nexus-dev 1.0.0

MCP server for persistent AI coding assistant memory with local RAG

Nexus-Dev

Persistent Memory for AI Coding Agents

Nexus-Dev is an open-source MCP (Model Context Protocol) server that provides a local RAG (Retrieval-Augmented Generation) system for AI coding assistants like GitHub Copilot, Cursor, and Windsurf. It learns from your codebase and mistakes, enabling cross-project knowledge sharing.

Features

• 🧠 Persistent Memory: Index your code and documentation for semantic search
• 📚 Lesson Learning: Record problems and solutions that the AI can recall later
• 🌐 Multi-Language Support: Python, JavaScript/TypeScript, Java (extensible via tree-sitter)
• 📖 Documentation Indexing: Parse and index Markdown/RST documentation
• 🔄 Cross-Project Learning: Share knowledge across all your projects
• 🏠 Local-First: All data stays on your machine with LanceDB

Installation

# Using pip
pip install nexus-dev

# Using uv (recommended)
uv pip install nexus-dev

Quick Start

1. Initialize a Project

cd your-project
nexus-init --project-name "my-project" --embedding-provider openai

This creates:

• nexus_config.json - Project configuration
• .nexus/lessons/ - Directory for learned lessons

2. Set Your API Key (OpenAI only)

The CLI commands require the API key in your environment:

export OPENAI_API_KEY="sk-..."

Tip: Add this to your shell profile (~/.zshrc, ~/.bashrc) so it's always available.

If using Ollama, no API key is needed—just ensure Ollama is running locally.

3. Index Your Code

# Index directories recursively (recommended)
nexus-index src/ -r

# Index multiple directories
nexus-index src/ docs/ -r

# Index specific files (no -r needed)
nexus-index main.py utils.py

Note: The -r flag is required to recursively index subdirectories. Without it, only files directly inside the given folder are indexed.

4. Configure Your AI Agent

Add to your MCP client configuration (e.g., Claude Desktop):

{
  "mcpServers": {
    "nexus-dev": {
      "command": "nexus-dev",
      "args": []
    }
  }
}

5. Verify Your Setup

Check indexed content via CLI:

nexus-status

Test in your AI agent — copy and paste this prompt:

Search the Nexus-Dev knowledge base for functions related to "embeddings" 
and show me the project statistics.

If the AI uses the search_code or get_project_context tools and returns results, your setup is complete! 🎉

MCP Tools

Nexus-Dev exposes 7 tools to AI agents:

Search Tools

Tool	Description
search_knowledge	Search all content (code, docs, lessons) with optional content_type filter
search_code	Search specifically in indexed code (functions, classes, methods)
search_docs	Search specifically in documentation (Markdown, RST, text)
search_lessons	Search in recorded lessons (problems & solutions)

Indexing Tools

Tool	Description
index_file	Index a file into the knowledge base
record_lesson	Store a problem/solution pair for future reference
get_project_context	Get project statistics and recent lessons

Configuration

nexus_config.json example:

{
  "project_id": "550e8400-e29b-41d4-a716-446655440000",
  "project_name": "my-project",
  "embedding_provider": "openai",
  "embedding_model": "text-embedding-3-small",
  "docs_folders": ["docs/", "README.md"],
  "include_patterns": ["**/*.py", "**/*.js", "**/*.java"],
  "exclude_patterns": ["**/node_modules/**", "**/__pycache__/**"]
}

Embedding Providers

Provider	Model	Dimensions	Notes
OpenAI	text-embedding-3-small	1536	Requires OPENAI_API_KEY
Ollama	nomic-embed-text	768	Local, no API key needed

⚠️ Warning: Embeddings are NOT portable between providers. Changing providers requires re-indexing all documents.

Optional: Pre-commit Hook

Install automatic indexing on commits:

nexus-init --project-name "my-project" --install-hook

Or manually add to .git/hooks/pre-commit:

#!/bin/bash
MODIFIED=$(git diff --cached --name-only --diff-filter=ACM | grep -E '\.(py|js|ts|java)$')
if [ -n "$MODIFIED" ]; then
    nexus-index $MODIFIED
fi

Configuring AI Agents

To maximize Nexus-Dev's value, configure your AI coding assistant to use its tools automatically.

Add AGENTS.md to Your Project

Copy our template to your project:

cp path/to/nexus-dev/docs/AGENTS_TEMPLATE.md your-project/AGENTS.md

This instructs AI agents to:

• Search first before implementing features
• Record lessons after solving bugs
• Use get_project_context() at session start

Add Workflow Files (Optional)

cp -r path/to/nexus-dev/.agent/workflows your-project/.agent/

This adds slash commands: /start-session, /search-first, /record-lesson, /index-code

📖 See docs/configuring-agents.md for detailed setup instructions.

