A Complete Guide to Multi-Agent Systems in LangGraph: Network to Supervisor and Hierarchical Models

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

A Complete Guide to Multi-Agent Systems in LangGraph: Network to Supervisor and Hierarchical Models

In modern AI applications, we often expect systems to handle complex, multi-step tasks. Instead of relying on a single model to solve everything, we can divide tasks across multiple specialized agents that collaborate to deliver faster, smarter solutions.

The article explores the concept of multi-agent systems, explaining how tasks can be effectively managed by using multiple specialized agents that communicate with each other, either directly or through a supervisory agent. It discusses different design patterns such as collaborative network patterns and supervisor patterns, their applications in scenarios like travel planning, and the benefits of proper agent handoffs for seamless transitions. Additionally, it provides practical implementation examples of workflow architectures utilizing LangGraph, emphasizing the advantages of choosing the right patterns based on the complexity and size of the tasks.

Read the full blog for free on Medium.

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Published via Towards AI