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