liteLLM Proxy Server: 50+ LLM Models, Error Handling, Caching
Hello hacker news, I’m the maintainer of liteLLM() - package to simplify input/output to OpenAI, Azure, Cohere, Anthropic, Hugging face API Endpoints: https://github.com/BerriAI/litellm/ We’re open sourcing our implementation of liteLLM proxy: https://github.com/BerriAI/litellm/blob/main/cookbook/proxy-... TLDR: It has one API endpoint /chat/completions and standardizes input/output for 50+ LLM models + handles logging, error tracking, caching, streaming What can liteLLM proxy do? - It’s a central place to…
In plain words
liteLLM Proxy Server is an open-source tool that provides a single API endpoint to access over 50 large language models from providers including OpenAI, Azure, Cohere, Anthropic, and Hugging Face. It standardizes input and output across different models using OpenAI's format, so developers can call any model with consistent syntax. The proxy handles logging, error tracking, caching, and streaming, and includes model fallback capabilities to automatically switch providers if one fails. It is designed for developers building applications that need to work with multiple LLM providers through a unified interface.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hello hacker news, I’m the maintainer of liteLLM() - package to simplify input/output to OpenAI, Azure, Cohere, Anthropic, Hugging face API Endpoints: https://github.com/BerriAI/litellm/ We’re open sourcing our implementation of liteLLM proxy: https://github.com/BerriAI/litellm/blob/main/cookbook/proxy-... TLDR: It has one API endpoint /chat/completions and standardizes input/output for 50+ LLM models + handles logging, error tracking, caching, streaming What can liteLLM proxy do? - It’s a central place to manage all LLM provider integrations - Consistent Input/Output Format - Call all models using the OpenAI format: completion(model, messages) - Text responses will always be available at ['choices'][0]['message']['content'] - Error Handling Using Model Fallbacks (if GPT-4 fails, try llama2) - Logging - Log Requests, Responses and Errors to Supabase, Posthog, Mixpanel, Sentry, Helicone - Token Usage & Spend - Track Input + Completion tokens used + Spend/model - Caching - Implementation of Semantic Caching - Streaming & Async Support - Return generators to stream text responses You can deploy liteLLM to your own infrastructure using Railway, GCP, AWS, Azure Happy completion() !
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