FastChat vs LiteLLM

Side-by-side comparison of two AI agent tools

Short answer

  • FastChat has had no commit in 16 months; LiteLLM is actively maintained (13,191 commits in the last 90 days).
  • LiteLLM is growing faster: +2,991 GitHub stars in the last 30 days vs +17 for FastChat.
  • Pick FastChat for: an open platform for training, serving, and evaluating large language models. Pick LiteLLM for: open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface.

From GitHub data refreshed daily.

FastChatopen-source

An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.

Open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface

Metrics

FastChatLiteLLM
Stars39.6k60.0k
Star velocity /mo16.5079365079365063.0k
Commits (90d)013.2k
Releases (6m)010
Overall score0.22019689096006190.9372179102436228

Pros

  • +业界权威的 LLM 评估平台，Chatbot Arena 排行榜是最受认可的模型性能参考标准
  • +完整的端到端解决方案，从模型训练、部署到评估全流程覆盖，支持 OpenAI 兼容 API
  • +活跃的开源生态和丰富的数据集资源，包括真实用户对话数据和人类偏好评估数据
  • +统一API接口设计，一套代码兼容100多个不同的LLM提供商，大幅简化多模型切换和对比测试
  • +内置企业级功能如成本追踪、负载均衡、安全防护栏，为生产环境提供完整的AI治理解决方案
  • +既提供Python SDK又提供独立的代理服务器部署模式，适合不同规模和架构的项目需求

Cons

  • -作为研究导向的平台，生产环境部署可能需要额外的稳定性和性能优化工作
  • -多模型服务系统的资源消耗较大，对硬件配置和运维能力有一定要求
  • -作为中间层抽象，可能无法完全利用某些模型提供商的独特功能和高级参数配置
  • -依赖网络连接和第三方API稳定性，增加了系统的复杂度和潜在故障点
  • -对于简单的单模型应用场景可能存在过度设计，增加不必要的依赖和学习成本

Use Cases

  • •LLM 研究者进行模型训练、微调和性能评估，特别是开发新的对话模型
  • •企业和开发者部署多模型聊天服务，提供统一的 API 接口支持多个 LLM
  • •教育和学术机构建立 LLM 评估基准，收集用户反馈数据进行模型对比分析
  • •AI应用开发中需要对比测试多个LLM模型性能，快速切换不同提供商而无需重写代码
  • •企业级AI服务需要统一的成本监控、访问控制和负载均衡管理多个模型调用
  • •构建AI代理或聊天机器人时需要根据用户需求和成本考虑动态选择最适合的模型
View FastChat DetailsView LiteLLM Details

FAQ

Which is more popular, FastChat or LiteLLM?
LiteLLM has more GitHub stars (60,036 vs 39,557).
Which is more actively developed, FastChat or LiteLLM?
LiteLLM had more commits in the last 90 days (13,191 vs 0).
Should I use FastChat or LiteLLM?
Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.