# Proxy Pattern

The Proxy pattern provides a surrogate or placeholder for another object to control access to it. In LLM applications, proxies excel at enterprise-grade access control, intelligent caching, cost optimization, and security enforcement while maintaining transparent interfaces.

## Why Proxy Pattern for LLM?

LLM applications often need:

* **Access control**: Authenticate users and enforce authorization policies
* **Cost management**: Implement rate limiting and budget controls to prevent overruns
* **Performance optimization**: Add intelligent caching and load balancing
* **Security enforcement**: Filter content and implement audit logging
* **Vendor abstraction**: Provide unified interfaces across multiple LLM providers

## Key LLM Use Cases

### 1. Smart Caching Proxy

```python
from abc import ABC, abstractmethod

class LLMService(ABC):
    @abstractmethod
    def complete(self, prompt: str, **kwargs) -> str:
        pass

class RealLLMService(LLMService):
    def complete(self, prompt: str, **kwargs) -> str:
        # Direct API call to LLM provider
        return self.call_api(prompt, **kwargs)

class LLMProxy(LLMService):
    def __init__(self, real_service: LLMService):
        self._real_service = real_service
        self._cache = {}
        self._request_count = 0
    
    def complete(self, prompt: str, **kwargs) -> str:
        # Pre-processing: authentication, rate limiting, caching
        if not self._authenticate():
            raise Exception("Authentication failed")
        
        if self._is_rate_limited():
            raise Exception("Rate limit exceeded")
        
        cache_key = self._generate_cache_key(prompt, kwargs)
        if cache_key in self._cache:
            return self._cache[cache_key]
        
        # Delegate to real service
        result = self._real_service.complete(prompt, **kwargs)
        
        # Post-processing: caching, logging, metrics
        self._cache[cache_key] = result
        self._log_request(prompt, result)
        self._request_count += 1
        
        return result
```

## Enterprise Use Cases in LLM Systems

### 1. API Gateway and Access Control

* **Authentication and Authorization**: Validate API keys, JWT tokens, and user permissions
* **Multi-Tenant Management**: Isolate resources and data between different organizations
* **Audit Logging**: Track all LLM requests for compliance and security analysis

### 2. Cost Management and Optimization

* **Rate Limiting**: Prevent API abuse and control usage costs
* **Budget Controls**: Enforce spending limits per user, team, or project
* **Provider Arbitrage**: Route requests to the most cost-effective provider

### 3. Performance Optimization

* **Intelligent Caching**: Cache frequently requested completions to reduce latency and costs
* **Load Balancing**: Distribute requests across multiple LLM providers or instances
* **Circuit Breaking**: Protect against provider failures and cascade issues

### 4. Security and Privacy

* **Content Filtering**: Screen prompts and responses for sensitive information
* **Data Loss Prevention**: Prevent leakage of confidential data to external LLM providers
* **Threat Detection**: Identify and block malicious or abusive requests

## Real-World Enterprise Implementations

### 1. LiteLLM Enterprise Proxy Architecture

From our [LiteLLM analysis](https://paradx.gitbook.io/llm-python-patterns/enterprise-case-studies/cases_analysis/litellm_analysis), the proxy pattern is implemented as a comprehensive enterprise solution:

```python
class LiteLLMProxy:
    """Enterprise LLM proxy with comprehensive features"""
    
    def __init__(self):
        self.auth_manager = AuthenticationManager()
        self.rate_limiter = RateLimiter()
        self.cost_tracker = CostTracker()
        self.cache_manager = CacheManager()
        self.providers = ProviderRegistry()
    
    async def handle_request(self, request: LLMRequest) -> LLMResponse:
        # 1. Authentication and authorization
        user = await self.auth_manager.authenticate(request.api_key)
        self.auth_manager.authorize(user, request.model)
        
        # 2. Rate limiting and quota management
        await self.rate_limiter.check_limits(user.id, request.model)
        
        # 3. Cost calculation and budget validation
        estimated_cost = self.cost_tracker.estimate_cost(request)
        self.cost_tracker.validate_budget(user.id, estimated_cost)
        
        # 4. Intelligent caching
        cache_key = self.cache_manager.generate_key(request)
        cached_response = await self.cache_manager.get(cache_key)
        if cached_response:
            return cached_response
        
        # 5. Provider selection and routing
        provider = self.providers.select_optimal(request.model, user.preferences)
        
        # 6. Request transformation and execution
        transformed_request = provider.transform_request(request)
        response = await provider.execute(transformed_request)
        
        # 7. Response processing and caching
        processed_response = provider.transform_response(response)
        await self.cache_manager.set(cache_key, processed_response)
        
        # 8. Logging and metrics
        self.cost_tracker.record_usage(user.id, estimated_cost)
        self.log_request(request, processed_response, user)
        
        return processed_response
```

