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A vector embedding-based code semantic search tool with MCP server and multi-model integration. Can be used as a pure CLI tool. Supports Ollama for fully local embedding and reranking, enabling complete offline operation and privacy protection for your code repository

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@autodev/codebase

npm version GitHub stars License: MIT

A vector embedding-based code semantic search tool with MCP server and multi-model integration. Can be used as a pure CLI tool. Supports Ollama for fully local embedding and reranking, enabling complete offline operation and privacy protection for your code repository.

# Semantic code search - Find code by meaning, not just keywords
╭─ ~/workspace/autodev-codebase 
╰─❯ codebase search "user manage" --demo
Found 20 results in 5 files for: "user manage"

==================================================
File: "hello.js"
==================================================
< class UserManager > (L7-20)
class UserManager {
  constructor() {
    this.users = [];
  }

  addUser(user) {
    this.users.push(user);
    console.log('User added:', user.name);
  }

  getUsers() {
    return this.users;
  }
}
……

# Call graph analysis - Trace function call relationships and execution paths
╭─ ~/workspace/autodev-codebase 
╰─❯ codebase call --demo --query="app,addUser"
Connections between app, addUser:

Found 2 matching node(s):
  - demo/app:L1-29
  - demo/hello.UserManager.addUser:L12-15

Direct connections:
  - demo/app:L1-29 → demo/hello.UserManager.addUser:L12-15

Chains found:
  - demo/app:L1-29 → demo/hello.UserManager.addUser:L12-15

# Code outline with AI summaries - Understand code structure at a glance
╭─ ~/workspace/autodev-codebase 
╰─❯ codebase outline 'hello.js' --demo --summarize
# hello.js (23 lines)
└─ Defines a greeting function that logs a personalized hello message and returns a welcome string. Implements a UserManager class managing an array of users with methods to add users and retrieve the current user list. Exports both components for external use.

   2--5 | function greetUser
   └─ Implements user greeting logic by logging a personalized hello message and returning a welcome message

   7--20 | class UserManager
   └─ Manages user data with methods to add users to a list and retrieve all stored users

   12--15 | method addUser
   └─ Adds a user to the users array and logs a confirmation message with the user's name.

🚀 Features

  • 🔍 Semantic Code Search: Vector-based search using advanced embedding models
  • 🔗 Call Graph Analysis: Trace function call relationships and execution paths
  • 🌐 MCP Server: HTTP-based MCP server with SSE and stdio adapters
  • 💻 Pure CLI Tool: Standalone command-line interface without GUI dependencies
  • ⚙️ Layered Configuration: CLI, project, and global config management
  • 🎯 Advanced Path Filtering: Glob patterns with brace expansion and exclusions
  • 🌲 Tree-sitter Parsing: Support for 40+ programming languages
  • 💾 Qdrant Integration: High-performance vector database
  • 🔄 Multiple Providers: OpenAI, Ollama, Jina, Gemini, Mistral, OpenRouter, Vercel
  • 📊 Real-time Watching: Automatic index updates
  • ⚡ Batch Processing: Efficient parallel processing
  • 📝 Code Outline Extraction: Generate structured code outlines with AI summaries
  • 💨 Dependency Analysis Cache: Intelligent caching for 10-50x faster re-analysis

📦 Installation

1. Dependencies

brew install ollama ripgrep
ollama serve
ollama pull nomic-embed-text

2. Qdrant

docker run -d -p 6333:6333 -p 6334:6334 --name qdrant qdrant/qdrant

3. Install

npm install -g @autodev/codebase
codebase config --set embedderProvider=ollama,embedderModelId=nomic-embed-text

🛠️ Quick Start

# Demo mode (recommended for first-time)
# Creates a demo directory in current working directory for testing

# Index & search
codebase index --demo
codebase search "user greet" --demo

# Call graph analysis
codebase call --demo --query="app,addUser"

# MCP server
codebase index --serve --demo

📋 Commands

📝 Code Outlines

# Extract code structure (functions, classes, methods)
codebase outline "src/**/*.ts"

# Generate code structure with AI summaries
codebase outline "src/**/*.ts" --summarize

# View only file-level summaries
codebase outline "src/**/*.ts" --summarize --title

# Clear summary cache
codebase outline --clear-summarize-cache

🔗 Call Graph Analysis

# Analyze function call relationships
codebase call --query="functionA,functionB"

# Analyze specific directory
codebase call src/commands

# Export analysis results
codebase call --output=graph.json

# Open interactive graph viewer
codebase call --open

# Set analysis depth
codebase call --query="main" --depth=3

# Specify workspace path
codebase call --path=/my/project

Query Patterns:

