🧠 Context Machine – Dynamic Data-Driven Platform

Context Machine is a revolutionary dynamic, data-driven platform where the entire user interface configures itself from backend data. Unlike traditional applications with hardcoded routes and components, Context Machine builds everything at runtime from database configurations.

🎯 Core Concept

No Hardcoded Frontend – Pages, navigation, widgets, and layouts are all generated dynamically from backend data. The system uses a sophisticated multi-tenant architecture with subscription-based access control and a message-driven communication protocol.

Key Architecture Components

• Vue 3 Frontend – Completely dynamic UI that builds itself from JSON manifests
• Flask API Gateway – JWT authentication with unified message routing to specialized services
• Widget-Based UI – Modular, themeable components loaded at runtime
• Multi-Tenant Database – SuperAdmin → Partner → Tenant → Project hierarchy
• Real-Time Updates – WebSocket-based live synchronization
• Message Protocol – Unified communication format across all services
• Code Analysis Engine – Multi-language AST parsing with graph visualization

What Makes It Special

🔄 Runtime Configuration – Everything from routes to UI components is loaded from the database
🎨 Dynamic Theming – CSS variables and themes applied at runtime
📦 Widget Ecosystem – Modular components with manifest-based loading
🏢 Multi-Tenant Ready – Built-in subscription and permission management
⚡ Real-Time Sync – Live updates across all connected clients
🔐 JWT Security – Secure, stateless authentication with role-based access
🔍 Code Intelligence – AST-based code analysis with graph database storage

🏗️ System Architecture

The platform consists of core services that power the dynamic system, plus additional tools for enhanced functionality:

Core Platform Services

• Backend Service (Port 3006) – Main API Gateway with JWT auth and message routing
• Frontend Service (Port 5173/8080) – Vue 3 SPA with dynamic UI generation
• WebSocket Service (Port 3010) – Real-time event distribution
• PostgreSQL – Multi-tenant database with complete schema

Code Analysis & Intelligence Services

• Analyzer Service (Port 3002) – Multi-language source code analyzer with AST parsing and recursive tree analysis
• Neo4j Service (Port 3001) – Graph database for storing code relationships and structures
• 22 Domain-Specific Analyzers – Agricultural, Business, Coding, Communication, Cultural, Digital, Educational, Energy, Environmental, Financial, Legal, Manufacturing, Maritime, Medical, Quality, Scientific, Security, Social, Spatial, Technical, Temporal, Transportation
• MCP Service (Port 3003) – Model Context Protocol integration for AI-powered analysis

Additional Tools (Bonus Features)

• Ollama + OpenWebUI – Local LLM runtime for AI-powered features
• n8n – Workflow automation engine
• Gitea – Local Git hosting and repository management
• MinIO – S3-compatible object storage
• RabbitMQ – Message broker for event routing

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🚀 Quick Start

1. Create .env.local

Example configuration:

# API Authentication
API_KEY=dev-key-123 

# MinIO
MINIO_ROOT_USER=minioadmin
MINIO_ROOT_PASSWORD=minioadmin
MINIO_BUCKET=incoming

# RabbitMQ
RABBITMQ_DEFAULT_USER=admin
RABBITMQ_DEFAULT_PASS=admin123
RABBITMQ_VHOST=/
RABBITMQ_EXCHANGE=file-events
RABBITMQ_QUEUE=file-processing
RABBITMQ_ROUTING_KEY=file.put

# n8n
N8N_BASIC_AUTH_ACTIVE=true
N8N_BASIC_AUTH_USER=admin
N8N_BASIC_AUTH_PASSWORD=admin123
N8N_ENCRYPTION_KEY=supersecretkey123
N8N_PORT=5678
N8N_ENFORCE_SETTINGS_FILE_PERMISSIONS=false
N8N_RUNNERS_ENABLED=true
N8N_BOOTSTRAP_EMAIL=foo@example.com
N8N_BOOTSTRAP_FIRSTNAME=bar
N8N_BOOTSTRAP_LASTNAME=foo
N8N_BOOTSTRAP_PASSWORD=A1234567
N8N_BOOTSTRAP_ROLE=global:owner

# Neo4j
NEO4J_AUTH=neo4j/test12345
DB_SQLITE_POOL_SIZE=2

# Analyzer ↔ Neo4j connection
SERVICE_NEO4J_URI=bolt://context-machine-neo4j:7687
SERVICE_NEO4J_AUTH=neo4j/test12345

# WebSocket Service
WS_HOST=0.0.0.0
WS_PORT=3010

# Gitea (Git service)
GITEA_HTTP_PORT=3005
GITEA_ADMIN_USER=gitea-admin
GITEA_ADMIN_PASSWORD=admin123
GITEA_ADMIN_EMAIL=admin@example.com

# Ollama + OpenWebUI
OLLAMA_MODEL=codellama:7b
OPENWEBUI_PORT=8080

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2. Start the infrastructure

make up

This will:

• Build all service containers
• Initialize MinIO, RabbitMQ, n8n, Neo4j, Gitea, Ollama, and OpenWebUI
• Automatically create users, queues, buckets, and tokens
• Pull and persist the configured Ollama model (OLLAMA_MODEL)

When setup completes, you’ll see a summary like:

