Which AI Agent Framework Wins? LangGraph vs CrewAI vs AutoGen vs OpenAI Agents SDK

This video compares four major AI agent frameworks—LangGraph, CrewAI, AutoGen (AG2), and OpenAI Agents SDK—by building an identical AI Stock Research Team in each. The comparison evaluates developer experience, code complexity, performance, and suitability for different use cases. Each framework offers distinct trade-offs between flexibility, ease of use, and control over agent behavior.

Key Points

• •LangGraph provides low-level control and flexibility for complex agent workflows with explicit state management and graph-based execution
• •CrewAI offers high-level abstractions with role-based agents, making it ideal for rapid prototyping and team-based agent systems
• •AutoGen (AG2) excels at multi-agent conversations and group chat scenarios with built-in support for agent-to-agent communication
• •OpenAI Agents SDK is tightly integrated with OpenAI models and provides the simplest API for straightforward agent tasks
• •Framework selection depends on use case: LangGraph for complex workflows, CrewAI for team coordination, AutoGen for multi-agent conversations, OpenAI SDK for simplicity
• •Code complexity varies significantly—OpenAI SDK requires least code, while LangGraph requires more explicit setup but offers maximum control
• •Performance and latency characteristics differ across frameworks based on architecture and overhead
• •Developer experience ranges from beginner-friendly (CrewAI, OpenAI SDK) to advanced (LangGraph)
• •Each framework has different strengths in error handling, debugging, and monitoring capabilities
• •Production readiness and community support vary—consider ecosystem maturity when choosing a framework

Workflow Diagram

Concepts