langgraph vs notte
Side-by-side comparison of two AI agent tools
langgraphopen-source
Build resilient language agents as graphs.
nottefree
🌸 Best framework to build web agents, and deploy serverless web automation functions on reliable browser infra.
Metrics
| langgraph | notte | |
|---|---|---|
| Stars | 28.0k | 1.9k |
| Star velocity /mo | 2.5k | 22.5 |
| Commits (90d) | — | — |
| Releases (6m) | 10 | 10 |
| Overall score | 0.8081963872278098 | 0.6496321767514388 |
Pros
- +Durable execution ensures agents automatically resume from exactly where they left off after failures or interruptions
- +Comprehensive memory system with both short-term working memory for ongoing reasoning and long-term persistent memory across sessions
- +Seamless human-in-the-loop capabilities allow for inspection and modification of agent state at any point during execution
- +混合架构设计通过脚本化确定性操作、仅在复杂场景使用 AI 的方式实现 50%+ 成本降低
- +提供完整的 web 自动化生态系统，包含隐身浏览器、CAPTCHA 解决、代理支持和企业级凭证管理
- +支持结构化数据输出和 Playwright 兼容接口，兼顾易用性和专业开发需求
Cons
- -Low-level framework requires more technical expertise and setup compared to high-level agent builders
- -Graph-based agent design paradigm may have a steeper learning curve for developers new to agent orchestration
- -Production deployment complexity may be overkill for simple chatbot or single-turn use cases
- -高级功能（隐身浏览器、密钥保险库、数字身份）需要付费 API 服务，增加了成本考量
- -作为相对较新的框架，生态系统和社区支持可能不如成熟的传统自动化工具
- -需要同时掌握传统脚本编程和 AI 代理概念，学习曲线相对陡峭
Use Cases
- •Long-running autonomous agents that need to persist through system failures and operate over days or weeks
- •Complex multi-step workflows requiring human oversight, approval, or intervention at specific decision points
- •Stateful agents that must maintain context and memory across multiple sessions and interactions
- •电商价格监控和库存管理自动化，需要处理各种反爬虫机制和验证码
- •社交媒体账号批量管理和内容发布，需要数字身份和自动化 2FA 支持
- •企业级数据采集和竞品分析，要求高可靠性和成本控制的长期运行