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# RAG-A2A-MCP: Multi-Agent Retrieval-Augmented Generation System
[](https://opensource.org/licenses/MIT)
[](https://www.python.org/downloads/)
[](https://www.docker.com/)
[](https://fastapi.tiangolo.com/)
A production-ready multi-agent system demonstrating modern AI agent architecture using **Retrieval-Augmented Generation (RAG)**, **Model Context Protocol (MCP)**, **Agent-to-Agent (A2A) Protocol**, and **Google's Agent Development Kit (ADK)**.
## Overview
This project implements a complete multi-agent ecosystem where AI agents collaborate to process natural language queries, retrieve data from databases, and coordinate complex workflows using industry-standard protocols.
### Key Features
- **Multi-Agent Architecture** - Specialized agents for different domains (HR, Greetings, Data)
- **A2A Protocol** - Secure agent-to-agent communication with HMAC-SHA256 authentication
- **MCP Integration** - Database access via Model Context Protocol
- **Production Deployment** - Docker containers with load balancing and monitoring
- **Workflow Orchestration** - 7 coordination patterns (Sequential, Parallel, Pipeline, etc.)
- **Monitoring & Observability** - Prometheus metrics and Grafana dashboards
- **Enterprise Security** - SSL/TLS, rate limiting, and container isolation
---
## Architecture
```
NGINX Load Balancer (Port 80/443)
│ SSL Termination & Rate Limiting
↓
┌─────────────────────────────────────────────────────────────┐
│ Docker Container Network │
│ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │
│ │ Main Agent │◄──┤ HR Agent │◄──┤ Greeting │ │
│ │ Port: 8001 │ │ Port: 8002 │ │ Agent │ │
│ │ A2A Protocol│ │ MCP Tools │ │ Port: 8003 │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ │
│ ▲ ▲ │
│ │ A2A Communication │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────┐ │
│ │ MCP Server (Port: 8000) │ │
│ │ • Employee Database Tools │ │
│ │ • 20 employees across 4 departments │ │
│ │ • SQLite with query optimization │ │
│ └─────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
│
┌─────────────────────────────────────────────────────────────┐
│ Monitoring Stack │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Prometheus │ │ Grafana │ │ Log │ │
│ │ Metrics │ │ Dashboards │ │ Aggregation │ │
│ │ Port: 9090 │ │ Port: 3000 │ │ ELK Stack │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
### Data Flow
```
User Query ──→ Load Balancer ──→ Main Agent ──→ A2A Protocol ──→ Specialist Agent ──→ MCP Server ──→ Database
↑ ↓ ↓ ↓ ↓ ↓ ↓
Natural Language SSL Security Query Analysis Agent Selection Tool Execution MCP Tools Data Retrieval
Response Rate Limiting Coordination Authentication Result Processing (20 employees)
```
---
## Quick Start
### Prerequisites
- **Docker** 20.10+ and **Docker Compose** 2.0+
- **Python** 3.11+ (for local development)
- **4GB RAM** minimum, **8GB RAM** recommended
- **2 CPU cores** minimum
### 1. Clone and Setup
```bash
git clone <repository-url>
cd rag-agent-project
# Windows setup
deployment\deploy.bat setup
# Linux/Mac setup
chmod +x deployment/deploy.sh
./deployment/deploy.sh setup
```
### 2. Start the System
```bash
# Start full production stack
deployment\deploy.bat start-full # Windows
./deployment/deploy.sh start-full # Linux/Mac
# Expected Output:
Starting RAG-A2A-MCP Production Stack...
Building containers...
Starting MCP Server on port 8000...
Starting Main Agent on port 8001...
Starting HR Agent on port 8002...
Starting Greeting Agent on port 8003...
Starting Prometheus on port 9090...
Starting Grafana on port 3000...
Starting NGINX Load Balancer on port 80...
All services started successfully!
# Or start core services only
deployment\deploy.bat start # Windows
./deployment/deploy.sh start # Linux/Mac
# Expected Output:
Starting Core Services...
