# AI Integration Overview

MockLoop MCP is specifically designed to enhance AI-assisted development workflows, providing seamless integration with popular AI frameworks and enabling sophisticated testing and development scenarios.

## Why AI Integration?

Modern AI development requires robust testing environments, realistic data, and the ability to simulate various scenarios. MockLoop MCP addresses these needs by:

- **Providing Realistic APIs**: Generate mock servers that behave like real services
- **Enabling Dynamic Testing**: Create and switch between test scenarios instantly
- **Offering Comprehensive Analytics**: Monitor AI system interactions with detailed logging
- **Supporting Rapid Iteration**: Update responses and configurations without restarts

## Supported AI Frameworks

MockLoop MCP provides native integration with major AI frameworks:

### 🔗 LangGraph
- **Workflow Integration**: Embed mock servers in LangGraph state machines
- **Dynamic State Management**: Update mock responses based on workflow state
- **Error Simulation**: Test error handling and recovery scenarios
- **Performance Testing**: Simulate API latencies and failures

### 👥 CrewAI
- **Multi-Agent Testing**: Provide different mock services for different agents
- **Role-Based Scenarios**: Create scenarios tailored to specific agent roles
- **Collaborative Workflows**: Test agent interactions through mock APIs
- **Task Validation**: Verify agent outputs against expected API responses

### 🦜 LangChain
- **Tool Integration**: Use MockLoop tools within LangChain applications
- **Chain Testing**: Test complex chains with realistic API responses
- **Memory Validation**: Verify chain memory against mock data
- **Retrieval Testing**: Mock external data sources for RAG applications

### 🔧 Custom Frameworks
- **Generic Integration**: REST API compatibility with any framework
- **Custom Tools**: Extend MockLoop with framework-specific tools
- **Webhook Support**: Real-time notifications for AI system events
- **Flexible Configuration**: Adapt to any AI workflow requirements

## Core AI Integration Features

### 1. Dynamic Response Management

Update API responses in real-time without restarting servers:

```python
# Update responses based on AI system state
await mockloop.manage_mock_data(
    server_url="http://localhost:8000",
    operation="update_response",
    endpoint_path="/ai/model/status",
    response_data={
        "status": "training",
        "progress": 0.75,
        "eta_minutes": 15
    }
)
```

### 2. Scenario-Based Testing

Create comprehensive test scenarios for different AI system states:

```python
# Create scenarios for different AI model states
scenarios = {
    "model_training": {
        "/ai/model/status": {"status": "training", "progress": 0.5},
        "/ai/predictions": {"error": "Model not ready", "status": 503}
    },
    "model_ready": {
        "/ai/model/status": {"status": "ready", "accuracy": 0.95},
        "/ai/predictions": {"predictions": [...], "confidence": 0.87}
    },
    "model_error": {
        "/ai/model/status": {"status": "error", "error": "Training failed"},
        "/ai/predictions": {"error": "Service unavailable", "status": 500}
    }
}
```

### 3. Performance Monitoring

Track AI system interactions with detailed analytics:

```python
# Analyze AI system API usage
logs = await mockloop.query_mock_logs(
    server_url="http://localhost:8000",
    analyze=True,
    path_pattern="/ai/.*"
)

# Get insights on AI system behavior
print(f"AI API calls: {logs['analysis']['total_requests']}")
print(f"Average response time: {logs['analysis']['avg_response_time_ms']}ms")
print(f"Error rate: {logs['analysis']['error_rate_percent']}%")
```

### 4. Real-Time Adaptation

Adapt mock responses based on AI system behavior:

```python
# Monitor AI system requests and adapt responses
def adapt_responses(request_logs):
    if request_logs['error_rate'] > 0.1:
        # Switch to error recovery scenario
        mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name="error_recovery"
        )
    elif request_logs['avg_response_time'] > 1000:
        # Switch to high-performance scenario
        mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name="fast_responses"
        )
```

## AI Development Workflows

### Workflow 1: AI Model Development

1. **Setup Mock Data Sources**
   ```python
   # Generate mocks for training data APIs
   await mockloop.generate_mock_api(
       spec_url_or_path="./training-data-api.yaml",
       output_dir_name="training_data_mock"
   )
   ```

2. **Create Training Scenarios**
   ```python
   # Different data quality scenarios
   scenarios = {
       "high_quality_data": {...},
       "noisy_data": {...},
       "missing_data": {...},
       "corrupted_data": {...}
   }
   ```

