AI Agent 四大框架白話拆解：AutoGen、AgentScope、CAMEL、LangGraph，框架驅動開發實踐｜Hello-Agents EP6

This episode compares four major AI agent frameworks—AutoGen, AgentScope, CAMEL, and LangGraph—breaking down their architecture and practical implementation approaches. Rather than building agents from scratch, the content focuses on leveraging existing frameworks to accelerate development. The episode builds on previous content about visual agent composition, transitioning from no-code to framework-driven development practices.

Key Points

• •Four major frameworks (AutoGen, AgentScope, CAMEL, LangGraph) provide different abstractions for agent development
• •Framework-driven development allows building on established patterns rather than implementing core agent logic from scratch
• •Each framework has distinct architectural approaches to agent communication, state management, and task orchestration
• •Practical implementation focuses on leveraging framework capabilities for faster agent deployment
• •Understanding framework differences helps select the right tool for specific use cases
• •Transition from visual/no-code agent building to code-based framework development increases flexibility
• •Frameworks abstract away common agent patterns like multi-turn conversations, tool integration, and agent coordination
• •Framework selection impacts development speed, scalability, and feature availability

Workflow Diagram

Concepts