LangGraph Tutorial: Building a Multi-Agent AI Research Assistant

Description

In this final Capstone Project, we bring together everything we've learned to build a sophisticated Multi-Agent Research Assistant! 🚀 Moving beyond simple chatbots, this video demonstrates how to architect a complex, stateful LLM system where multiple specialized agents collaborate to perform deep research. We use LangGraph for orchestration, FAISS for vector retrieval, and OpenAI for reasoning and generation. What we build in this project: 1. Router Agent: Uses an LLM to classify user intent and decide whether to trigger a research workflow or a chitchat response. 2. Research Agent: Performs semantic search on a FAISS Vector Database to retrieve relevant document chunks. 3. Summarizer Agent: Condenses high-volume retrieved data into clear, actionable summaries. 4. Writer Agent: Takes the summarized data and crafts a professional, user-facing final report. 5. Chitchat Agent: Handles casual conversation to ensure a seamless user experience. Key Technical Concepts Covered: - Multi-Agent Orchestration: Managing hand-offs between specialized AI nodes. - Stateful RAG: Maintaining retrieved context and summaries across the graph state. - Advanced Branching: Implementing conditional edges based on LLM routing decisions. - Vector DB Integration: Implementing local search using FAISS and OpenAI embeddings. By the end of this video, you will have a template for building production-grade agentic workflows that can be applied to real-world research, legal, or technical documentation tasks. Prerequisites: Python proficiency and an OpenAI API key. This is the final module—be sure to check out the previous videos in the series for foundational concepts! #LangGraph #AIAgents #FAISS #VectorDatabase #RAG #LangChain #GenerativeAI #PythonTutorial #MachineLearning #AIAssistant