LiteLLM

LiteLLM is a unified proxy for multiple LLM providers that we're evaluating to simplify model switching, reduce costs, and provide consistent APIs across different AI agent implementations.

Why we're trialing LiteLLM:

• Unified API: Single interface for OpenAI, Anthropic, Azure OpenAI, and local models
• Cost Optimization: Automatic model routing based on cost and performance
• Fallback Handling: Graceful degradation when primary models are unavailable
• Rate Limit Management: Intelligent request distribution across model providers
• Token Tracking: Centralized usage monitoring and cost attribution

Key capabilities for agent systems:

• Model Abstraction: Agents can switch models without code changes
• Load Balancing: Distribute requests across multiple model endpoints
• Caching: Response caching to reduce redundant API calls and costs
• Request Routing: Route requests based on agent requirements and model capabilities
• Retry Logic: Automatic retries with exponential backoff for failed requests

Cost optimization features:

• Smart Routing: Choose cheapest model that meets quality requirements
• Request Batching: Combine multiple agent requests for efficiency
• Usage Analytics: Detailed cost breakdown by agent, model, and use case
• Budget Controls: Set spending limits per agent or customer
• Token Optimization: Track and optimize token usage patterns

Integration potential:

• Kubernetes Deployment: Run as sidecar or dedicated service in our clusters
• External Secrets: Secure management of multiple provider API keys
• Prometheus Metrics: Export usage and performance metrics for monitoring
• Service Mesh: Integrate with Istio for secure inter-service communication
• Agent Frameworks: Drop-in replacement for direct LLM API calls

Evaluation focus:

• Latency Impact: Overhead compared to direct API calls
• Reliability: Failure handling and uptime characteristics
• Model Coverage: Support for all models we need for different agent types
• Configuration Management: Ease of managing routing rules and policies
• Observability: Quality of metrics, logging, and debugging capabilities

Use cases being tested:

• Multi-model agents: Agents that can use different models for different tasks
• Cost-sensitive workflows: Background processing with cheaper models
• High-availability agents: Critical agents with multi-provider fallback
• Experimentation: A/B testing different models for agent performance