LangGraph Deep Dive: Build Better Agents
LangGraph is an agent framework from LangChain that allows us to develop agents via graphs. By building agents using graphs we have much more control and flexibility in our AI agent execution path. In this video, we will build an AI research agent using LangGraph. Research agents are multi-step LLM agents that can produce in-depth research reports on a topic of our choosing through multiple steps. We will see how we can build our own AI research agent using gpt-4o, Pinecone, LangGraph, arXiv, and Google via the SerpAPI. 📌 Code: https://colab.research.google.com/github/pinecone-io/examples/blob/master/learn/generation/langchain/langgraph/01-gpt-4o-research-agent.ipynb 📖 Article: https://www.pinecone.io/learn/langgraph-research-agent/ 🌟 Build Better Agents + RAG: https://platform.aurelio.ai (use "JBMARCH2025" coupon code for $20 free credits) 👾 Discord: https://discord.gg/c5QtDB9RAP Twitter: https://twitter.com/jamescalam LinkedIn: https://www.linkedin.com/in/jamescalam/ #artificialintelligence #langchain #llm #python #rag 00:00 LangGraph Agents 02:04 LangGraph Agent Overview 04:46 Short History of Agents and ReAct 07:58 Agents as Graphs 10:18 LangGraph 12:41 Research Agent Components 14:30 Building the RAG Pipeline 17:28 LangGraph Graph State 18:56 Custom Agent Tools 19:10 ArXiv Paper Fetch Tool 21:22 Web Search Tool 22:42 RAG Tools 23:57 Final Answer Tool 25:10 Agent Decision Making 30:16 LangGraph Router and Nodes 33:00 Building the LangGraph Graph 36:52 Building the Research Agent Report 39:39 Testing the Research Agent 43:42 Final Notes on Agentic Graphs
