Can Google's ADK Replace LangChain and MCP? (with Christina Lin)

About this episode

The episode discusses the potential of Google's ADK in building AI systems and its implications for agent pipelines compared to existing frameworks.

How do you build systems with AI? Not code-generating assistants, but production systems that use LLMs as part of their processing pipeline. When should you chain multiple agent calls together versus just making one LLM request? And how do you debug, test, and deploy these things? The industry is clearly in exploration mode—we're seeing good ideas implemented badly and expensive mistakes made at scale. But Google needs to get this right more than most companies, because AI is both their biggest opportunity and an existential threat to their search-based business model. Christina Lin from Google joins us to discuss Agent Development Kit (ADK), Google's open-source Python framework for building agentic pipelines. We dig into the fundamental question of when agent pipelines make sense versus traditional code, exploring concepts like separation of concerns for agents, tool calling versus MCP servers, Google's grounding feature for citation-backed responses, and agent memory management. Christina explains A2A (Agent-to-Agent), Google's protocol for distributed agent communication that could replace both LangChain and MCP. We also cover practical concerns like debugging agent…

People in this episode

Host: Kris Jenkins

Guest: Christina Lin

Topics covered

• AI systems
• agent pipelines
• debugging
• production deployment
• Google ADK
• LLMs

Keywords

• AI
• agent systems
• debugging
• production
• Google ADK
• LangChain
• MCP
• LLMs
• agent communication
• evaluation strategies

Mentioned in this episode

Organizations: Google

Products: Agent Development Kit (ADK), LangChain, MCP

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