Enterprise DNA
O Open Source Observability medium

LiteLLM πŸš…

by Community

Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging. [Bedrock, Azure, OpenAI, Vertex

LοΏ½

OSS

LiteLLM πŸš…

Added 1 June 2026

#ai-gateway #anthropic #azure-openai #bedrock #gateway #langchain #litellm #llm

Overview

Python SDK and proxy server that abstracts 100+ LLM APIs behind a unified OpenAI-compatible interface. Handles cost tracking, request logging, load balancing, and guardrails across providers like Bedrock, Azure, Anthropic, VertexAI, and HuggingFace without rewriting application code.

Best for

Best for
Teams managing multiple LLM providers or needing cost visibility across model calls

Use cases

  • Switch between LLM providers without changing application code
  • Track token costs and usage across multiple models in production
  • Distribute requests across models for load balancing and fallback

Notes

Python SDK and proxy server that abstracts 100+ LLM APIs behind a unified OpenAI-compatible interface. Handles cost tracking, request logging, load balancing, and guardrails across providers like Bedrock, Azure, Anthropic, VertexAI, and HuggingFace without rewriting application code.

48,950 stars on GitHub. Last updated 2026-06-01.

Use cases

  • Switch between LLM providers without changing application code
  • Track token costs and usage across multiple models in production
  • Distribute requests across models for load balancing and fallback

Pros

  • Supports 100+ models with standardized API interface
  • Built-in cost tracking and logging for observability
  • Can run as proxy server or Python SDK for flexible deployment

Cons

  • Adds latency layer between application and LLM endpoints
  • Requires maintenance as new model APIs and breaking changes emerge
  • Community-maintained project with no commercial support guarantee

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Supports 100+ models with standardized API interface
  • Built-in cost tracking and logging for observability
  • Can run as proxy server or Python SDK for flexible deployment

Cons

  • Adds latency layer between application and LLM endpoints
  • Requires maintenance as new model APIs and breaking changes emerge
  • Community-maintained project with no commercial support guarantee

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