Advanced Multi-Agent Orchestration System with ReAct, MCP, and Agentic RAG

Omni-Agent Hub is a production-ready, enterprise-grade AI agent orchestration platform that implements 6 advanced agentic patterns with comprehensive multi-service architecture. Built for scalability, reliability, and real-time performance with full SOC2 compliance readiness.

1. 🧠 ReAct Orchestration - Advanced reasoning and acting with adaptive learning
2. 🔧 CodeAct Runner - Secure code execution in containerized environments
3. 🛠️ MCP-based ToolHub - Model Context Protocol tool integration
4. 🔍 Self-Reflection - Continuous performance optimization and learning
5. 🤝 Multi-Agent Workflows - Collaborative agent coordination
6. 📚 Agentic RAG - Intelligent retrieval-augmented generation with Milvus

• 🔄 Real-time Event Streaming with Apache Kafka
• 📁 Intelligent File Management with MinIO object storage
• 🧠 Context-Aware Memory System with Redis caching
• 📊 Advanced Analytics Dashboard with real-time metrics
• 🔍 Vector-based Knowledge Search with Milvus database
• 🌐 WebSocket Support for real-time chat and monitoring
• 🔧 Comprehensive Health Monitoring with automated checks
• 📈 Adaptive Learning Engine with session-based tracking

Goal: Build a fully integrated multi-agent system that can instantly understand user requests, plan solutions, generate code, trigger third-party services, audit its own outputs, coordinate with specialized sub-agents, and return evidence-based responses grounded in enterprise data.

Use Cases: Automated reporting, data analytics, intelligent help desk, code-generation-as-a-service (CaaS), content synthesis, operational automation, and enterprise AI workflows.

• Docker & Docker Compose
• Python 3.11+
• OpenAI API Key (recommended) or Anthropic API Key

git clone https://github.com/neuraparse/Omni-Agent-Hub.git
cd Omni-Agent-Hub
cp .env.example .env
# Edit .env with your API keys (OpenAI recommended)

docker-compose up -d

pip install -e .
omni-hub serve --reload

# System health check (all 6 services)
curl http://localhost:8000/api/v1/system/status

# Comprehensive health check
curl -X POST http://localhost:8000/api/v1/system/health-check

# Chat with ReAct orchestrator
curl -X POST http://localhost:8000/api/v1/agents/chat \
  -H "Content-Type: application/json" \
  -d '{"message": "Hello! Can you analyze system performance and create a Python script?", "session_id": "test_123"}'

# Upload a file to MinIO
curl -X POST http://localhost:8000/api/v1/files/upload \
  -F "file=@your_file.txt" \
  -F "bucket=user-uploads"

# View analytics dashboard
curl http://localhost:8000/api/v1/analytics/dashboard

# Real-time system monitoring (WebSocket)
# Open: ws://localhost:8000/api/v1/ws/system-monitor

# View API documentation
open http://localhost:8000/docs

The CLI provides easy management of the entire system:

# System health and status
omni-hub health --all              # Check all services
omni-hub status                    # System metrics
omni-hub config                    # View configuration

# Server management
omni-hub serve                     # Start production server
omni-hub serve --reload            # Development with hot reload

# Database operations
omni-hub db init                   # Initialize database
omni-hub db migrate                # Run migrations
omni-hub db reset                  # Reset database

# Vector database management
omni-hub vector create-collection  # Create embeddings collection
omni-hub vector stats              # View collection statistics

# Redis cache management
omni-hub redis flush               # Clear cache
omni-hub redis stats               # View cache statistics

curl -X POST http://localhost:8000/api/v1/agents/chat \
  -H "Content-Type: application/json" \
  -d '{
    "message": "Analyze this data and create a visualization",
    "session_id": "user123",
    "context": {"data_source": "sales_db"}
  }'

# Create session
curl -X POST http://localhost:8000/api/v1/sessions \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user123",
    "context": {"department": "analytics"}
  }'

# Get session
curl http://localhost:8000/api/v1/sessions/{session_id}

curl -X POST http://localhost:8000/api/v1/agents/task \
  -H "Content-Type: application/json" \
  -d '{
    "task_type": "code_generation",
    "session_id": "user123",
    "parameters": {
      "language": "python",
      "description": "Create a data processing pipeline"
    }
  }'

curl -X POST http://localhost:8000/api/v1/knowledge/search \
  -H "Content-Type: application/json" \
  -d '{
    "query": "customer retention strategies",
    "max_results": 5
  }'

import asyncio
from omni_agent_hub.client import OmniAgentClient

async def main():
    client = OmniAgentClient(base_url="http://localhost:8000")
    
    # Create session
    session = await client.create_session(
        user_id="user123",
        context={"department": "engineering"}
    )
    
