Announcement
LiteLLM - 统一接口调用100+ LLM API的Python SDK/代理服务器
Simplify interactions with over 100 Large Language Models (LLMs) using a unified OpenAI-compatible API. LiteLLM acts as a Python SDK and Proxy Server, abstracting away provider-specific differences for services like OpenAI, Azure, Bedrock, VertexAI, and more.
Project Introduction
Summary
LiteLLM is an open-source Python library and proxy designed to provide a simple, unified interface for accessing over 100 different Large Language Model APIs. It allows developers to call models from various providers (like OpenAI, Azure, Bedrock, VertexAI, etc.) using the standard OpenAI API format, simplifying development, enabling easy model switching, and facilitating advanced features like failover and load balancing.
Problem Solved
Integrating and managing multiple Large Language Models from different providers involves significant complexity due to varying APIs, authentication methods, and data formats. Developers face challenges with vendor lock-in, testing different models, implementing failover, and tracking usage across platforms.
Core Features
Unified API (OpenAI Format)
Interact with diverse LLM providers (OpenAI, Azure, AWS Bedrock, Google VertexAI, etc.) using a single, familiar API interface.
LLM Proxy Server
Deploy LiteLLM as a proxy server to manage calls, implement fallbacks, retries, and load balancing across different LLM endpoints.
Python SDK
Built as a lightweight Python library, easily integrable into existing Python applications and workflows.
Key Management & Monitoring
Provides tools for key management, usage tracking, and cost monitoring across all integrated LLM providers.
Tech Stack
使用场景
LiteLLM is versatile and can be applied in various scenarios where seamless interaction with multiple LLMs is required:
场景一：构建多模型AI应用
Details
Build AI applications (chatbots, content generation tools, etc.) that can easily switch between different LLM providers based on cost, performance, or availability, without rewriting core logic.
User Value
Increases application resilience and allows for dynamic optimization of LLM usage.
场景二：集中化LLM API管理与监控
Details
Companies or teams needing to manage API keys, track usage, and monitor costs centrally for LLM calls originating from different projects or users.
User Value
Simplifies operational overhead and provides better visibility into LLM expenses.
场景三：增强生产环境LLM调用的可靠性
Details
Setting up a proxy layer to handle retries, failover, and potentially load balancing across multiple endpoints or providers for critical production workloads.
User Value
Minimizes downtime and ensures continuity of service even if a primary LLM provider experiences issues.
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