developer vs langgraph
Side-by-side comparison of two AI agent tools
developeropen-source
the first library to let you embed a developer agent in your own app!
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| developer | langgraph | |
|---|---|---|
| Stars | 12.2k | 28.0k |
| Star velocity /mo | -22.5 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.22257543112778125 | 0.8081963872278098 |
Pros
- +极致灵活性 - 通过自然语言提示生成任何类型应用，不受预设模板限制，真正实现 'create-anything-app' 的愿景
- +人机协作工作流 - 支持增量式开发，可根据运行结果和错误信息持续优化提示，形成高效的迭代开发循环
- +高度可集成 - 提供库化接口，可轻松嵌入到现有开发工具链中，打造定制化的 AI 辅助开发环境
- +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
Cons
- -提示工程门槛 - 需要学会编写有效的提示来获得理想结果，对初学者可能存在学习曲线
- -代码质量波动 - 生成的代码质量依赖于 AI 模型能力和提示质量，可能需要人工审查和优化
- -环境依赖复杂 - 需要 Python 运行环境和 Poetry 包管理器，增加了部署和维护的复杂性
- -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
Use Cases
- •快速原型开发 - 产品经理或创业者可通过自然语言描述快速获得可演示的应用原型，加速产品验证流程
- •技术学习辅助 - 开发者可通过描述想要实现的功能来生成示例代码，作为学习新技术栈或框架的起点
- •定制开发工具 - 团队可将 smol developer 集成到现有的开发流程中，打造符合团队特色的 AI 辅助编程环境
- •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