How to Build Your First Multi-Agent AI System in Python and LangGraph

                     How to Build Your First Multi-Agent AI System in Python and LangGraph
               



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                     How to Build Your First Multi-Agent AI System in Python and LangGraph
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

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In this tutorial, I'll show you how to build a multi-agent AI system in Python with no orchestration framework. We'll also implement this in LangGraph with nodes, edges, and shared state.

The point of building both versions is to show you the difference between doing it with and without a framework.

The simple Python version shows how little code you actually need to build a multi-agent system. The LangGraph version shows what a workflow framework enables for building such systems.

The agents run locally with Ollama and Qwen so you'll have no API costs.

Table of Contents

• Background

• What is a Multi-Agent System?

• Single Agent vs Multi-Agent System

• Motivation and Architecture

• Step 1: Install Ollama and Dependencies

• Step 2: Simple Python Version

• Step 3: LangGraph Version with Nodes and Edges

• Sample Output

• Common Multi-Agent Patterns

• Conclusion

Background

Large language models are capable of solving surprisingly complex tasks with a single prompt. For many applications, that's exactly the right approach.

But as workflows grow, a single prompt often has to do too many things at once. Combining all of those responsibilities into one prompt can make it harder to maintain, extend, and reason about the problem, especially for a smaller local model.

A common solution is to break the work into smaller steps to create a multi-agent system instead of relying on one agent to perform all the tasks.

To follow this tutorial, you'll need Ollama installed on your machine and a free Ollama account. The tutorial works on macOS, Windows, and Linux. I'm using a MacBook Pro with 32 GB of RAM, but you can run this on a lower-memory machine by choosing a smaller Qwen model from Ollama.

What is a Multi-Agent System?

In this tutorial, a multi-agent system is simply a collection of AI agents that collaborate to complete a larger task.

Each agent has:

• a specific responsibility

• its own prompt and instructions

• a defined place in the workflow

Rather than asking one model to solve the entire problem, the workload is divided into smaller, focused tasks. Because each agent has a narrower objective, its prompt is typically simpler and easier for the model to follow consistently.

This tutorial intentionally keeps the system simple. There's no memory, tool calling, or complex patterns. Instead, the focus is on a simple use case to show the building blocks for a multi-agent AI system.

When to Use a Multi-Agent System

Multi-agent systems make sense when a task naturally breaks into distinct steps or roles, such as planning, writing, reviewing, or using different specialized prompts for different parts of the workflow. If single agent can handle the task well with a clear prompt and produce the output reliably, adding more agents can just introduce extra complexity, latency, and overhead.

In general, use multiple agents when separation of responsibilities clearly improves the result, and use a single agent when the task is still manageable as one coherent interaction.

Motivation and Architecture

In this tutorial, we'll build a simple AI-powered study guide generator using a small Qwen local LLM and Ollama. Given a topic in the prompt, the system produces a structured study guide that contains outline, notes, and review questions. A single agent prompt looks like this:

[code]Create a beginner-friendly study guide for

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