3. **Train and Test Models**
   ```python
   for scenario in scenarios:
       # Switch to scenario
       await mockloop.manage_mock_data(
           operation="switch_scenario",
           scenario_name=scenario
       )
       
       # Train model with scenario data
       model = train_model(data_source="http://localhost:8000")
       
       # Evaluate performance
       metrics = evaluate_model(model)
       
       # Log results
       print(f"Scenario {scenario}: Accuracy = {metrics.accuracy}")
   ```

### Workflow 2: AI Agent Testing

1. **Setup Multi-Service Environment**
   ```python
   # Generate mocks for different services
   services = [
       {"spec": "./user-service.yaml", "port": 8001},
       {"spec": "./payment-service.yaml", "port": 8002},
       {"spec": "./inventory-service.yaml", "port": 8003}
   ]
   
   for service in services:
       await mockloop.generate_mock_api(
           spec_url_or_path=service["spec"],
           output_dir_name=f"service_{service['port']}"
       )
   ```

2. **Create Agent Test Scenarios**
   ```python
   # Test different business scenarios
   scenarios = {
       "happy_path": {
           # All services working normally
           "user_service": {"status": "healthy"},
           "payment_service": {"status": "healthy"},
           "inventory_service": {"status": "healthy"}
       },
       "payment_failure": {
           # Payment service down
           "user_service": {"status": "healthy"},
           "payment_service": {"status": "error", "error": "Service unavailable"},
           "inventory_service": {"status": "healthy"}
       }
   }
   ```

3. **Run Agent Tests**
   ```python
   for scenario_name, scenario_config in scenarios.items():
       # Configure all services for scenario
       for service, config in scenario_config.items():
           await mockloop.manage_mock_data(
               server_url=f"http://localhost:{service_ports[service]}",
               operation="update_response",
               endpoint_path="/health",
               response_data=config
           )
       
       # Run agent with scenario
       result = run_agent_scenario(scenario_name)
       
       # Analyze agent behavior
       logs = await mockloop.query_mock_logs(
           server_url="http://localhost:8001",
           analyze=True
       )
   ```

### Workflow 3: RAG System Testing

1. **Mock Knowledge Base APIs**
   ```python
   # Generate mock for document retrieval API
   await mockloop.generate_mock_api(
       spec_url_or_path="./knowledge-base-api.yaml",
       output_dir_name="knowledge_base_mock"
   )
   ```

2. **Create Document Scenarios**
   ```python
   # Different document availability scenarios
   document_scenarios = {
       "comprehensive_docs": {
           "/search": {"documents": [...], "total": 100},
           "/document/{id}": {"content": "...", "metadata": {...}}
       },
       "limited_docs": {
           "/search": {"documents": [...], "total": 5},
           "/document/{id}": {"error": "Document not found", "status": 404}
       },
       "slow_retrieval": {
           "/search": {"documents": [...], "delay_ms": 2000},
           "/document/{id}": {"content": "...", "delay_ms": 1000}
       }
   }
   ```

3. **Test RAG Performance**
   ```python
   for scenario in document_scenarios:
       # Configure knowledge base mock
       await mockloop.manage_mock_data(
           operation="switch_scenario",
           scenario_name=scenario
       )
       
       # Test RAG system
       questions = ["What is...?", "How do I...?", "Why does...?"]
       for question in questions:
           answer = rag_system.ask(question)
           quality = evaluate_answer_quality(question, answer)
           
           print(f"Scenario {scenario}, Question: {question}")
           print(f"Answer quality: {quality}")
   ```

## Integration Patterns

### Pattern 1: State-Driven Mocks

Synchronize mock responses with AI system state:

```python
class StateDrivenMock:
    def __init__(self, mockloop_client, server_url):
        self.mockloop = mockloop_client
        self.server_url = server_url
        self.current_state = "initial"
    
    async def update_state(self, new_state, context=None):
        """Update mock responses based on AI system state"""
        self.current_state = new_state
        
        # Define state-specific responses
        state_responses = {
            "training": {
                "/model/status": {"status": "training", "progress": context.get("progress", 0)},
                "/predictions": {"error": "Model not ready", "status": 503}
            },
            "ready": {
                "/model/status": {"status": "ready", "accuracy": context.get("accuracy", 0.9)},
                "/predictions": {"predictions": context.get("predictions", [])}
            }
        }
        
        # Update all endpoints for the new state
        for endpoint, response in state_responses[new_state].items():
            await self.mockloop.manage_mock_data(
                server_url=self.server_url,
                operation="update_response",
                endpoint_path=endpoint,
                response_data=response
            )
```