    # Chat with agent
    response = await client.chat(
        message="Help me optimize this SQL query",
        session_id=session.session_id
    )
    
    print(f"Agent: {response.content}")
    print(f"Confidence: {response.confidence}")
    print(f"Tools used: {response.tools_used}")

asyncio.run(main())

• Automated Report Generation: "Generate weekly sales report with charts"
• Data Pipeline Orchestration: "Process customer data and update dashboards"
• Compliance Monitoring: "Check all systems for security compliance"

# Example: Automated reporting
curl -X POST http://localhost:8000/api/v1/agents/chat \
  -d '{"message": "Generate Q4 sales report with trend analysis", "session_id": "reporting"}'

• Technical Support: Multi-step troubleshooting with tool integration
• Knowledge Base Queries: Context-aware answers from company documentation
• Escalation Management: Automatic routing to appropriate specialists

# Example: Technical support
curl -X POST http://localhost:8000/api/v1/agents/chat \
  -d '{"message": "User cannot access the CRM system, help troubleshoot", "session_id": "support"}'

• API Development: "Create REST API for user management"
• Database Schema Design: "Design schema for e-commerce platform"
• Testing Automation: "Generate unit tests for this module"

# Example: Code generation
curl -X POST http://localhost:8000/api/v1/agents/task \
  -d '{
    "task_type": "code_generation",
    "parameters": {
      "language": "python",
      "description": "Create a FastAPI endpoint for user authentication"
    }
  }'

• Business Intelligence: "Analyze customer churn patterns"
• Predictive Analytics: "Forecast next quarter revenue"
• Market Research: "Research competitor pricing strategies"

# Example: Data analysis
curl -X POST http://localhost:8000/api/v1/agents/chat \
  -d '{"message": "Analyze customer behavior data and identify key trends", "session_id": "analytics"}'

• Technical Documentation: "Create API documentation from code"
• Training Materials: "Generate onboarding guide for new developers"
• Marketing Content: "Create product feature comparison"

• Deployment Automation: "Deploy application to staging environment"
• Monitoring & Alerting: "Check system health and create alerts"
• Infrastructure as Code: "Generate Terraform scripts for AWS setup"

• Risk assessment automation
• Regulatory compliance reporting
• Fraud detection workflows
• Customer service automation

• Patient data analysis
• Treatment protocol recommendations
• Medical research assistance
• Administrative task automation

• Inventory management
• Customer behavior analysis
• Personalized recommendations
• Supply chain optimization

• Quality control automation
• Predictive maintenance
• Supply chain coordination
• Production optimization

• Code review automation
• Bug triage and resolution
• Documentation generation
• Performance monitoring

• Basic CRUD operations
• Simple data transformations
• Static content serving
• Basic form processing

• High-frequency trading
• Emergency response systems
• Real-time control systems
• Safety-critical applications

• Personal health records (without proper compliance)
• Financial transactions (without audit trails)
• Legal document processing (without review)
• Classified information handling

# .env for development
DEBUG=true
LOG_LEVEL=DEBUG
OPENAI_MODEL=gpt-4o
AUTO_FALLBACK_ENABLED=true
REFLECTION_ENABLED=true

# .env for production
DEBUG=false
LOG_LEVEL=INFO
OPENAI_MODEL=gpt-4.1
RATE_LIMIT_REQUESTS_PER_MINUTE=1000
PROMETHEUS_ENABLED=true

# .env for secure environments
CODE_EXECUTION_ENABLED=false
ENABLE_CORS=false
SECRET_KEY=your_super_secure_key
BLOCKED_IMPORTS="os,sys,subprocess,socket,urllib"

1. 🧠 ReAct Orchestrator - Advanced reasoning engine with Thought → Action → Observation → Reflection pattern
2. 🔧 CodeAct Runner - Secure code execution in isolated Docker containers with real-time monitoring
3. 🛠️ MCP-based ToolHub - Model Context Protocol integration for external services (Kagi, AWS, Slack, dbt)
4. 🔍 Self-Reflection Unit - Continuous quality assurance and performance optimization
5. 🤝 Multi-Agent Workflows - Collaborative coordination between specialized agents (Planner, Developer, QA, Analyst)
6. 📚 Agentic RAG - Intelligent retrieval-augmented generation with Milvus vector database

graph TB
    A[User Request] --> B[FastAPI Gateway]
    B --> C[ReAct Orchestrator]
    C --> D[PostgreSQL Session]
    C --> E[Redis Cache]
    C --> F[Milvus Vector Search]
    C --> G[OpenAI LLM]
    C --> H[Kafka Events]
    C --> I[MinIO Files]

