E11: LangGraph vs CrewAI vs AutoGen vs Google ADK: Choosing a Multi-Agent Framework

This episode compares four major multi-agent frameworks—LangGraph, CrewAI, AutoGen, and Google ADK—highlighting how each encodes a different mental model for agent orchestration. Rather than focusing on feature lists, the analysis emphasizes how these underlying philosophies shape framework design, capabilities, and use cases. Understanding these mental models is crucial for selecting the right framework for specific multi-agent applications.

Key Points

• •Each multi-agent framework is built on a distinct mental model that fundamentally shapes its architecture and capabilities
• •Mental models matter more than feature checklists when evaluating frameworks for your specific use case
• •LangGraph emphasizes explicit state management and graph-based control flow for complex agent workflows
• •CrewAI focuses on role-based agent teams with hierarchical task execution and crew-level coordination
• •AutoGen prioritizes conversational patterns and multi-turn interactions between heterogeneous agents
• •Google ADK provides a different approach to agent development, likely emphasizing Google's ecosystem integration
• •Framework selection should align with your application's primary interaction pattern (state-driven, role-based, conversational, or ecosystem-specific)
• •Understanding the underlying philosophy helps predict how each framework will handle edge cases and scaling challenges
• •Different frameworks excel at different problem domains based on their encoded mental models
• •Hybrid approaches may be necessary when requirements span multiple mental models

Workflow Diagram

Concepts