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LiteLLM

The AI Engineer presents LiteLLM

Overview

LiteLLM standardizes inputs and outputs across LLM APIs like OpenAI, Azure, Anthropic, etc. It lets you call any LLM using a consistent OpenAI-like format, with built-in streaming, logging, and load balancing between providers.

Description

LiteLLM is an open-source package that enables you to call multiple LLM APIs (Azure, Anthropic, Replicate, etc.) using a consistent OpenAI-like input format. It helps eliminate the complexity of dealing with provider-specific API calls.

Key Highlights

  • 🚘 Consistent inputs/outputs - Call different LLMs with OpenAI-like completion() & embedding() and obtain standardized results.
  • 🔁 Built-in load balancing - Route requests across providers optimized for rate limits & usage.
  • 🌀 Streaming support - Stream responses for performance & cost savings.
  • 📊 Logs & analytics - Integrate monitoring via Sentry, Posthog, etc., to analyze usage.
  • ⚙️ Extensible - Easily add new LLM provider integrations. Auto-configured from a GUI/UI.

Whether you want to simplify calling multiple provider APIs or build robust systems with error handling and advanced logic across models, LiteLLM makes it dramatically easier. With extensive test coverage across 50+ cases and battle-tested in production, it provides a reliable foundation for calling LLMs consistently.

🤔 Why should The AI Engineer care about LiteLLM?

  1. ⚡️ Speed - 10x faster development cycles integrating any LLM API into apps
  2. 🔌 Flexibility - Hotswap LLMs and providers with no code change via unified interface
  3. 📡 Simplicity - One line of code removes need to handle model complexities
  4. 🔒 Reliability - Built-in robustness via exception handling and observability
  5. 🎚 Configurability - Easily customize load balancing, authentication, logging etc.

In summary, LiteLLM eliminates the undifferentiated heavy lifting when leveraging LLMs so engineers can focus innovation on end-user functionality and capabilities.

📊 LiteLLM Stats

🖇️ Links

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