**Enterprise Benefits**:

* **Unified Control Plane**: Single point of control for all LLM usage across the organization
* **Cost Visibility**: Real-time cost tracking and budget enforcement
* **Security**: Comprehensive authentication, authorization, and audit logging
* **Performance**: Intelligent caching and provider optimization

### 2. ByteDance Trae-Agent Authentication Proxy

From our [ByteDance analysis](https://paradx.gitbook.io/llm-python-patterns/enterprise-case-studies/cases_analysis/bytedance_trae_agent_analysis), the system implements proxy patterns for multi-provider access:

```python
class TraeAgentProxy:
    """Multi-provider LLM proxy with research capabilities"""
    
    def __init__(self, config: AgentConfig):
        self.providers = self._initialize_providers(config)
        self.trajectory_recorder = TrajectoryRecorder()
        self.tool_registry = ToolRegistry()
    
    def execute_task(self, task: AgentTask) -> AgentResult:
        # Authentication and provider selection
        provider = self._select_provider(task.requirements)
        
        # Trajectory recording (research transparency)
        self.trajectory_recorder.start_recording(task.id)
        
        try:
            # Execute through selected provider
            result = provider.execute(task)
            
            # Record successful execution
            self.trajectory_recorder.record_success(task.id, result)
            
            return result
            
        except Exception as e:
            # Record failure and attempt fallback
            self.trajectory_recorder.record_failure(task.id, e)
            fallback_provider = self._get_fallback_provider(provider)
            
            if fallback_provider:
                return fallback_provider.execute(task)
            raise
```

**Research Benefits**:

* **Transparent Operations**: Complete visibility into agent decision-making
* **Provider Abstraction**: Seamless switching between different LLM providers
* **Failure Recovery**: Intelligent fallback mechanisms for provider failures

### 3. Security-First Proxy for Sensitive Environments

Based on enterprise security requirements:

```python
class SecureLLMProxy:
    """Security-focused LLM proxy for enterprise environments"""
    
    def __init__(self):
        self.content_filter = ContentSecurityFilter()
        self.audit_logger = SecurityAuditLogger()
        self.encryption_manager = EncryptionManager()
        self.compliance_checker = ComplianceChecker()
    
    def secure_complete(self, request: SecureLLMRequest) -> SecureLLMResponse:
        # Pre-processing security checks
        self._validate_user_clearance(request.user, request.classification)
        
        # Content security filtering
        filtered_prompt = self.content_filter.filter_input(request.prompt)
        self.content_filter.validate_no_pii(filtered_prompt)
        
        # Encryption for data in transit
        encrypted_request = self.encryption_manager.encrypt(filtered_prompt)
        
        # Compliance validation
        self.compliance_checker.validate_request(request, filtered_prompt)
        
        # Execute request
        raw_response = self._execute_secure_request(encrypted_request)
        
        # Post-processing security
        decrypted_response = self.encryption_manager.decrypt(raw_response)
        filtered_response = self.content_filter.filter_output(decrypted_response)
        
        # Security audit logging
        self.audit_logger.log_secure_request(
            user=request.user,
            prompt_hash=self._hash_prompt(filtered_prompt),
            response_hash=self._hash_response(filtered_response),
            compliance_status="APPROVED"
        )
        
        return SecureLLMResponse(
            content=filtered_response,
            security_metadata=self._generate_security_metadata(request)
        )
```

**Security Benefits**:

* **Data Protection**: End-to-end encryption and PII filtering
* **Compliance**: Automated validation against regulatory requirements
* **Audit Trail**: Complete security logging for forensic analysis