🚀 Context Machine is up and running

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3. Analyze Your Code

Create a project folder and add your source code:

mkdir project
# Copy your source code into the project folder

Start the code analysis:

curl -X POST http://localhost:3002/api/analyze \
  -H "Content-Type: application/json" \
  -H "X-API-Key: dev-key-123"

The analyzer will:

• Recursively scan your project directory for all supported file types
• Parse AST (Abstract Syntax Trees) for each source file
• Create graph nodes in Neo4j representing files, folders, and code structures
• Stream real-time progress via WebSocket (1% increments)
• Generate visualizations of your codebase structure

Supported Languages: JavaScript, TypeScript, Python, Java, C/C++, Go, Rust, PHP, Ruby, and more

Real-time Progress Updates:

{"percent": 1}   // Starting analysis
{"percent": 50}  // Halfway through
{"percent": 100} // Analysis complete

Query Your Code Graph:

// Find all Python files
MATCH (f:File) WHERE f.extension = '.py' RETURN f

// Analyze project structure  
MATCH (folder:Folder)-[:CONTAINS]->(file:File) 
RETURN folder.name, count(file) as file_count

Everything runs locally – no code leaves your machine.

🔍 Code Analysis Integration

The code analysis system seamlessly integrates with the dynamic platform:

Dynamic Widgets for Code Visualization:

• Code structure widgets automatically appear in the frontend
• Interactive graph visualizations load as dynamic components
• Real-time analysis progress shown via WebSocket updates

Multi-Tenant Code Projects:

• Each tenant can have multiple code analysis projects
• Permission-based access to different codebases
• Subscription-based limits on analysis scope

AI-Powered Insights:

• Local LLM integration via Ollama for code understanding
• Context-aware code suggestions and documentation
• Privacy-first approach - all AI runs locally

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4. Access All Services

Core Platform:

Service	URL	Credentials
Frontend	http://localhost:5173 (Dev) / http://localhost:8080 (Prod)	admin / admin123
Backend API	http://localhost:3006/apidocs	JWT Token
WebSocket	ws://localhost:3010	JWT Token

Code Analysis:

Service	URL	Credentials
Analyzer Service API	http://localhost:3002/apidocs	Header: X-API-Key: dev-key-123
Neo4j Browser	http://localhost:7474	neo4j / test12345
Neo4j Service API	http://localhost:3001/apidocs	Header: X-API-Key: dev-key-123
WebSocket Progress	ws://localhost:3010/progress?api_key=dev-key-123	—

Additional Tools:

Service	URL	Credentials
Ollama	http://localhost:11434	Model: codellama:7b
OpenWebUI	http://localhost:8080	Chat with your local model
n8n	http://localhost:5678	admin / admin123
Gitea	http://localhost:3005	gitea-admin / admin123
MinIO	http://localhost:9001	minioadmin / minioadmin
RabbitMQ	http://localhost:15672	admin / admin123

The Gitea API token (used for integrations) is saved at:

infra/gitea/admin_token.txt

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5. Stop or Reset

Stop all services:

make down

Reset everything (delete all volumes & data):

make reset

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🧩 Scripts

Script	Purpose
setup-minio.sh	Creates MinIO buckets if missing
setup-rabbitmq.sh	Sets up vhost, users, exchanges, queues, bindings
setup-minio-event.sh	Configures AMQP notifications for MinIO
setup-n8n.sh	Bootstraps n8n and imports credentials
setup-gitea.sh	Ensures Gitea admin exists & generates API token
setup-ollama.sh	Starts Ollama container, pulls & persists model
container-utils.sh	Builds custom service containers
messages.sh	Colorized logging utilities
progress.sh	Displays progress bars during waits

All scripts live under infra/scripts/utils/.

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🧰 Useful Commands

Logs

docker logs context-machine-minio
docker logs context-machine-rabbitmq
docker logs context-machine-n8n
docker logs context-machine-neo4j
docker logs context-machine-neo4j-service
docker logs context-machine-analyzer-service
docker logs context-machine-websocket-service
docker logs context-machine-gitea
docker logs context-machine-ollama
docker logs context-machine-openwebui

Cleanup

docker rm -f $(docker ps -aq --filter name=context-machine)
docker volume prune -f
docker network prune -f

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🧠 Notes

• Scripts are idempotent — safe to re-run anytime
• .env.local changes take effect on next make up
• Gitea now auto-provisions a token for API automation
• Ollama models persist under infra/ollama/ and are only downloaded once
• All services run locally; nothing is sent to external servers

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🧼 Troubleshooting

Error:
failed to bind port 0.0.0.0:11434
→ Ollama is already running locally. Either stop the host process or change the container port.

Progress bar not updating?
→ Check WebSocket service and API key. Test manually:

echo '{"api_key":"dev-key-123","percent":42}' | nc localhost 3011

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📜 License

Business Source License 1.1 (BUSL 1.1)
Copyright (c) 2025 Jochen Schultz

Licensed under the Business Source License 1.1 (the “License”).
Full text: https://mariadb.com/bsl11/

Terms:

• Internal & commercial use allowed for orgs ≤ 50 people
• Hosted/SaaS usage prohibited
• Redistribution/resale prohibited

• 50 employees → not permitted

Change Date: October 4, 2028
Automatically becomes Apache License 2.0

✅ Internal & small commercial use OK
🚫 Large-scale/hosted use forbidden
🕒 Open Source in 2028