Complete Guide to Building Multi-Agent workflows in Langgraph: Network and Supervisor Agents

Author(s): Aayushi_Sharma

Originally published on Towards AI.

“What if your AI agents could collaborate like a team of experts, rather than working in silos?”

In the evolving world of AI development, agent collaboration is fast becoming the next frontier. Imagine building workflows where each agent specializes in a task — one scours the internet for facts, another visualizes insights, and yet another ties everything together into a clear report. This is no longer science fiction — thanks to LangGraph, it’s just Python code.

This article dives into the concept of multi-agent workflows using LangGraph, outlining the benefits of modularity, specialization, and control that come from using multiple simpler agents rather than a monolithic agent. It explains different architectures, such as Network and Supervisor models, to efficiently orchestrate tasks among agents, illustrating the design process with practical examples like a Researcher Agent and a Chart Generator Agent. Overall, the piece emphasizes how adopting such frameworks can significantly enhance AI application development.

Read the full blog for free on Medium.

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Published via Towards AI