Course
Multi-Agent Systems with LangGraph
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Build AI Agents with LangGraph
Design and build your own agents with LangGraph! LangGraph is a core part of the LangChain ecosystem, and it's used to build production-ready AI agents with a high degree of customizability. LangGraph allows developers to build agents as graphs with nodes and edges, which allows information flow and decision pathways to be carefully mapped out, and reduces the room for unexpected errors to creep in.Explore Emerging Multi-Agent Architectures
Since the rise of AI agents, a handful of agentic design patterns have emerged, and you'll learn about two of the most popular: network (or decentralized) multi-agents and supervisor multi-agents. Manage multiple agents effectively by designing a supervisor agent to delegate tasks and encourage collaboration between the worker agents.Create Your Own Agentic Assistant
You'll use LangGraph to build an agentic assistant to gather information and stock performance data on Fortune 500 companies, and analyze it using visualizations!You'll see this agent progress from a simple single-agent system to a three-agent supervisor multi-agent! Join the growing number of AI builders and learn to design and build AI agents today!
Prerequisites
Designing Agentic Systems with LangChainAgents as Graphs
LangGraph Multi-Agents
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FAQs
What prior experience do I need for this course?
This is an advanced course. You should have experience with LangChain, the OpenAI API, prompt engineering, embeddings, and intermediate Python.
What will I build in this course?
You will build an agentic assistant that gathers information and stock performance data on Fortune 500 companies and analyzes it with visualizations.
What multi-agent architectures does this course cover?
You will learn swarm (decentralized) multi-agent systems and supervisor multi-agent architectures, both representing current approaches in AI agent design.
How does LangGraph relate to LangChain?
LangGraph is a core part of the LangChain ecosystem designed specifically for building production-ready AI agents with a high degree of customizability.
How is the course structured?
The course has 2 chapters and 13 exercises. Most learners complete it in about 2 hours.
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