf, and 15+ clients
• [MCP Integration](doc/MCP_INTEGRATION.md) — MCP server details and advanced configuration
• [Commands Reference](doc/COMMANDS.md) — Complete CLI reference
• [Configuration](doc/CONFIGURATION.md) — Templates and customization
• [API Keys](doc/API_KEYS.md) — Provider setup guide
• [Architecture](doc/ARCHITECTURE.md) — How it works under the hood
• [Contributing](doc/CONTRIBUTING.md) — 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

<details> <summary><strong>📊 Retrieval Quality Benchmark</strong></summary>

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).

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

Missed queries (6 of 127):

</details>

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

Missed queries (1 of 127):

</details>

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.

</details>

🤝 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](https://muvon.io) in Hong Kong

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