### Pattern 2: Behavior-Driven Testing

Test AI system behavior under different conditions:

```python
class BehaviorDrivenTesting:
    def __init__(self, mockloop_client):
        self.mockloop = mockloop_client
        self.test_scenarios = {}
    
    async def define_behavior(self, behavior_name, conditions):
        """Define a behavior scenario"""
        self.test_scenarios[behavior_name] = conditions
        
        await self.mockloop.manage_mock_data(
            operation="create_scenario",
            scenario_name=behavior_name,
            scenario_config=conditions
        )
    
    async def test_behavior(self, behavior_name, ai_system):
        """Test AI system under specific behavior conditions"""
        # Switch to behavior scenario
        await self.mockloop.manage_mock_data(
            operation="switch_scenario",
            scenario_name=behavior_name
        )
        
        # Run AI system
        result = await ai_system.execute()
        
        # Analyze behavior
        logs = await self.mockloop.query_mock_logs(
            server_url=ai_system.api_endpoint,
            analyze=True
        )
        
        return {
            "behavior": behavior_name,
            "result": result,
            "api_interactions": logs["analysis"],
            "success": self.evaluate_behavior(result, behavior_name)
        }
```

### Pattern 3: Adaptive Response Generation

Generate responses based on AI system requests:

```python
class AdaptiveResponseGenerator:
    def __init__(self, mockloop_client):
        self.mockloop = mockloop_client
        self.request_history = []
    
    async def monitor_and_adapt(self, server_url):
        """Monitor requests and adapt responses"""
        while True:
            # Get recent logs
            logs = await self.mockloop.query_mock_logs(
                server_url=server_url,
                limit=10,
                analyze=False
            )
            
            # Analyze request patterns
            patterns = self.analyze_patterns(logs["logs"])
            
            # Adapt responses based on patterns
            if patterns["high_error_rate"]:
                await self.switch_to_recovery_mode(server_url)
            elif patterns["high_load"]:
                await self.optimize_for_performance(server_url)
            
            await asyncio.sleep(30)  # Check every 30 seconds
    
    async def switch_to_recovery_mode(self, server_url):
        """Switch to error recovery responses"""
        await self.mockloop.manage_mock_data(
            server_url=server_url,
            operation="switch_scenario",
            scenario_name="error_recovery"
        )
    
    async def optimize_for_performance(self, server_url):
        """Switch to high-performance responses"""
        await self.mockloop.manage_mock_data(
            server_url=server_url,
            operation="switch_scenario",
            scenario_name="fast_responses"
        )
```

## Best Practices for AI Integration

### 1. Design for Testability

- **Modular APIs**: Design AI systems with clear API boundaries
- **Configurable Endpoints**: Make API endpoints configurable
- **Graceful Degradation**: Handle API failures gracefully
- **Comprehensive Logging**: Log all AI system decisions and API interactions

### 2. Create Realistic Scenarios

- **Real-World Data**: Use realistic data in mock responses
- **Error Conditions**: Include various error scenarios
- **Performance Variations**: Simulate different response times
- **Edge Cases**: Test boundary conditions and unusual inputs

### 3. Monitor and Analyze

- **Continuous Monitoring**: Track AI system behavior continuously
- **Performance Metrics**: Monitor response times and error rates
- **Behavior Analysis**: Analyze AI decision patterns
- **Feedback Loops**: Use insights to improve AI systems

### 4. Automate Testing

- **CI/CD Integration**: Include AI testing in deployment pipelines
- **Regression Testing**: Test AI systems against known scenarios
- **Performance Benchmarks**: Establish and monitor performance baselines
- **Automated Scenarios**: Create self-updating test scenarios

## Getting Started

Ready to integrate MockLoop MCP with your AI framework? Choose your framework:

- **[LangGraph Integration](langgraph.md)**: Build state-driven AI workflows
- **[CrewAI Integration](crewai.md)**: Test multi-agent systems
- **[LangChain Integration](langchain.md)**: Enhance chain testing
- **[Custom AI Workflows](custom-workflows.md)**: Create custom integrations

## Examples Repository

Find complete examples and templates in our examples repository:

- **AI Model Training**: Mock data sources for model training
- **Multi-Agent Systems**: Complex agent interaction scenarios
- **RAG Applications**: Knowledge base and retrieval testing
- **Chatbot Testing**: Conversation flow and response testing
- **ML Pipeline Testing**: End-to-end pipeline validation

## Community and Support

- **GitHub Discussions**: Share AI integration patterns
- **Example Contributions**: Contribute AI workflow examples
- **Framework Requests**: Request support for new AI frameworks
- **Best Practices**: Share and learn AI testing best practices

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MockLoop MCP transforms AI development by providing realistic, controllable, and observable API environments. Start with your preferred framework and discover how mock servers can accelerate your AI development workflow!