Beer-Bears/scaffold

The Challenge
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.
Our Solution
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.
---
Getting Started
There are two primary ways to run Scaffold. Choose the one that best fits your needs.
Option 1: Run with Pre-built Docker Image (Recommended for Users)
> This is the fastest method to get Scaffold running. It uses the official pre-built image from the GitHub Container Registry and does not require you to clone the source code repository. You only need to create two configuration files.
1. Prepare Your Project Directory
Create a new folder for your project setup.
2. Create Configuration Files
In the my-scaffold-server directory, create the following two files.
**docker-compose.yaml:**
**.env:**
3. Run the Application
Start all services using Docker Compose.
Scaffold will now start and begin analyzing the code of your codebase.
Option 2: Build from Source (For Developers)
> This method is for developers who have cloned the repository and want to build the Docker image locally. This is ideal for contributing to Scaffold or making custom modifications, or just run all containers at once in self-hosting mode.
1. Set Up The Project
First, clone the repository and navigate into the project directory.
Next, create your environment file from the example provided.
2. Add Your Codebase
Place the Python project you want to analyze into the codebase directory.
Alternatively, you can edit the .env file and set the PROJECT_PATH variable to the absolute path of your project on your host machine.
3. Run the Application
Start the entire application stack using Docker Compose. The --build flag will compile your local source code into a new Docker image.
Interact with Scaffold
Once the containers are running (using either method), you can interact with the system.
A. Configure your MCP Client (e.g., Cursor)
Add the Scaffold server to your client's mcp.json file.
B. Explore the Knowledge Graph
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/
C. Send a Direct API Request
You can also test the MCP endpoint directly using curl.