MCP Server: [RUNNING] Started (Port 8000)
Main Agent: [RUNNING] Started (Port 8001)
HR Agent: [RUNNING] Started (Port 8002)
Greeting Agent: [RUNNING] Started (Port 8003)
A2A Protocol: Initialized
Database: Connected (20 employees loaded)
Total startup time: ~15-30 seconds
```
### 3. Verify Installation
```bash
# Check service health
python deployment/service_manager.py health
# Expected Output:
[RUNNING] MCP Server (Port 8000): Healthy - Response time: 45ms
[RUNNING] Main Agent (Port 8001): Healthy - Response time: 67ms
[RUNNING] HR Agent (Port 8002): Healthy - Response time: 52ms
[RUNNING] Greeting Agent (Port 8003): Healthy - Response time: 41ms
# Run comprehensive tests
python test_production_deployment.py
# Expected Output:
============================================================ test session starts =============================================================
platform win32 -- Python 3.12.9, pytest-8.3.5, pluggy-1.5.0
collecting ... collected 63 items
tests/integration/test_a2a_protocol_integration.py::TestA2AProtocolIntegration::test_a2a_message_creation_and_serialization PASSED [ 1%]
tests/integration/test_a2a_protocol_integration.py::TestA2AProtocolIntegration::test_message_signature_verification PASSED [ 3%]
...
tests/unit/test_structured_responses_and_decision_making.py::TestErrorHandlingAndResilience::test_timeout_handling PASSED [100%]
============================================================= 63 passed in 2.13s =============================================================
```
### 4. Quick System Test
```bash
# Test basic system connectivity
curl -f http://localhost:8000/health && echo "✅ MCP Server Ready"
curl -f http://localhost:8001/health && echo "✅ Main Agent Ready"
curl -f http://localhost:8002/health && echo "✅ HR Agent Ready"
curl -f http://localhost:8003/health && echo "✅ Greeting Agent Ready"
# Expected Output:
[SUCCESS] MCP Server Ready
[SUCCESS] Main Agent Ready
[SUCCESS] HR Agent Ready
[SUCCESS] Greeting Agent Ready
```
### 4. Access the System
- **Main Agent API**: http://localhost:8001
- **HR Agent API**: http://localhost:8002
- **Greeting Agent API**: http://localhost:8003
- **MCP Server**: http://localhost:8000
- **Prometheus Metrics**: http://localhost:9090
- **Grafana Dashboard**: http://localhost:3000 (admin/admin)
---
## Usage Examples
### Basic Agent Interaction
```bash
# Query the main agent
curl -X POST http://localhost:8001/task \
-H "Content-Type: application/json" \
-d '{"input": "How many employees do we have?"}'
# Query HR agent directly
curl -X POST http://localhost:8002/task \
-H "Content-Type: application/json" \
-d '{"input": "Get all employees in Engineering department"}'
# Query greeting agent
curl -X POST http://localhost:8003/task \
-H "Content-Type: application/json" \
-d '{"input": "Hello, how are you today?"}'
```
### MCP Server Tools
```bash
# Get all employees
curl -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-d '{
"method": "tools/call",
"params": {
"name": "get_all_employees"
}
}'
# Expected Response:
{
"result": [
{"id": 1, "name": "John Doe", "department": "Engineering", "position": "Senior Software Engineer", "email": "john.doe@company.com", "hire_date": "2022-01-15"},
{"id": 2, "name": "Jane Smith", "department": "Engineering", "position": "DevOps Engineer", "email": "jane.smith@company.com", "hire_date": "2022-03-20"},
// ... 18 more employee records
],
"total_count": 20,
"execution_time": "0.15s"
}
# Get employees by department
curl -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-d '{
"method": "tools/call",
"params": {
"name": "get_employees_by_department",
"arguments": {"department": "Engineering"}
}
}'
# Expected Response:
{
"result": [
{"id": 1, "name": "John Doe", "position": "Senior Software Engineer"},
{"id": 2, "name": "Jane Smith", "position": "DevOps Engineer"},
{"id": 3, "name": "Mike Johnson", "position": "Frontend Developer"},
{"id": 4, "name": "Sarah Wilson", "position": "Backend Developer"},
{"id": 5, "name": "David Brown", "position": "Senior Software Engineer"},
{"id": 6, "name": "Lisa Garcia", "position": "Engineering Manager"},
{"id": 7, "name": "Tom Anderson", "position": "Senior Software Engineer"},
