<div align="center">
<img src="https://raw.githubusercontent.com/Muvon/octocode/master/logo.svg" width="240" alt="Octocode">
### **Structural Code Intelligence for AI Agents β MCP Server + Knowledge Graph + Semantic Search**
[](https://github.com/Muvon/octocode/stargazers)
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.rust-lang.org)
[](https://github.com/Muvon/octocode/releases)
**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.
[π Quick Start](#-quick-start) β’ [π€ MCP Integration](#-mcp-server-integration) β’ [π Documentation](#-documentation) β’ [π Website](https://octocode.muvon.io)
<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>
</div>
---
## π€ 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:
- π **Semantic search** β Find code by meaning, not keywords
- πΈοΈ **Knowledge graph** β Navigate imports, calls, and dependencies
- π **Code signatures** β View structure without reading entire files
- π§ **LSP precision** β Go-to-definition, find-references, and hover docs via your language server
**Works with:** Claude Desktop β’ Cursor β’ Windsurf β’ Any MCP-compatible AI
```json
// 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: "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"
```
## π€ 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"
# GraphRAG reveals the full dependency chain
octocode graphrag get-relationships --node_id src/middleware/auth.rs
Outgoing:
imports β jwt (src/auth/jwt.rs): token validation logic
calls β user_store (src/db/user_store.rs): user lookup by token
Incoming:
imports β 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** β so AI tools can *navigate* your project architecture, not just search it.
## π¬ How It Works
```
Source Code β Tree-sitter AST β Symbols & Relationships β Knowledge Graph
β
Embeddings + Hybrid Search + Reranking β MCP Server
```
1. **AST Parsing** β tree-sitter extracts real code symbols (functions, classes, imports), not arbitrary text chunks
2. **Knowledge Graph** β GraphRAG maps relationships between files: `imports`, `calls`, `implements`, `extends`, `configures`, and 9 more types β each with importance weighting
3. **Hybrid Search** β semantic similarity + BM25 full-text search + reranking β not just vector embeddings
4. **MCP Server** β exposes `semantic_search`, `view_signatures`, and `graphrag` tools to any MCP-compatible client
## β¨ What Makes It Different
| **Indexes** | Text chunks | External library docs | Your codebase structure (AST) |
| **Understands** | Similar text | API specs & usage | Functions, imports, dependencies |
| **Cross-file** | No | No | Yes β 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.
## π Retrieval Quality
Octocode ships a **reproducible retrieval benchmark** ([`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 β `jina-embeddings-v2-base-code` via fastembed, **no reranker** β so they are a floor, not a ceiling:
| Dense vector only | 0.598 | 0.717 | 0.485 | 0.528 | 0.671 |
| Hybrid, default RRF weights (0.7/0.3) | 0.598 | 0.717 | 0.485 | 0.528 | 0.671 |
| **Hybrid, keyword-tuned (0.3/0.7)** | **0.732** | **0.835** | **0.572** | **0.620** | **0.807** |
Tilting RRF fusion toward the BM25/keyword signal β which carries disproportionate weight for code's exact identifiers β 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`](benchmark/RESULTS.md)): a **generic** local cross-encoder reranker (`bge-reranker-base`) actually **regressed** results (Hit@5 0.732 β 0.598) β code retrieval needs a _code-aware_ reranker (e.g. `voyage:rerank-2.5`), not an off-the-shelf one.
```bash
git worktree add /tmp/corpus b1771ba # pin the corpus to the ground-truth commit
CORPUS=/tmp/corpus python3 benchmark/run_matrix.py
```
See [benchmark/README.md](benchmark/README.md) for methodology and metric definitions.
## π Quick Start
### 1. Install
```bash
# Universal installer (Linux, macOS, Windows)
# macOS with Homebrew
brew install muvon/tap/octocode
```
<details>
<summary><strong>Other installation methods</strong></summary>
```bash
# 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](INSTALL.md) for platform-specific instructions.
</details>
### 2. Set Up API Keys
```bash
# 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](https://www.voyageai.com/) (free tier available)
<details>
<summary><strong>Other embedding providers</strong></summary>
Octocode supports multiple embedding providers:
```bash
# 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](doc/API_KEYS.md) for all supported providers.
</details>
### 3. Index Your Codebase
```bash
cd /your/project
octocode index
# β Indexed 12,847 blocks across 342 files
```
### 4. Search Your Code
```bash
# 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):
```json
{
"mcpServers": {
"octocode": {
"command": "octocode",
"args": ["mcp", "--path", "/your/project"]
}
}
}
```
Done! Your AI assistant now understands your codebase structure.
## π 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 β give your AI assistant direct access to search and navigate your code.
