LiteLLM vs Mastra
Side-by-side comparison of two AI agent tools
Short answer
- LiteLLM is growing faster: +2,999 GitHub stars in the last 30 days vs +969 for Mastra.
- Pick LiteLLM for: open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface. Pick Mastra for: from the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents.
From GitHub data refreshed daily.
LiteLLMfree
Open-source Python SDK and AI gateway for calling 100+ LLMs through a unified OpenAI-compatible interface
Mastrafree
From the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.
Metrics
| LiteLLM | Mastra | |
|---|---|---|
| Stars | 60.0k | 28.5k |
| Star velocity /mo | 3.0k | 969.4148936170212 |
| Commits (90d) | 13.2k | 4.0k |
| Releases (6m) | 10 | 10 |
| Overall score | 0.9397920782417792 | 0.9079103856981554 |
Pros
- +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
- +统一的多提供商接口支持 40+ AI 模型提供商，避免供应商锁定
- +完整的 AI 应用工具链包括代理、工作流、人机交互和上下文管理
- +TypeScript 原生支持和现代技术栈集成，开发体验优秀
Cons
- -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
- -作为相对较新的框架，生态系统和社区资源可能有限
- -多功能集成可能带来学习曲线，需要时间掌握各个组件
- -文档和最佳实践可能还在完善中，缺少大规模生产案例
Use Cases
- •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
- •构建需要多个 AI 模型协作的复杂智能代理系统
- •开发需要人机交互审批流程的自动化工作流应用
- •快速原型验证 AI 产品概念并扩展到生产环境
FAQ
- Which is more popular, LiteLLM or Mastra?
- LiteLLM has more GitHub stars (59,982 vs 28,467).
- Which is more actively developed, LiteLLM or Mastra?
- LiteLLM had more commits in the last 90 days (13,173 vs 4,007).
- Should I use LiteLLM or Mastra?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.