Which is more popular, langgraph or OpenHuman?

OpenHuman has more GitHub stars (40,447 vs 3,332).

Which is more actively developed, langgraph or OpenHuman?

OpenHuman had more commits in the last 90 days (22,600 vs 135).

Should I use langgraph or OpenHuman?

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.

langgraph vs OpenHuman

Side-by-side comparison of two AI agent tools

Short answer

OpenHuman is growing faster: +3,180 GitHub stars in the last 30 days vs +99 for langgraph.
Pick langgraph for: framework to build resilient language agents as graphs. Pick OpenHuman for: openHuman is the fastest, cheapest, most efficient open-source agent harness.

From GitHub data refreshed daily.

langgraphopen-source

Framework to build resilient language agents as graphs.

OpenHumanopen-source

OpenHuman is the fastest, cheapest, most efficient open-source agent harness. Written in Rust

Metrics

	langgraph	OpenHuman
Stars	3.3k	40.4k
Star velocity /mo	98.88888888888889	3.2k
Commits (90d)	135	22.6k
Releases (6m)	10	10
Overall score	0.6740823209830771	0.9408550749378012

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

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

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

View langgraph Details View OpenHuman Details

FAQ

Which is more popular, langgraph or OpenHuman?: OpenHuman has more GitHub stars (40,447 vs 3,332).
Which is more actively developed, langgraph or OpenHuman?: OpenHuman had more commits in the last 90 days (22,600 vs 135).
Should I use langgraph or OpenHuman?: 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.