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

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
• 🧭 LSP precision — Go-to-definition, find-references, and hover docs via your language server

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

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: "Find every call site of this function"
AI: *uses LSP find-references* "process_payment() is called from 4 places:
    checkout.rs:87, refund.rs:134, billing.rs:56, and tests/payment_test.rs:23"

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: 0.9234

# The GraphRAG CLI queries the optional persisted graph
octocode config --graphrag-enabled true
octocode index
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 build a live graph of files, symbols, imports, calls, inheritance, and implementations. The MCP graphrag tool builds this graph lazily from the current source tree, without an index, embeddings, or an LLM. Optional indexed GraphRAG adds semantic file discovery, descriptions, and broader architectural relationships.

Current Source → Tree-sitter AST → Live Symbol Graph ──────────────→ MCP `graphrag`
                                           ↑                              ↑
Indexed Code → Embeddings + Optional LLM → Persisted File Enrichment ─────┘

1. Live AST Graph — tree-sitter extracts file and symbol nodes plus deterministic contains, imports, calls, extends, and implements relationships directly from current source
2. Always-on Graph Navigation — MCP graph lookup, relationship traversal, path finding, and overview work with [graphrag].enabled = false
3. Optional Enrichment — enabling indexed GraphRAG overlays semantic file matches, LLM descriptions, and broader file-level architectural relationships; symbols are never embedded or LLM-generated
4. Hybrid Search — semantic similarity + BM25 full-text search + reranking handles meaning-based code retrieval separately
5. MCP Server — exposes semantic_search, view_signatures, graphrag, and structural_search to any MCP-compatible client

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.

Octocode ships a reproducible retrieval benchmark (benchmark/): 127 curated code-search queries with line-range ground truth, run against octocode's own source (pinned at b1771ba so annotations never drift). The numbers below use a fully local, no-API-key stack — jina-embeddings-v2-base-code via fastembed, no reranker — so they are a floor, not a ceiling:

Tilting RRF fusion toward the BM25/keyword signal — which carries disproportionate weight for code's exact identifiers — lifts Hit@5 by +22% and Recall@10 by +20% at zero added cost.

The benchmark also flags what doesn't help here (full 6-variant matrix in benchmark/RESULTS.md): a generic local cross-encoder reranker (bge-reranker-base) actually regressed results (Hit@5 0.732 → 0.598) — code retrieval needs a code-aware reranker (e.g. voyage:rerank-2.5), not an off-the-shelf one.

git clone https://github.com/Muvon/octocode && cd octocode
git worktree add /tmp/corpus b1771ba   # pin the corpus to the ground-truth commit
CORPUS=/tmp/corpus python3 benchmark/run_matrix.py   # set OCTO_BIN to use a custom binary

See benchmark/README.md for methodology and metric definitions.

# 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

# From crates.io
cargo install octocode

# Or from source (latest)
cargo install --git https://github.com/Muvon/octocode

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

# Required: Embedding provider (Voyage AI has a free tier)
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)

# 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"

cd /your/project
octocode index
# → ✓ Indexing complete! 342 of 342 files processed (342 new, 0 unchanged)

# 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

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.

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.

Enable the LSP tools by starting the server with your language server:

octocode mcp --path /your/project --with-lsp="rust-analyzer"

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"

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.

16 languages with full tree-sitter AST parsing:

• 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

• 🏠 Local-first — fully local embedding via fastembed (no API key required); cloud providers optional
• 🔐 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 — cloud providers receive only the code chunks being embedded; use local models for fully offline indexing

• ⭐ Star us on GitHub — It really helps!
• 🐛 Report Issues
• 💬 Discussions
• 📧 opensource@muvon.io
• 🌐 muvon.io

Apache License 2.0 — See LICENSE for details.