Apr 26
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It’s time to put everything we’ve learned into practice! 🚀 In this LangGraph Capstone Project, we are building a production-grade Autonomous Research Assistant using a multi-agent architecture. This project demonstrates how to orchestrate complex workflows where different AI agents collaborate to deliver a final result. The best part? We are using a 100% open-source and free-to-use tech stack. What we build in this project: 1. The Orchestrator (Router): The "brain" that directs traffic between research tasks and general chitchat. 2. The Research Agent: Connects to a FAISS Vector Database to retrieve relevant documents based on user queries. 3. The Summarizer Agent: Distills high-volume retrieved data into clear, concise insights. 4. The Writer Agent: Formats and polishes the final output into a professional report. Key Technologies Used: 1. LangGraph: For advanced multi-agent orchestration. 2. FAISS: For local, high-performance vector similarity search. 3. Open Source Embeddings: To keep the project free and accessible. By the end of this project, you will understand how to build stateful, multi-step AI systems that go far beyond simple chatbots. Let’s dive into the architecture and start coding! #LangGraph #AIAgents #FAISS #VectorDatabase #Python #GenerativeAI #MachineLearning #AICapstone #langchain We will discuss the following- langgraph multi agent langgraph agents langgraph project langgraph playlist langgraph and langchain langgraph studio langgraph ai agents langgraph multi agent tutorial langgraph chatbot langgraph multi agent project langgraph memory langgraph js