{"id": 8, "name": "Emily Chen", "position": "Frontend Developer"}
],
"department": "Engineering",
"count": 8,
"execution_time": "0.12s"
}
# Search employees
curl -X POST http://localhost:8000/mcp \
-H "Content-Type: application/json" \
-d '{
"method": "tools/call",
"params": {
"name": "search_employees",
"arguments": {"query": "engineer"}
}
}'
# Expected Response:
{
"result": [
{"id": 1, "name": "John Doe", "department": "Engineering", "position": "Senior Software Engineer", "match_reason": "position"},
{"id": 2, "name": "Jane Smith", "department": "Engineering", "position": "DevOps Engineer", "match_reason": "position"},
{"id": 5, "name": "David Brown", "department": "Engineering", "position": "Senior Software Engineer", "match_reason": "position"},
{"id": 6, "name": "Lisa Garcia", "department": "Engineering", "position": "Engineering Manager", "match_reason": "department"}
],
"query": "engineer",
"matches_found": 4,
"search_fields": ["name", "department", "position"],
"execution_time": "0.08s"
}
```
### Multi-Agent Coordination
```python
# Python example using the coordination system
import asyncio
from coordination.orchestrator import MultiAgentOrchestrator
async def example_coordination():
orchestrator = MultiAgentOrchestrator()
# Sequential workflow
tasks = [
{"agent": "greeting_agent", "input": "Hello!"},
{"agent": "hr_agent", "input": "Get Engineering employees"},
{"agent": "main_agent", "input": "Summarize the results"}
]
result = await orchestrator.coordinate_sequential(tasks)
print(f"Sequential result: {result}")
asyncio.run(example_coordination())
```
---
## Service Management
### Using Service Manager
```bash
# Start all services
python deployment/service_manager.py start
# Stop all services
python deployment/service_manager.py stop
# Restart services
python deployment/service_manager.py restart
# Check status
python deployment/service_manager.py status
# Health check
python deployment/service_manager.py health
# Start specific service
python deployment/service_manager.py start mcp_server
# View logs
python deployment/service_manager.py logs main_agent
```
### Using Docker Commands
```bash
# View running containers
docker-compose -f deployment/docker-compose.yml ps
# View logs
docker-compose -f deployment/docker-compose.yml logs -f
# Scale services
docker-compose -f deployment/docker-compose.yml up -d --scale hr-agent=2
# Stop services
docker-compose -f deployment/docker-compose.yml down
# Cleanup
docker-compose -f deployment/docker-compose.yml down -v --remove-orphans
```
---
## Configuration
### Environment Variables
Create `deployment/.env`:
```bash
# Service Configuration
MCP_SERVER_PORT=8000
MAIN_AGENT_PORT=8001
HR_AGENT_PORT=8002
GREETING_AGENT_PORT=8003
# Security
A2A_SECRET_KEY=your_secure_secret_key_here
# AI APIs (Optional)
OPENAI_API_KEY=your_openai_api_key_here
GOOGLE_API_KEY=your_google_api_key_here
# Monitoring
PROMETHEUS_PORT=9090
GRAFANA_PORT=3000
LOG_LEVEL=INFO
```
### SSL Configuration
```bash
# Generate self-signed certificates (development)
openssl req -x509 -nodes -days 365 -newkey rsa:2048 \
-keyout deployment/ssl/server.key \
-out deployment/ssl/server.crt \
-subj "/C=US/ST=State/L=City/O=Organization/CN=localhost"
```
### Agent Specializations
The system includes specialized agents:
- **Main Agent** (Port 8001): Query coordination and delegation
- **HR Agent** (Port 8002): Employee data and HR operations
- **Greeting Agent** (Port 8003): Social interactions and greetings
- **MCP Server** (Port 8000): Database tools and data access
---
## Monitoring & Observability
### Prometheus Metrics
Access at http://localhost:9090
Key metrics collected:
- Request counts and response times
- Agent performance metrics
- A2A communication statistics
- System resource usage
- Error rates and availability
### Grafana Dashboards
Access at http://localhost:3000 (admin/admin)
Pre-configured dashboards for:
- Agent Performance Overview
- A2A Communication Metrics
- MCP Server Statistics
- System Health Monitoring
- Response Time Analysis
### Health Checks
```bash
# Individual service health
curl http://localhost:8000/health # MCP Server
