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

🤖 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
• 🧭 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"

🤔 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: 0.9234

# 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.

🔬 How It Works

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

✨ 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.

📊 Retrieval Quality

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.

🚀 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

# 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

See Installation Guide for platform-specific instructions.

2. Set Up API Keys

# Optional: embedding provider (defaults to local FastEmbed — no key needed)
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
# → ✓ Indexing complete! 342 of 342 files processed (342 new, 0 unchanged)

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" --language 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 s