Which is more popular, Agency Swarm or langgraph?

Agency Swarm has more GitHub stars (4,588 vs 3,333).

Which is more actively developed, Agency Swarm or langgraph?

langgraph had more commits in the last 90 days (145 vs 104).

Should I use Agency Swarm or langgraph?

Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.

Agency Swarm vs langgraph

Side-by-side comparison of two AI agent tools

Short answer

Pick Agency Swarm for: reliable Multi-Agent Orchestration Framework. Pick langgraph for: framework to build resilient language agents as graphs.

From GitHub data refreshed daily.

Agency Swarmopen-source

Reliable Multi-Agent Orchestration Framework

langgraphopen-source

Framework to build resilient language agents as graphs.

Metrics

	Agency Swarm	langgraph
Stars	4.6k	3.3k
Star velocity /mo	73.57894736842105	98.52631578947368
Commits (90d)	104	145
Releases (6m)	10	10
Downloads (30d, npm + PyPI)	3.3K	—
Overall score	0.6399585851872038	0.6636992956489073

Pros

+基于OpenAI Agents SDK的生产就绪架构，确保稳定性和可扩展性
+完全控制代理提示和指令，实现精确的行为定制
+类型安全的工具系统和自动参数验证，减少运行时错误

+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

Cons

-依赖OpenAI API，可能产生持续的使用成本
-复杂多代理系统的调试和监控可能具有挑战性
-需要深入理解代理编排概念才能有效使用

-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

Use Cases

•构建企业级AI助手团队，如CEO、开发者、虚拟助理协作处理业务流程
•创建客户服务自动化系统，多个专业代理处理不同类型的询问和任务
•开发内容生成工作流，编排研究、写作、编辑代理完成复杂项目

•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

View Agency Swarm Details View langgraph Details

FAQ

Which is more popular, Agency Swarm or langgraph?: Agency Swarm has more GitHub stars (4,588 vs 3,333).
Which is more actively developed, Agency Swarm or langgraph?: langgraph had more commits in the last 90 days (145 vs 104).
Should I use Agency Swarm or langgraph?: Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.