    D --> J[Analytics Dashboard]
    E --> J
    F --> J
    H --> J
    I --> J

    J --> K[Real-time Monitoring]
    K --> L[WebSocket Updates]

• PostgreSQL: 16 tables, real-time interaction logging
• Redis: Session caching, memory optimization
• Milvus: Vector embeddings, knowledge search
• Kafka: Event streaming, system coordination
• MinIO: 5 buckets, file management
• OpenAI: GPT-4o models, text-embedding-3-small

• 🔒 API Authentication: JWT-based authentication with role-based access control
• 🛡️ Input Validation: Comprehensive request validation and sanitization
• 🏰 Code Sandboxing: Isolated Docker containers for code execution
• 📝 Audit Logging: Complete audit trail for all operations
• 🔐 Secret Management: Secure handling of API keys and credentials

• SOC 2 Type II: Security controls and monitoring
• GDPR: Data privacy and user consent management
• HIPAA: Healthcare data protection (with proper configuration)
• ISO 27001: Information security management

# Comprehensive system status (all 6 services)
curl http://localhost:8000/api/v1/system/status

# Advanced health checks with service tests
curl -X POST http://localhost:8000/api/v1/system/health-check

# Real-time analytics dashboard
curl http://localhost:8000/api/v1/analytics/dashboard

# Learning system analytics
curl http://localhost:8000/api/v1/analytics/learning

# System performance metrics
curl http://localhost:8000/api/v1/system/metrics

# Event streaming status
curl http://localhost:8000/api/v1/events/stream

// Real-time system monitoring
const ws = new WebSocket('ws://localhost:8000/api/v1/ws/system-monitor');
ws.onmessage = (event) => {
    const data = JSON.parse(event.data);
    console.log('System Status:', data);
};

// Real-time chat monitoring
const chatWs = new WebSocket('ws://localhost:8000/api/v1/ws/chat/session_123');

• 📊 Interaction Analytics: Success rates, confidence scores, execution times
• 🧠 Learning Analytics: Pattern recognition, improvement trends
• ⚡ Performance Metrics: Service health, response times, throughput
• 📁 File Analytics: Storage usage, upload/download metrics
• 🔄 Event Analytics: Real-time event streaming, system coordination

• Prometheus: Metrics collection and alerting
• Grafana: Dashboards and visualization
• Jaeger: Distributed tracing
• ELK Stack: Log aggregation and analysis
• Kafka Connect: Event stream integration

docker-compose up -d
omni-hub serve --reload

# Using Docker Compose
docker-compose -f docker-compose.prod.yml up -d

# Using Kubernetes
kubectl apply -f k8s/

• AWS: ECS, EKS, or Lambda deployment options
• Azure: Container Instances or AKS
• GCP: Cloud Run or GKE
• Kubernetes: Helm charts provided

We welcome contributions! Please see our Contributing Guide for details.

git clone https://github.com/neuraparse/Omni-Agent-Hub.git
cd Omni-Agent-Hub
python -m venv venv
source venv/bin/activate  # or `venv\Scripts\activate` on Windows
pip install -e ".[dev]"
pre-commit install

pytest tests/
pytest tests/ --cov=omni_agent_hub

• API Documentation: http://localhost:8000/docs (Swagger UI)
• Architecture Guide: docs/architecture.md
• Deployment Guide: docs/deployment.md
• Security Guide: docs/security.md

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

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

• OpenAI for GPT models and API
• Anthropic for Claude models
• The FastAPI community
• Docker and container ecosystem
• Open source AI/ML community

• 🔄 Real-time Event Streaming - Apache Kafka with agent-events and system-events topics
• 📁 Intelligent File Management - MinIO with 5 buckets for organized storage
• 🧠 Context-Aware Memory - Redis caching with session-based memory management
• 📊 Advanced Analytics - Real-time dashboard with interaction metrics and learning analytics
• 🔍 Vector Knowledge Search - Milvus database with semantic search capabilities
• 🌐 WebSocket Support - Real-time chat and system monitoring
• 🔧 Health Monitoring - Comprehensive health checks for all services
• 📈 Adaptive Learning - Session-based learning with performance tracking

• SOC2 Compliance Ready - Security controls and audit logging
• Multi-tenant Architecture - Session isolation and user management
• Scalable Design - Microservices with Docker orchestration
• Real-time Analytics - Performance metrics and learning insights
• Event-driven Architecture - Kafka-based coordination and monitoring
• Comprehensive API - REST endpoints + WebSocket for real-time features

# Get real-time system status
curl http://localhost:8000/api/v1/system/status

# View analytics dashboard
curl http://localhost:8000/api/v1/analytics/dashboard

# Monitor via WebSocket
ws://localhost:8000/api/v1/ws/system-monitor

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