  • Exact match: --query="functionName" or --query="ClassName.methodName"
  • Wildcards: * (any characters), ? (single character)
    • Examples: --query="get*", --query="*User*", --query="*.*.get*"
  • Single pattern: --query="main" - Shows dependency tree (what it calls, who calls it)
    • Use --depth to control tree depth (default: 10)
  • Multiple patterns: --query="main,helper" - Analyzes connections between functions
    • Connection search depth is fixed at 10 (--depth is ignored)

Supported Languages:

  • TypeScript/JavaScript (.ts, .tsx, .js, .jsx)
  • Python (.py)
  • Java (.java)
  • C/C++ (.c, .h, .cpp, .cc, .cxx, .hpp, .hxx, .c++)
  • C# (.cs)
  • Rust (.rs)
  • Go (.go)

🔍 Indexing & Search

# Index the codebase
codebase index --path=/my/project --force

# Search with filters
codebase search "error handling" --path-filters="src/**/*.ts"

# Search with custom limit and minimum score
codebase search "authentication" --limit=20 --min-score=0.7
codebase search "API" -l 30 -S 0.5

# Search in JSON format
codebase search "authentication" --json

# Clear index data
codebase index --clear-cache --path=/my/project

🌐 MCP Server

# HTTP mode (recommended)
codebase index --serve --port=3001 --path=/my/project

# Stdio adapter
codebase stdio --server-url=http://localhost:3001/mcp

⚙️ Configuration

# View config
codebase config --get
codebase config --get embedderProvider --json

# Set config
codebase config --set embedderProvider=ollama,embedderModelId=nomic-embed-text
codebase config --set --global qdrantUrl=http://localhost:6333

🚀 Advanced Features

🔍 LLM-Powered Search Reranking

Enable LLM reranking to dramatically improve search relevance:

# Enable reranking with Ollama (recommended)
codebase config --set rerankerEnabled=true,rerankerProvider=ollama,rerankerOllamaModelId=qwen3-vl:4b-instruct

# Or use OpenAI-compatible providers
codebase config --set rerankerEnabled=true,rerankerProvider=openai-compatible,rerankerOpenAiCompatibleModelId=deepseek-chat

# Search with automatic reranking
codebase search "user authentication"  # Results are automatically reranked by LLM

Benefits:

  • 🎯 Higher precision: LLM understands semantic relevance beyond vector similarity
  • 📊 Smart scoring: Results are reranked on a 0-10 scale based on query relevance
  • ⚡ Batch processing: Efficiently handles large result sets with configurable batch sizes
  • 🎛️ Threshold control: Filter results with rerankerMinScore to keep only high-quality matches

Path Filtering & Export

# Path filtering with brace expansion and exclusions
codebase search "API" --path-filters="src/**/*.ts,lib/**/*.js"
codebase search "utils" --path-filters="{src,test}/**/*.ts"

# Export results in JSON format for scripts
codebase search "auth" --json

Path Filtering & Export

# Path filtering with brace expansion and exclusions
codebase search "API" --path-filters="src/**/*.ts,lib/**/*.js"
codebase search "utils" --path-filters="{src,test}/**/*.ts"

# Export results in JSON format for scripts
codebase search "auth" --json

⚙️ Configuration

Config Layers (Priority Order)

  1. CLI Arguments - Runtime parameters (--path, --config, --log-level, --force, etc.)
  2. Project Config - ./autodev-config.json (or custom path via --config)
  3. Global Config - ~/.autodev-cache/autodev-config.json
  4. Built-in Defaults - Fallback values

Note: CLI arguments provide runtime override for paths, logging, and operational behavior. For persistent configuration (embedderProvider, API keys, search parameters), use config --set to save to config files.

