99 | AI Agent Frameworks Explained | LangChain vs LangGraph vs ADK | Build AI Agents

This video compares three major AI agent frameworks: Google ADK, LangChain, and LangGraph. It explains the architectural differences, use cases, and capabilities of each framework for building AI agents. The content helps developers choose the right framework based on their specific needs and project requirements.

Key Points

• •Google ADK (Agent Development Kit) is Google's framework designed for building production-ready AI agents with built-in integrations
• •LangChain is a flexible framework that provides chains, agents, and memory management for LLM applications with extensive tool integration
• •LangGraph extends LangChain by adding stateful graph-based workflows, enabling complex multi-step agent orchestration
• •ADK focuses on structured agent patterns with predefined workflows, while LangChain offers more flexibility for custom implementations
• •LangGraph's graph-based approach allows for better control flow, conditional routing, and parallel execution of agent tasks
• •Choose ADK for rapid deployment with Google Cloud integration, LangChain for flexibility and community support, LangGraph for complex multi-agent systems
• •All three frameworks support tool calling, memory management, and integration with various LLM providers
• •LangGraph's state management and cycle detection prevent infinite loops in agent execution
• •Framework selection depends on project complexity, team expertise, and infrastructure preferences (Google Cloud vs. open-source)

Workflow Diagram

Concepts