Scaffold is a specialized RAG (Retrieval-Augmented Generation) system designed to revolutionize how development teams interact with large codebases. Born from real-world frustrations with traditional documentation and AI-assisted development, Scaffold provides the structural foundation AI agents need to effectively construct, maintain, and repair complex software projects.

Modern development teams face three critical problems:

1. Documentation Decay: Maintaining accurate and up-to-date technical documentation requires unsustainable manual effort.
2. AI Context Blindness: LLMs lack awareness of project-specific architecture and business logic, requiring inefficient manual context provisioning.
3. Knowledge Fragmentation: Critical system understanding exists only in tribal knowledge that's lost when team members leave.

Scaffold transforms your source code into a living knowledge graph stored in a graph database. This creates an intelligent context layer that:

• Captures structural relationships between code entities.
• Maintains both vector and graph representations of your codebase.
• Enables precise context injection for LLMs and AI agents.
• Supports construction, maintenance, and refactoring workflows.

Like its physical namesake, Scaffold provides the temporary support structure needed to build something great - then disappears when the work is done.

There are two primary ways to run Scaffold. Choose the one that best fits your needs.

mkdir my-scaffold-server
cd my-scaffold-server

services:
  scaffold-mcp:
    image: ghcr.io/beer-bears/scaffold:latest
    container_name: scaffold-mcp-prod
    env_file:
      - .env
    tty: true
    ports:
      - "8000:8080"
    depends_on:
      - neo4j
    volumes:
      - ./codebase:/app/codebase

  chromadb:
    image: chromadb/chroma:1.0.13
    container_name: scaffold-chromadb
    restart: unless-stopped
    volumes:
      - chroma_data:/data

  neo4j:
    image: neo4j:5
    container_name: scaffold-neo4j
    restart: unless-stopped
    environment:
      NEO4J_AUTH: "${NEO4J_USER:-neo4j}/${NEO4J_PASSWORD:-password}"
    volumes:
      - neo4j_data:/data
    ports:
      - "7474:7474"
      - "7687:7687"

volumes:
  chroma_data:
  neo4j_data:

# ChromaDB Settings
CHROMA_SERVER_HOST=chromadb
CHROMA_SERVER_PORT=8000
CHROMA_COLLECTION_NAME=scaffold_data

# Neo4j Credentials
NEO4J_USER=neo4j
NEO4J_PASSWORD=password
NEO4J_URI=bolt://neo4j:password@neo4j:7687

# Absolute path to your codebase
PROJECT_PATH=<ABSOLUTE_PATH_TO_YOUR_CODEBASE>

docker-compose up -d

git clone https://github.com/Beer-Bears/scaffold.git
cd scaffold

cp .env.example .env

# Create the directory if it doesn't exist
mkdir -p codebase

# Copy your project files into it
cp -r /path/to/your/python/project/* ./codebase/

docker-compose up --build -d

Once the containers are running (using either method), you can interact with the system.

Add the Scaffold server to your client's mcp.json file.

{
  "mcpServers": {
    "scaffold-mcp": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Access the Neo4j web UI to visually explore the graph of your codebase. Use the credentials from your .env file (default: neo4j / password). URL: http://localhost:7474/

You can also test the MCP endpoint directly using curl.

curl -N -X POST http://localhost:8000/mcp/ \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{
    "jsonrpc": "2.0",
    "id": "1",
    "method": "tools/call",
    "params": {
      "name": "get_code_entity_information",
      "arguments": {
        "entity_name": "MyClassName"
      }
    }
  }'

You also can check out mcp description and get docker cmd configuration

View Architecture Schema

View More Diagrams

Usecase Schema

Interfaces Schema

.
├── docs
│   ├── img       # Static Images
│   └── research  # Research reports
└── src
    ├── core      # RAG Context Fetching Algorithms
    ├── database  # Graph/Vector Database Logic
    ├── generator # Abstract Tree Generator
    ├── mcp       # MCP Interface
    └── parsers   # AST Parcers

• What is Retrieval-Augmented Generation (RAG)?
• GraphRAG vs. Traditional RAG: Higher Accuracy & Insight with LLM
• What is MCP? Integrate AI Agents with Databases & APIs

Scaffold is an open-source project and we welcome contributions from the community! Whether it's reporting a bug, discussing features, or submitting code, your help is valued.

To get started, please read our Contributing Guidelines for details on how to set up your development environment, run tests, and submit a pull request.

This project is licensed under the MIT License. See the LICENSE file for details.