Architecture

flowchart TB
    subgraph Agent["🤖 AI Agent"]
        direction TB
        Cursor["Cursor / Copilot / Windsurf"]
    end

    subgraph MCP["📡 Nexus-Dev MCP Server"]
        direction TB
        
        subgraph Tools["MCP Tools"]
            search_knowledge["search_knowledge"]
            search_code["search_code"]
            search_docs["search_docs"]
            search_lessons["search_lessons"]
            index_file["index_file"]
            record_lesson["record_lesson"]
        end
        
        subgraph Chunkers["🔧 Chunker Registry"]
            Python["Python"]
            JavaScript["JavaScript/TypeScript"]
            Java["Java"]
            Docs["Documentation"]
        end
        
        subgraph Embeddings["🧮 Embedding Layer"]
            OpenAI["OpenAI API"]
            Ollama["Ollama (Local)"]
        end
        
        subgraph DB["💾 LanceDB"]
            Vectors["Vector Storage"]
            Metadata["Metadata Index"]
        end
    end

    Agent -->|"stdio"| Tools
    Tools --> Chunkers
    Chunkers --> Embeddings
    Embeddings --> DB

Data Flow

sequenceDiagram
    participant AI as AI Agent
    participant MCP as Nexus-Dev
    participant Embed as Embeddings
    participant DB as LanceDB

    Note over AI,DB: Indexing Flow
    AI->>MCP: index_file(path)
    MCP->>MCP: Parse with Chunker
    MCP->>Embed: Generate embeddings
    Embed-->>MCP: Vectors
    MCP->>DB: Store chunks + vectors
    DB-->>MCP: OK
    MCP-->>AI: ✅ Indexed

    Note over AI,DB: Search Flow
    AI->>MCP: search_knowledge(query)
    MCP->>Embed: Embed query
    Embed-->>MCP: Query vector
    MCP->>DB: Vector similarity search
    DB-->>MCP: Results
    MCP-->>AI: Formatted results

Development Setup

Since Nexus-Dev is not yet published to PyPI/Docker Hub, developers must build from source.

Option 1: Local Python Installation (Recommended for Development)

# Clone repository
git clone https://github.com/mmornati/nexus-dev.git
cd nexus-dev

# Option A: Use the Makefile (handles pyenv + venv)
make setup
source .venv/bin/activate

# Option B: Manual setup
pyenv install 3.13       # or use your preferred Python 3.13+ manager
python3 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"  # Editable install with dev dependencies

After installation, CLI commands are available:

nexus-init --help        # Initialize a project
nexus-index --help       # Index files
nexus-dev                # Run MCP server

Option 2: Docker Build

# Build the image
make docker-build
# or: docker build -t nexus-dev:latest .

# Run with volume mounts
docker run -it --rm \
    -v /path/to/your-project:/workspace:ro \
    -v nexus-dev-data:/data/nexus-dev \
    -e OPENAI_API_KEY=$OPENAI_API_KEY \
    nexus-dev:latest

# Or use Makefile shortcuts
make docker-run          # Run container
make docker-logs         # View logs
make docker-stop         # Stop container

Makefile Commands

Command	Description
make setup	Full dev environment setup (pyenv + venv + deps)
make install-dev	Install package with dev dependencies
make lint	Run ruff linter
make format	Format code + auto-fix lint issues
make check	Run all CI checks (lint + format + type-check)
make test	Run tests
make test-cov	Run tests with coverage report
make docker-build	Build Docker image
make docker-run	Run Docker container
make help	Show all available commands

MCP Configuration (Development Mode)

Configure your AI agent to use the locally-built server. This single configuration works for ALL your indexed projects!

For Claude Desktop / Cursor / Windsurf:

{
  "mcpServers": {
    "nexus-dev": {
      "command": "/path/to/nexus-dev/.venv/bin/python",
      "args": ["-m", "nexus_dev.server"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

How it works: The server now defaults to searching all indexed projects when no specific project context is active. You don't need to configure cwd or create separate MCP entries for each project.

Tip: If OPENAI_API_KEY is already in your shell profile (.zshrc, .bashrc), some clients inherit it automatically. Check your client's documentation.

Using Docker:

The /workspace mount is the server's working directory. It looks for nexus_config.json and .nexus/lessons/ there. Mount your project (or parent directory) to /workspace:

{
  "mcpServers": {
    "nexus-dev": {
      "command": "docker",
      "args": [
        "run", "-i", "--rm",
        "-v", "/path/to/project:/workspace:ro",
        "-v", "nexus-dev-data:/data/nexus-dev",
        "-e", "OPENAI_API_KEY",
        "nexus-dev:latest"
      ]
    }
  }
}

Multi-Project Setup:

For multiple projects, you have two options:

1. Mount parent directory containing all projects:

   "-v", "/Users/you/Projects:/workspace:ro"
   

   Then index paths like /workspace/project-a/src/, /workspace/project-b/src/. Each project needs its own nexus_config.json with a unique project_id.

2. Use local Python install (recommended): MCP clients automatically set the working directory to the project root, so no path configuration is needed.

Running Tests

make test              # Run all tests
make test-cov          # Run with coverage report
pytest tests/unit/ -v  # Run specific test directory

Adding Language Support

See CONTRIBUTING.md for instructions on adding new language chunkers.

License

MIT License - see LICENSE for details.