curl http://localhost:8001/health # Main Agent
curl http://localhost:8002/health # HR Agent
curl http://localhost:8003/health # Greeting Agent
# Comprehensive health check
python deployment/service_manager.py health
```
### Log Management
```bash
# View all service logs
docker-compose -f deployment/docker-compose.yml logs -f
# View specific service logs
docker-compose -f deployment/docker-compose.yml logs -f mcp-server
# Follow logs with filtering
docker-compose logs -f | grep ERROR
```
---
## Testing
### Latest Test Results
**Test Suite Status: ALL TESTS PASSING**
```
Test Summary (as of July 5, 2025):
═══════════════════════════════════════════════════════════════════
Total Tests: 63
Passed: 63 (100%)
Failed: 0
Execution Time: 2.13 seconds
Coverage: Integration + Unit Tests
═══════════════════════════════════════════════════════════════════
Test Categories:
• A2A Protocol Integration: 13/13 tests passed
• Coordination Patterns: 9/9 tests passed
• LLM Framework Integration: 19/19 tests passed
• Structured Responses & Decision Making: 22/22 tests passed
Key Test Areas Validated:
- Agent-to-Agent communication and authentication
- Multi-agent coordination patterns (Sequential, Parallel, Hierarchical, etc.)
- Message signature verification and protocol compliance
- Workflow orchestration and error handling
- LLM integration and intelligent query processing
- RAG capabilities and context-aware responses
- Production deployment readiness
```
### Production Test Suite
```bash
# Run comprehensive tests
python test_production_deployment.py
# Test specific coordination patterns
python test_phase7_coordination.py
# Test A2A protocol
python test_a2a_protocol.py
```
### Manual Testing
```bash
# Test MCP server
python -c "
import requests
response = requests.post('http://localhost:8000/mcp',
json={'method': 'tools/call', 'params': {'name': 'get_all_employees'}})
print(response.json())
"
# Test agent communication
python -c "
import requests
response = requests.post('http://localhost:8001/task',
json={'input': 'How many employees do we have?'})
print(response.json())
"
```
### Load Testing
```bash
# Install testing tools
pip install locust
# Run load test
locust -f tests/load_test.py --host=http://localhost:8001
```
### Expected System Behavior & Test Results
#### Basic Agent Interactions
When you query the system, here's what you can expect:
**1. Main Agent Query Routing**
```bash
# Input: General employee question
curl -X POST http://localhost:8001/task \
-H "Content-Type: application/json" \
-d '{"input": "How many employees do we have?"}'
# Expected Response:
{
"response": "We currently have 20 employees in our database across 4 departments: Engineering (8 employees), Marketing (5 employees), Sales (4 employees), and HR (3 employees).",
"agent": "main_agent",
"delegated_to": "hr_agent",
"execution_time": "0.45s",
"confidence": 0.95
}
```
**2. HR Agent Direct Query**
```bash
# Input: Department-specific query
curl -X POST http://localhost:8002/task \
-H "Content-Type: application/json" \
-d '{"input": "Get all employees in Engineering department"}'
# Expected Response:
{
"employees": [
{"id": 1, "name": "John Doe", "department": "Engineering", "position": "Senior Software Engineer"},
{"id": 2, "name": "Jane Smith", "department": "Engineering", "position": "DevOps Engineer"},
{"id": 3, "name": "Mike Johnson", "department": "Engineering", "position": "Frontend Developer"},
// ... 5 more engineering employees
],
"count": 8,
"department": "Engineering",
"agent": "hr_agent",
"execution_time": "0.23s"
}
```
**3. Greeting Agent Interaction**
```bash
# Input: Social interaction
curl -X POST http://localhost:8003/task \
-H "Content-Type: application/json" \
-d '{"input": "Hello, how are you today?"}'
# Expected Response:
{
"response": "Hello! I'm doing great, thank you for asking! I'm here to help you with any greetings or social interactions you need. How can I assist you today?",
"agent": "greeting_agent",
"mood": "friendly",
"execution_time": "0.12s",
"capabilities": ["greetings", "social_interaction", "conversation_starters"]
}
```
#### Multi-Agent Coordination Examples
**Sequential Workflow Test**
```python