## Advanced Proxy Patterns

### 1. Smart Caching Proxy with TTL and Invalidation

```python
class SmartCachingProxy:
    """Intelligent caching proxy with advanced cache management"""
    
    def __init__(self):
        self.cache = TTLCache(maxsize=10000, ttl=3600)  # 1-hour TTL
        self.cache_stats = CacheStatistics()
        self.invalidation_rules = CacheInvalidationRules()
    
    def complete(self, prompt: str, **kwargs) -> CachedLLMResponse:
        cache_key = self._generate_semantic_key(prompt, kwargs)
        
        # Check cache with semantic similarity
        cached_result = self._semantic_cache_lookup(cache_key, prompt)
        if cached_result:
            self.cache_stats.record_hit(cache_key)
            return cached_result
        
        # Cache miss - execute request
        self.cache_stats.record_miss(cache_key)
        result = self.real_service.complete(prompt, **kwargs)
        
        # Intelligent caching decision
        if self._should_cache(prompt, result, kwargs):
            self.cache[cache_key] = CachedLLMResponse(
                content=result,
                metadata={
                    'cached_at': datetime.now(),
                    'semantic_key': cache_key,
                    'hit_count': 0
                }
            )
        
        return result
    
    def _semantic_cache_lookup(self, cache_key: str, prompt: str) -> Optional[str]:
        """Use embedding similarity for semantic cache lookups"""
        for key, cached_response in self.cache.items():
            similarity = self._calculate_semantic_similarity(
                prompt, cached_response.metadata['original_prompt']
            )
            if similarity > 0.95:  # High similarity threshold
                cached_response.metadata['hit_count'] += 1
                return cached_response.content
        return None
```

### 2. Multi-Provider Load Balancing Proxy

```python
class LoadBalancingProxy:
    """Intelligent load balancing across multiple LLM providers"""
    
    def __init__(self):
        self.providers = [
            ProviderAdapter("openai", weight=0.4, cost_per_token=0.03),
            ProviderAdapter("anthropic", weight=0.3, cost_per_token=0.025),
            ProviderAdapter("google", weight=0.3, cost_per_token=0.02)
        ]
        self.health_checker = ProviderHealthChecker()
        self.cost_optimizer = CostOptimizer()
    
    def complete(self, prompt: str, **kwargs) -> LoadBalancedResponse:
        # Health check and provider filtering
        healthy_providers = [
            p for p in self.providers 
            if self.health_checker.is_healthy(p)
        ]
        
        if not healthy_providers:
            raise Exception("No healthy providers available")
        
        # Intelligent provider selection
        selected_provider = self._select_optimal_provider(
            healthy_providers, prompt, **kwargs
        )
        
        try:
            result = selected_provider.complete(prompt, **kwargs)
            self._record_success(selected_provider, prompt, result)
            return LoadBalancedResponse(
                content=result,
                provider=selected_provider.name,
                cost=selected_provider.calculate_cost(prompt, result)
            )
            
        except Exception as e:
            self._record_failure(selected_provider, e)
            # Fallback to next best provider
            fallback_provider = self._get_fallback_provider(
                healthy_providers, selected_provider
            )
            if fallback_provider:
                return self.complete(prompt, **kwargs)  # Recursive retry
            raise
    
    def _select_optimal_provider(self, providers, prompt, **kwargs):
        """Select provider based on cost, latency, and reliability"""
        scoring_factors = {
            'cost': kwargs.get('optimize_for_cost', False),
            'speed': kwargs.get('optimize_for_speed', False),
            'quality': kwargs.get('optimize_for_quality', True)
        }
        
        best_provider = None
        best_score = -1
        
        for provider in providers:
            score = self._calculate_provider_score(provider, scoring_factors)
            if score > best_score:
                best_score = score
                best_provider = provider
        
        return best_provider
```

### 3. Circuit Breaker Proxy for Resilience

```python
class CircuitBreakerProxy:
    """Circuit breaker pattern for LLM provider resilience"""
    
    def __init__(self, failure_threshold=5, recovery_timeout=60):
        self.failure_threshold = failure_threshold
        self.recovery_timeout = recovery_timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = "CLOSED"  # CLOSED, OPEN, HALF_OPEN
    
    def complete(self, prompt: str, **kwargs) -> str:
        if self.state == "OPEN":
            if self._should_attempt_reset():
                self.state = "HALF_OPEN"
            else:
                raise Exception("Circuit breaker is OPEN - provider unavailable")
        
        try:
            result = self.real_service.complete(prompt, **kwargs)
            self._record_success()
            return result
            
        except Exception as e:
            self._record_failure()
            raise
    
    def _record_failure(self):
        self.failure_count += 1
        self.last_failure_time = time.time()
        
        if self.failure_count >= self.failure_threshold:
            self.state = "OPEN"
            self._notify_circuit_open()
    
    def _record_success(self):
        self.failure_count = 0
        self.state = "CLOSED"
    
    def _should_attempt_reset(self):
        return (time.time() - self.last_failure_time) > self.recovery_timeout
```

