multi-agent

Build Multi-Agent LLMs with LazyLLM

Name: Build Multi-Agent LLMs with LazyLLM
Author: LazyAGI

Get easy multi-agent LLM apps with LazyLLM, for founders, 3.8k+ GitHub stars

intermediate⏱ 1-2 hours💵 Free + LLM API costs

3,854 stars399 forksPythonQuality 8/10Updated 7/22/2026100% free · open source

What it is

Build multi-agent models using LazyLLM to create applications.

What you can make with it

Apps that can interact with multiple systems, like adding new customers to your Notion database and sending a welcome email from Resend when a new Stripe customer signs up.

How it helps

With LazyLLM, you can more easily build multi-agent applications without needing to handle the underlying complexity, saving time and reducing the learning curve for these AI models.

Real use case example

"A founder wants to automate a workflow where new GitHub contributors are added to a Slack channel and receive a welcome message. They use LazyLLM to create a multi-agent model that pulls contributor data from GitHub, adds it to the Slack channel, and sends the welcome message."

If you're new

For anyone new to building AI applications, LazyLLM is a good starting point to learn about multi-agent models and how to apply them.

If you're senior

A senior engineer or professional would use LazyLLM for its ease of use and flexibility when building complex multi-agent applications.

Common confusion cleared up

The most common confusion is that LazyLLM is just for simple LLM applications, but it's a powerful tool for building complex multi-agent models.

Best inside these AI tools

Claude DesktopCursorCodex CLIGemini Code Assist

Pairs with

Claude APIStripe webhookNotion database

Why we list it on WorkflowStacks: We're including LazyLLM because it's an easy and accessible way to build multi-agent LLM apps, making it a great fit for our marketplace of AI tools.

What it does

LazyLLM allows founders to easily build and deploy multi-agent large language model applications with minimal setup and configuration

Install / run

git clone https://github.com/LazyAGI/LazyLLM.git && cd LazyLLM

When to use it

•You need to build a conversational AI application with multiple agents interacting with each other
•You want to prototype and test multi-agent LLM ideas quickly without extensive coding
•You need a simple way to integrate multiple LLMs into a single application

Quick start

1Run the example application with `python examples/simple_app.py` to see a basic multi-agent LLM in action
2Modify the `config.json` file to customize the behavior of the agents and the application
3Use the `lazy_llm.Agent` class to create custom agents and integrate them into your application
4Run `python scripts/train.py` to fine-tune the LLM models used in your application
5Use the `lazy_llm.Application` class to deploy and manage your multi-agent LLM application

Ready-to-paste prompt

python examples/simple_app.py --prompt 'Discuss the implications of AI on modern society' --num_agents 3

Heads up: Make sure you have the required Python version (3.8 or later) and the necessary dependencies, including `transformers` and `torch`, installed before running LazyLLM

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