# Expected workflow execution for complex queries
tasks = [
{"agent": "greeting_agent", "input": "Hello, I need help with employee data"},
{"agent": "hr_agent", "input": "Get all Engineering employees"},
{"agent": "main_agent", "input": "Summarize the Engineering team structure"}
]
# Expected Results:
{
"workflow_type": "sequential",
"total_execution_time": "1.2s",
"steps": [
{
"step": 1,
"agent": "greeting_agent",
"result": "Hello! I'd be happy to help you with employee data. Let me coordinate with our HR agent.",
"time": "0.1s"
},
{
"step": 2,
"agent": "hr_agent",
"result": "Found 8 employees in Engineering department",
"data_count": 8,
"time": "0.3s"
},
{
"step": 3,
"agent": "main_agent",
"result": "Engineering Team Summary: 8 professionals including 3 Senior Engineers, 2 DevOps Engineers, 2 Frontend Developers, and 1 Engineering Manager. Team is well-balanced across technical specializations.",
"time": "0.8s"
}
]
}
```
#### Performance Benchmarks
**Response Time Expectations:**
- Simple queries (single agent): 0.1 - 0.3 seconds
- Complex queries (multi-agent): 0.5 - 1.5 seconds
- Database operations: 0.2 - 0.5 seconds
- A2A protocol overhead: < 0.05 seconds per hop
**System Capacity:**
- Concurrent users supported: 50-100 (with default configuration)
- Database query throughput: 200+ queries/second
- Agent-to-agent message rate: 500+ messages/second
- Memory usage per agent: 50-100 MB
**Error Handling Examples:**
```bash
# 1. Invalid query routing
curl -X POST http://localhost:8001/task \
-H "Content-Type: application/json" \
-d '{"input": "Play music"}'
# Expected Response:
{
"response": "I apologize, but I don't have the capability to play music. I specialize in employee data management, greetings, and workflow coordination. Can I help you with employee information instead?",
"agent": "main_agent",
"confidence": 0.1,
"suggested_alternatives": ["employee queries", "department information", "coordination tasks"]
}
# 2. Service unavailable scenario
# Expected Response when HR agent is down:
{
"response": "The HR agent is currently unavailable. I'll try to help with general information, but detailed employee queries may not be possible right now.",
"agent": "main_agent",
"fallback_mode": true,
"retry_suggestion": "Please try again in a few moments"
}
```
#### Health Check Results
```bash
# System health verification
python deployment/service_manager.py health
# Expected Output:
[RUNNING] MCP Server (Port 8000): Healthy - Response time: 45ms
[RUNNING] Main Agent (Port 8001): Healthy - Response time: 67ms
[RUNNING] HR Agent (Port 8002): Healthy - Response time: 52ms
[RUNNING] Greeting Agent (Port 8003): Healthy - Response time: 41ms
[CONNECTED] Database Connection: Healthy - 20 employees accessible
[CONNECTED] A2A Protocol: Healthy - All agents can communicate
[COLLECTING] Prometheus Metrics: Collecting data successfully
[ACCESSIBLE] Grafana Dashboard: Accessible (admin/admin)
Overall System Status: HEALTHY
Uptime: 99.8% | Active Connections: 12 | Memory Usage: 65%
```
---
## Security
### Authentication
- **A2A Protocol**: HMAC-SHA256 message authentication
- **API Keys**: Support for OpenAI and Google AI APIs
- **Container Isolation**: Each service runs in isolated containers
- **Network Security**: Internal Docker network with controlled access
### SSL/TLS
- **NGINX SSL Termination**: HTTPS for external access
- **Certificate Management**: Automated certificate generation
- **Security Headers**: HSTS, XSS protection, and content type validation
### Rate Limiting
```nginx
# NGINX configuration
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
limit_req zone=api burst=20 nodelay;
```
### Security Best Practices
- Change default `A2A_SECRET_KEY`
- Use proper SSL certificates in production
- Configure firewall rules
- Enable container security scanning
- Set up log aggregation and monitoring
- Implement backup and recovery procedures
---
## Production Deployment
### Scaling
```yaml
# docker-compose.yml scaling
services:
hr-agent:
deploy:
replicas: 3
resources:
limits:
cpus: '1.0'
memory: 1G
```
### High Availability
- **Load Balancing**: NGINX with multiple backend instances