Common Config Examples

Ollama:

{
  "embedderProvider": "ollama",
  "embedderModelId": "nomic-embed-text",
  "qdrantUrl": "http://localhost:6333"
}

OpenAI:

{
  "embedderProvider": "openai",
  "embedderModelId": "text-embedding-3-small",
  "embedderOpenAiApiKey": "sk-your-key",
  "qdrantUrl": "http://localhost:6333"
}

OpenAI-Compatible:

{
  "embedderProvider": "openai-compatible",
  "embedderModelId": "text-embedding-3-small",
  "embedderOpenAiCompatibleApiKey": "sk-your-key",
  "embedderOpenAiCompatibleBaseUrl": "https://api.openai.com/v1"
}

Key Configuration Options

Category Options Description
Embedding embedderProvider, embedderModelId, embedderModelDimension Provider and model settings
API Keys embedderOpenAiApiKey, embedderOpenAiCompatibleApiKey Authentication
Vector Store qdrantUrl, qdrantApiKey Qdrant connection
Search vectorSearchMinScore, vectorSearchMaxResults Search behavior
Reranker rerankerEnabled, rerankerProvider Result reranking
Summarizer summarizerProvider, summarizerLanguage, summarizerBatchSize AI summary generation

Key CLI Arguments:

  • index - Index the codebase
  • search <query> - Search the codebase (required positional argument)
  • outline <pattern> - Extract code outlines (supports glob patterns)
  • call - Analyze function call relationships and dependency graphs
  • stdio - Start stdio adapter for MCP
  • config - Manage configuration (use with --get or --set)
  • --serve - Start MCP HTTP server (use with index command)
  • --summarize - Generate AI summaries for code outlines
  • --dry-run - Preview operations before execution
  • --title - Show only file-level summaries
  • --clear-summarize-cache - Clear all summary caches
  • --path, --demo, --force - Common options
  • --limit / -l <number> - Maximum number of search results (default: from config, max 50)
  • --min-score / -S <number> - Minimum similarity score for search results (0-1, default: from config)
  • --query <patterns> - Query patterns for call graph analysis (comma-separated)
  • --output <file> - Export analysis results to JSON file
  • --open - Open interactive graph viewer
  • --depth <number> - Set analysis depth for call graphs
  • --help - Show all available options

Configuration Commands:

# View config
codebase config --get
codebase config --get --json

# Set config (saves to file)
codebase config --set embedderProvider=ollama,embedderModelId=nomic-embed-text
codebase config --set --global embedderProvider=openai,embedderOpenAiApiKey=sk-xxx

# Use custom config file
codebase --config=/path/to/config.json config --get
codebase --config=/path/to/config.json config --set embedderProvider=ollama

# Runtime override (paths, logging, etc.)
codebase index --path=/my/project --log-level=info --force

For complete configuration reference, see CONFIG.md.

🔌 MCP Integration

HTTP Streamable Mode (Recommended)

codebase index --serve --port=3001

IDE Config:

{
  "mcpServers": {
    "codebase": {
      "url": "http://localhost:3001/mcp"
    }
  }
}

Stdio Adapter

# First start the MCP server in one terminal
codebase index --serve --port=3001

# Then connect via stdio adapter in another terminal (for IDEs that require stdio)
codebase stdio --server-url=http://localhost:3001/mcp

IDE Config:

{
  "mcpServers": {
    "codebase": {
      "command": "codebase",
      "args": ["stdio", "--server-url=http://localhost:3001/mcp"]
    }
  }
}

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request or open an Issue on GitHub.

📄 License

This project is licensed under the MIT License.

🙏 Acknowledgments

This project is a fork and derivative work based on Roo Code. We've built upon their excellent foundation to create this specialized codebase analysis tool with enhanced features and MCP server capabilities.

🌟 If you find this tool helpful, please give us a star on GitHub!

Made with ❤️ for the developer community

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A vector embedding-based code semantic search tool with MCP server and multi-model integration. Can be used as a pure CLI tool. Supports Ollama for fully local embedding and reranking, enabling complete offline operation and privacy protection for your code repository

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