AgentScope vs langgraph

Side-by-side comparison of two AI agent tools

Short answer

  • AgentScope is growing faster: +1,829 GitHub stars in the last 30 days vs +99 for langgraph.
  • Pick AgentScope for: build and run agents you can see, understand and trust. Pick langgraph for: framework to build resilient language agents as graphs.

From GitHub data refreshed daily.

AgentScopeopen-source

Build and run agents you can see, understand and trust.

langgraphopen-source

Framework to build resilient language agents as graphs.

Metrics

AgentScopelanggraph
Stars32.7k3.3k
Star velocity /mo1.8k98.52631578947368
Commits (90d)304145
Releases (6m)1010
Downloads (30d, npm + PyPI)296.7K—
Overall score0.82942033818210880.6636992956489073

Pros

  • +Production-ready with multiple deployment options including local, serverless, and Kubernetes with built-in observability
  • +Comprehensive built-in features including ReAct agents, memory, planning, voice interaction, and model finetuning capabilities
  • +Flexible multi-agent orchestration through message hub architecture with support for complex workflows and agent communication
  • +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

  • -Python-only framework limits usage for teams working in other programming languages
  • -Requires Python 3.10+ which may not be compatible with all existing environments
  • -As a comprehensive framework, may have a steeper learning curve compared to simpler agent libraries
  • -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

  • •Building production AI agent systems that require transparency, debugging capabilities, and human oversight
  • •Developing multi-agent workflows where agents need to collaborate, communicate, and orchestrate complex tasks
  • •Creating conversational AI applications with realtime voice interaction and custom model finetuning requirements
  • •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 AgentScope DetailsView langgraph Details

FAQ

Which is more popular, AgentScope or langgraph?
AgentScope has more GitHub stars (32,703 vs 3,333).
Which is more actively developed, AgentScope or langgraph?
AgentScope had more commits in the last 90 days (304 vs 145).
Should I use AgentScope 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.