agency vs langgraph
Side-by-side comparison of two AI agent tools
agencyopen-source
🕵️♂️ Library designed for developers eager to explore the potential of Large Language Models (LLMs) and other generative AI through a clean, effective, and Go-idiomatic approach.
langgraphopen-source
Build resilient language agents as graphs.
Metrics
| agency | langgraph | |
|---|---|---|
| Stars | 505 | 28.0k |
| Star velocity /mo | -7.5 | 2.5k |
| Commits (90d) | — | — |
| Releases (6m) | 0 | 10 |
| Overall score | 0.24332300518156355 | 0.8081963872278098 |
Pros
- +纯Go实现提供卓越性能和类型安全，无需Python或JavaScript依赖
- +支持清洁架构原则，业务逻辑与实现分离，代码可维护性高
- +易于扩展的接口设计，可创建自定义操作并组合成复杂AI流程
- +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
- -相对较新的库，GitHub星数较少(506)，社区规模有限
- -Go生态系统中AI库相对稀缺，可能缺乏一些成熟Python库的高级功能
- -文档和示例相对有限，学习资源可能不如主流AI库丰富
- -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
- •构建高性能的AI聊天机器人和对话系统
- •开发复杂的数据分析和处理管道，利用LLM进行智能分析
- •创建自主AI代理系统，实现多步骤推理和决策流程
- •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