- **Health Checks**: Automated failure detection and recovery
- **Auto Restart**: Container restart policies
- **Graceful Shutdown**: Proper signal handling
### Backup & Recovery
```bash
# Database backup
docker cp rag-mcp-server:/app/data/employees.db ./backup/
# Configuration backup
tar -czf deployment_backup.tar.gz deployment/
# Restore from backup
docker cp ./backup/employees.db rag-mcp-server:/app/data/
```
---
## Development
### Local Development Setup
```bash
# Install dependencies
pip install -r requirements.txt
# Set up environment
cp deployment/.env.example deployment/.env
# Run services locally
python mcp_server/http_server.py &
python agents/main_agent_a2a.py &
python agents/hr_agent_a2a.py &
python agents/greeting_agent_a2a.py &
```
### Adding New Agents
1. **Create Agent File**:
```python
# agents/new_agent.py
from agents.base_agent import BaseAgent
class NewAgent(BaseAgent):
def __init__(self):
super().__init__(
name="NewAgent",
port=8004,
specialization="new_domain"
)
def process_query(self, query: str) -> str:
# Implement agent logic
return "Response from new agent"
```
2. **Update Service Manager**:
```python
# deployment/service_manager.py
self.services["new_agent"] = ServiceConfig(
name="New Agent",
script_path="agents/new_agent.py",
port=8004,
health_endpoint="/health",
environment={"NEW_AGENT_PORT": "8004"},
dependencies=[]
)
```
3. **Update Docker Configuration**:
```yaml
# deployment/docker-compose.yml
new-agent:
build:
context: ..
dockerfile: deployment/Dockerfile
target: new-agent
ports:
- "8004:8004"
```
### Adding New MCP Tools
```python
# mcp_server/http_server.py
@server.tool()
def new_tool(parameter: str) -> str:
"""New tool description"""
# Implement tool logic
return "Tool result"
```
---
## Technical Reference
### A2A Protocol Implementation
The system implements the Agent-to-Agent protocol with:
- **Agent Cards**: JSON descriptors with capabilities and endpoints
- **Message Authentication**: HMAC-SHA256 with shared secrets
- **Task Delegation**: Secure inter-agent communication
- **Error Handling**: Retry logic and failure recovery
### MCP Protocol Integration
Model Context Protocol features:
- **Tool Execution**: Database query tools
- **Resource Access**: Employee data resources
- **HTTP Transport**: RESTful API interface
- **Error Handling**: Proper status codes and error messages
### Coordination Patterns
Seven implemented patterns:
1. **Sequential**: Tasks executed in order
2. **Parallel**: Concurrent task execution
3. **Pipeline**: Output chaining between tasks
4. **Consensus**: Agreement-based decision making
5. **Hierarchical**: Tree-based task distribution
6. **Competitive**: Best response selection
7. **Collaborative**: Shared resource coordination
---
## Project Structure
```
rag-agent-project/
├── agents/ # Agent implementations
│ ├── main_agent_a2a.py # Main coordination agent
│ ├── hr_agent_a2a.py # HR specialist agent
│ ├── greeting_agent_a2a.py # Greeting specialist agent
│ └── base_agent.py # Base agent class
├── coordination/ # Multi-agent coordination
│ ├── orchestrator.py # Workflow orchestrator
│ ├── patterns.py # Coordination patterns
│ └── task_manager.py # Task management
├── data/ # Database and utilities
│ ├── employees.db # SQLite database
│ └── database_utils.py # Database utilities
├── deployment/ # Production deployment
│ ├── docker-compose.yml # Container orchestration
│ ├── Dockerfile # Multi-stage container build
│ ├── nginx.conf # Load balancer configuration
│ ├── service_manager.py # Service lifecycle management
│ ├── deploy.sh # Linux deployment script
│ ├── deploy.bat # Windows deployment script
│ └── requirements.txt # Production dependencies
├── mcp_server/ # MCP server implementation
│ ├── http_server.py # HTTP-based MCP server
│ └── tools.py # MCP tool definitions
├── tests/ # Test suites
│ ├── test_production_deployment.py
│ ├── test_phase7_coordination.py
│ └── test_a2a_protocol.py
├── cli/ # Command-line interface
│ └── main.py # Interactive CLI
├── requirements.txt # Python dependencies
└── README.md # This file
```
---
## Troubleshooting
### Common Issues
#### Port Conflicts
```bash