### Available Tools
| `semantic_search` | Find code by meaning β "authentication flow", "error handling", "database queries" |
| `view_signatures` | View file structure β function signatures, class definitions, imports |
| `graphrag` | Query relationships β "what calls this function?", "what does this module import?" |
| `structural_search` | AST pattern matching β find `.unwrap()` calls, `new` instantiations, specific patterns |
| `lsp_goto_definition` | Jump to a symbol's definition (requires `--with-lsp`) |
| `lsp_find_references` | Find all usages of a symbol across the workspace (requires `--with-lsp`) |
| `lsp_hover` | Type info and documentation for a symbol (requires `--with-lsp`) |
| `lsp_document_symbols` / `lsp_workspace_symbols` / `lsp_completion` | File symbols, workspace-wide symbol search, completions (requires `--with-lsp`) |
Enable the LSP tools by starting the server with your language server:
```bash
octocode mcp --path /your/project --with-lsp="rust-analyzer"
```
### 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)** β Zero setup, Octocode pre-configured:
```bash
```
**Claude Code (CLI)** β Command-line setup:
```bash
claude mcp add octocode -- octocode mcp --path /path/to/your/project
```
**Claude Desktop / Cursor / Windsurf** β Add to config:
```json
{
"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 β MCP Servers
- Windsurf: Settings β MCP
π **[Complete MCP Client Setup Guide](doc/MCP_CLIENTS.md)** β Detailed instructions for 15+ clients including VS Code (Cline/Continue), Zed, Replit, and more.
## π― 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."
```
## π Supported Languages
16 languages with full tree-sitter AST parsing:
| **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 |
| **Swift** | `.swift` | Class/struct/protocol extraction, import analysis |
| **Svelte** | `.svelte` | Component structure, script/style block extraction |
| **Lua** | `.lua` | Function and table extraction |
| **CSS** | `.css` | Rule and selector extraction |
| **JSON** | `.json` | Structure analysis, key extraction |
| **Bash** | `.sh`, `.bash` | Function and variable extraction |
| **Markdown** | `.md` | Document section indexing, header extraction |
## π Documentation
- **[Getting Started](doc/GETTING_STARTED.md)** β First steps and basic workflow
- **[Installation Guide](INSTALL.md)** β Detailed methods and building from source
- **[MCP Client Setup](doc/MCP_CLIENTS.md)** β Connect to Claude, Cursor, Windsurf, and 15+ clients
- **[MCP Integration](doc/MCP_INTEGRATION.md)** β MCP server details and advanced configuration
- **[Commands Reference](doc/COMMANDS.md)** β Complete CLI reference
- **[Configuration](doc/CONFIGURATION.md)** β Templates and customization
- **[API Keys](doc/API_KEYS.md)** β Provider setup guide
- **[Architecture](doc/ARCHITECTURE.md)** β How it works under the hood
- **[Contributing](doc/CONTRIBUTING.md)** β Development setup
## π Privacy & Security
- **π Local-first** β local embedding models available on supported platforms (macOS ARM default builds); cloud providers on all platforms
- **π Secure** β API keys stored locally, env vars supported
- **π« Respects .gitignore** β Never indexes sensitive files
- **π‘οΈ MCP security** β Local-only server, no external network for search
- **π€ Cloud-safe** β Embeddings process only metadata, never source code
<details>
<summary><strong>π 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 1β3 expected results scored by relevance.
Tested on commit [`b1771ba`](https://github.com/Muvon/octocode/commit/b1771ba) with [benchmark config](benchmark/config.toml) (contextual retrieval, Voyage reranker, RaBitQ quantization).
<details>
<summary><strong>Documentation search</strong> (<code>--mode docs</code>) β Hit@10: 0.953, MRR: 0.776</summary>
| 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):
| 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` |
</details>
<details>
<summary><strong>Code search</strong> (<code>--mode code</code>) β Hit@10: 0.992, MRR: 0.895</summary>
| 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):
| 105 | how does the system ensure two developers get the same database path | `src/storage.rs:60-83` | `src/mcp/proxy.rs:631-644` |
</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/](benchmark/) for methodology, scoring script, and the full dataset.
</details>
## π€ Community & Support
- β **Star us on GitHub** β It really helps!
- π [Report Issues](https://github.com/Muvon/octocode/issues)
- π¬ [Discussions](https://github.com/Muvon/octocode/discussions)
- π§ [opensource@muvon.io](mailto:opensource@muvon.io)
- π [muvon.io](https://muvon.io)
## βοΈ License
Apache License 2.0 β See [LICENSE](LICENSE) for details.
---
<div align="center">
**Built with π¦ Rust by [Muvon](https://muvon.io) in Hong Kong**
[β Star](https://github.com/Muvon/octocode) β’ [π΄ Fork](https://github.com/Muvon/octocode/fork) β’ [π£ Share](https://twitter.com/intent/tweet?text=Octocode%20-%20AI-powered%20code%20intelligence%20with%20built-in%20MCP%20server&url=https://github.com/Muvon/octocode)
</div>
mcp-name: io.github.Muvon/octocode