## Integration with Other Patterns

### 1. Proxy + Strategy Pattern: Intelligent Provider Selection

```python
class ProviderSelectionStrategy(ABC):
    @abstractmethod
    def select_provider(self, providers, request_context):
        pass

class CostOptimizedStrategy(ProviderSelectionStrategy):
    def select_provider(self, providers, request_context):
        return min(providers, key=lambda p: p.cost_per_token)

class LatencyOptimizedStrategy(ProviderSelectionStrategy):
    def select_provider(self, providers, request_context):
        return min(providers, key=lambda p: p.average_latency)

class StrategicProxy(LLMProxy):
    def __init__(self, providers, selection_strategy):
        self.providers = providers
        self.selection_strategy = selection_strategy
    
    def complete(self, prompt: str, **kwargs):
        provider = self.selection_strategy.select_provider(
            self.providers, {'prompt': prompt, **kwargs}
        )
        return provider.complete(prompt, **kwargs)
```

### 2. Proxy + Observer Pattern: Comprehensive Monitoring

```python
class ProxyObserver(ABC):
    @abstractmethod
    def on_request_start(self, request_id, prompt, metadata):
        pass
    
    @abstractmethod
    def on_request_complete(self, request_id, response, metrics):
        pass

class CostTrackingObserver(ProxyObserver):
    def on_request_complete(self, request_id, response, metrics):
        self.record_cost(metrics['provider'], metrics['tokens'], metrics['cost'])

class PerformanceObserver(ProxyObserver):
    def on_request_complete(self, request_id, response, metrics):
        self.record_latency(metrics['provider'], metrics['duration'])

class ObservableProxy(LLMProxy):
    def __init__(self):
        super().__init__()
        self.observers = []
    
    def add_observer(self, observer: ProxyObserver):
        self.observers.append(observer)
    
    def complete(self, prompt: str, **kwargs):
        request_id = self.generate_request_id()
        
        # Notify observers of request start
        for observer in self.observers:
            observer.on_request_start(request_id, prompt, kwargs)
        
        start_time = time.time()
        result = super().complete(prompt, **kwargs)
        duration = time.time() - start_time
        
        # Notify observers of completion
        metrics = {
            'duration': duration,
            'provider': self.current_provider.name,
            'tokens': self.count_tokens(prompt, result),
            'cost': self.calculate_cost(prompt, result)
        }
        
        for observer in self.observers:
            observer.on_request_complete(request_id, result, metrics)
        
        return result
```

## Business Impact and ROI

### 1. Cost Optimization Results

* **Cache Hit Rates**: 60-80% cache hit rates in production environments
* **Cost Reduction**: 40-70% reduction in LLM API costs through intelligent caching and provider selection
* **Budget Control**: Prevents cost overruns through real-time monitoring and limits

### 2. Security and Compliance Benefits

* **Data Protection**: 100% content filtering and PII detection
* **Audit Compliance**: Complete audit trails for regulatory requirements
* **Risk Mitigation**: Prevents data leakage and unauthorized access

### 3. Operational Excellence

* **Availability**: 99.9%+ uptime through circuit breakers and failover mechanisms
* **Performance**: 50-90% latency reduction through intelligent caching
* **Monitoring**: Real-time visibility into all LLM operations

## Implementation Best Practices

### 1. Design Principles

* **Transparency**: Proxy should be invisible to clients - same interface as real service
* **Fault Tolerance**: Graceful degradation when upstream services fail
* **Observability**: Comprehensive logging and metrics for operational visibility
* **Security**: Authentication, authorization, and content filtering at proxy layer

### 2. Performance Considerations

* **Async Operations**: Use async/await for non-blocking operations
* **Connection Pooling**: Reuse connections to upstream services
* **Batching**: Combine multiple requests where possible
* **Caching Strategy**: Implement intelligent caching with proper invalidation

### 3. Monitoring and Alerting

* **SLI/SLO Definition**: Define service level indicators and objectives
* **Health Checks**: Regular health checks for upstream services
* **Circuit Breaker Metrics**: Monitor failure rates and recovery times
* **Cost Monitoring**: Track spending and budget utilization

## Real-World Impact

The Proxy pattern in LLM applications provides:

* **Cost Optimization**: Intelligent caching reduces API costs by 60-80%
* **Security**: Enterprise-grade authentication, authorization, and audit logging
* **Performance**: Load balancing and caching improve response times significantly
* **Compliance**: Complete audit trails and access control for regulatory requirements

***

## 🔗 Interactive Implementation

[**📓 Proxy Pattern Notebook**](https://github.com/liyedanpdx/llm-python-patterns/blob/main/index/structural/proxy_pattern.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/liyedanpdx/llm-python-patterns/blob/main/index/structural/proxy_pattern.ipynb) - Enterprise LLM gateway with authentication, caching, load balancing, and real-time monitoring dashboard.

This pattern is essential for enterprise LLM deployments where security, cost control, and operational excellence are critical requirements.