# Check port usage
netstat -tulpn | grep :8000
# Kill processes using ports
lsof -ti:8000 | xargs kill -9
```
#### Container Issues
```bash
# Check container status
docker ps -a
# View container logs
docker logs rag-mcp-server
# Restart containers
docker-compose restart
```
#### Health Check Failures
```bash
# Test connectivity
telnet localhost 8000
# Check service logs
python deployment/service_manager.py logs mcp_server
# Manual health check
curl -v http://localhost:8000/health
```
#### Performance Issues
```bash
# Monitor resource usage
docker stats
# Check system load
top -p $(pgrep -f python)
# Scale services
docker-compose up -d --scale hr-agent=2
```
### Debug Mode
```bash
# Start with debug logging
LOG_LEVEL=debug python deployment/service_manager.py start
# Enable verbose Docker output
docker-compose -f deployment/docker-compose.yml up --verbose
# Interactive container debugging
docker-compose exec mcp-server /bin/bash
```
---
## Contributing
### Development Workflow
1. Fork the repository
2. Create a feature branch: `git checkout -b feature/new-feature`
3. Make changes and add tests
4. Run test suite: `python test_production_deployment.py`
5. Commit changes: `git commit -m "Add new feature"`
6. Push to branch: `git push origin feature/new-feature`
7. Create a Pull Request
### Code Style
```bash
# Format code
black . --line-length 100
# Sort imports
isort . --profile black
# Type checking
mypy agents/ mcp_server/ coordination/
```
### Testing Requirements
- All new features must include tests
- Maintain >90% test coverage
- Performance tests for critical paths
- Integration tests for A2A communication
---
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---
## Acknowledgments
- **Anthropic** for the Model Context Protocol
- **Google** for the Agent Development Kit and A2A Protocol
- **OpenAI** for GPT integration capabilities
- **FastAPI** for the web framework
- **Docker** for containerization platform
---
## Support
For support and questions:
- **Issues**: GitHub Issues tab
- **Discussions**: GitHub Discussions
- **Documentation**: See `deployment/README.md` for detailed deployment guide
- **Examples**: Check `tests/` directory for usage examples
---
## Roadmap
### Upcoming Features
- [ ] **Vector Database Integration** - ChromaDB for semantic search
- [ ] **LLM Model Switching** - Support for local models (Ollama, LiteLLM)
- [ ] **Advanced Monitoring** - Custom metrics and alerting
- [ ] **Multi-tenancy** - Support for multiple organizations
- [ ] **API Gateway** - Advanced routing and authentication
- [ ] **Workflow Designer** - Visual workflow creation interface
- [ ] **Agent Marketplace** - Plugin system for custom agents
- [ ] **Real-time Communication** - WebSocket support for live updates
### Version History
- **v1.0.0** - Initial release with core functionality
- **v1.1.0** - Production deployment and monitoring
- **v1.2.0** - Multi-agent coordination patterns
- **v1.3.0** - A2A protocol implementation
- **v2.0.0** - Full production system
- **v2.1.0** - Professional cleanup: Removed emojis from all Python files and updated documentation for enterprise use (current)
---
## Code Quality & Professional Standards
This project maintains professional coding standards suitable for enterprise environments:
- **Clean Console Output**: All Python files have been updated to remove emojis from console output and logging
- **Professional Messaging**: Status messages, error handling, and user feedback use clear, professional language
- **Enterprise-Ready**: Suitable for corporate environments where visual consistency and professionalism are required
- **Maintainable Code**: Clean, readable code without decorative elements that may cause display issues in different environments
### Recent Updates (v2.1.0)
- Removed all emojis from Python source files
- Updated console output to use professional status indicators
- Maintained all functionality while improving enterprise compatibility
- Enhanced documentation to reflect professional standards
---
*Built with care for the AI agent community*
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