LiteLLM

LiteLLM is a lightweight, unified API layer that standardizes interactions with over 100 different LLM providers using the OpenAI SDK format. It solves one of the most frustrating problems in AI development: every LLM provider has a different API, making it painful to switch models, test alternatives, or implement multi-model fallback strategies.

With LiteLLM, you write code once using the familiar OpenAI API structure, and it transparently translates your requests to work with any provider:

• Major Providers: OpenAI, Anthropic (Claude), Google (Gemini), Cohere, AI21, Replicate, Hugging Face.
• Open Source: Ollama, vLLM, LocalAI, Together AI, Anyscale, Groq.
• Azure & AWS: Azure OpenAI, AWS Bedrock with full authentication support.
• Custom Endpoints: Any OpenAI-compatible API endpoint.

This abstraction provides massive flexibility and resilience for production applications:

Example Usage:

from litellm import completion

# Same code works across all providers
response = completion(
  model="gpt-4",  # or "claude-3-opus", "gemini-pro", etc.
  messages=[{"role": "user", "content": "Hello!"}]
)

# Seamlessly switch providers
response = completion(
  model="claude-3-sonnet",  # Just change the model name
  messages=[{"role": "user", "content": "Hello!"}]
)

LiteLLM's key capabilities include:

• Automatic Fallbacks: Configure fallback chains (e.g., GPT-4 → Claude → Gemini) so your app stays online even if one provider has an outage.
• Load Balancing: Distribute requests across multiple API keys or providers to stay within rate limits and optimize costs.
• Cost Tracking: Built-in tracking of token usage and costs across all providers in a unified format.
• Streaming Support: Consistent streaming API across all providers, even those with different streaming implementations.
• Caching: Automatic response caching to reduce redundant API calls and costs.
• Function Calling: Unified function/tool calling interface that works across providers with different native implementations.

LiteLLM is particularly valuable for:

• Model Evaluation: Easily compare responses from multiple models without rewriting integration code.
• Reliability: Implement sophisticated fallback strategies to maintain uptime during provider outages.
• Cost Optimization: Route requests to the cheapest available provider that meets quality requirements.
• Vendor Independence: Avoid lock-in by maintaining the flexibility to switch providers based on performance, cost, or availability.

The library also includes LiteLLM Proxy, a production-ready server that acts as a central API gateway for all LLM requests in your organization. It provides team-based access control, budget limits, usage analytics, and a unified endpoint for all models. This is especially useful for enterprises managing multiple teams and projects with different model requirements. By standardizing on the OpenAI format (the most widely adopted LLM API specification), LiteLLM ensures compatibility with existing tools and frameworks while giving you the freedom to use any model provider.