langgraph vs Mastra
Side-by-side comparison of two AI agent tools
Short answer
- Mastra is growing faster: +968 GitHub stars in the last 30 days vs +99 for langgraph.
- Pick langgraph for: framework to build resilient language agents as graphs. Pick Mastra for: from the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents.
From GitHub data refreshed daily.
langgraphopen-source
Framework to build resilient language agents as graphs.
Mastrafree
From the team behind Gatsby, Mastra is a framework for building AI-powered applications and agents with a modern TypeScript stack.
Metrics
| langgraph | Mastra | |
|---|---|---|
| Stars | 3.3k | 28.5k |
| Star velocity /mo | 98.52631578947368 | 968.3684210526316 |
| Commits (90d) | 145 | 4.1k |
| Releases (6m) | 10 | 10 |
| Downloads (30d, npm + PyPI) | — | 3.1M |
| Overall score | 0.6636992956489073 | 0.8983723604743185 |
Pros
- +提供可视化的图形控制流，让智能体行为更加透明和可调试，相比黑盒式的自主智能体更易于理解和维护
- +内置人机协作机制和长期记忆支持，适合处理需要人工介入或持续状态的复杂业务流程
- +CLI 工具和预构建智能体模板显著降低了入门门槛，支持从概念验证到生产部署的快速迭代
- +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
- -作为低级框架需要更多的架构设计工作，学习曲线相对陡峭，不如高级抽象框架那样开箱即用
- -主要依赖 LangChain 生态系统，在非 LangChain 技术栈中的集成可能需要额外的适配工作
- -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
- •构建需要人工审核和批准的自动化工作流，如内容审核、财务审批或合规检查流程
- •开发具有长期记忆的客服或助理智能体，能够跨会话保持上下文和用户偏好
- •创建复杂的数据处理管道，需要在多个 AI 模型和外部 API 之间协调执行任务
- •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, langgraph or Mastra?
- Mastra has more GitHub stars (28,525 vs 3,333).
- Which is more actively developed, langgraph or Mastra?
- Mastra had more commits in the last 90 days (4,109 vs 145).
- Should I use langgraph or Mastra?
- Compare their capabilities, limitations and "best for" notes above. Trying each on a small task is the fastest way to decide.