May 6
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In this session, we move from theory to actual implementation. We are building a high-performance Multi-Agent System in LangGraph that leverages the unique strengths of different LLMs. You will learn how to orchestrate a Research Agent (Groq), a Writer Agent (Gemini), and a Critique Agent (Ollama/Llama) into a cohesive, iterative workflow. We cover everything from setting up API keys and defining TypedDict states to implementing conditional loops that ensure your AI output meets a satisfactory quality score. Timestamps: 0:00 – Introduction: Moving from Reflection to Multi-Agent Systems 1:14 – The Workflow: Research, Writer, Critique, and Improve Agents 2:30 – Step 1: Fetching API Keys for Groq and Google Gemini 4:10 – Step 2: Importing Libraries (TypedDict, StateGraph, LangChain) 6:20 – Creating the Agents: Initializing Groq and Gemini Objects 8:00 – The Critique Agent: Using Ollama and Gemini Backups 9:00 – Defining the AgentState for Structured Data 9:40 – Building the Nodes: Implementing the .invoke() method 12:45 – Designing the Decision Function and Conditional Edges 13:35 – Compiling the Graph: Creating an Executable Workflow 15:15 – Running the System: Quantum Computing Example 17:00 – Summary: The Power of Division of Intelligence in AI