LazyLLM is a Python-based framework for building multi-agent LLM applications with low-code tooling, focusing on prototype-to-production workflow, one-click deployment, and cross-platform compatibility.
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What it is
LazyLLM is a low-code development tool for building multi-agent large language model applications. It supports an application-building workflow that goes from prototype to data-driven optimization, and offers features for one-click deployment, cross-platform compatibility, and support for fine-tuning and inference across local and online services.
How it works
The project provides modular components for assembling multi-agent workflows (pipelines, parallel structures, diversions, conditional logic, loops) and a deployment pathway that can package images and expose services via a gateway. It includes support for online and offline model services, automatic selection of fine-tuning/inference frameworks, and RAG components such as Document, Retriever, and Reranker. Examples show building chat bots and RAG-based bots using pipelines and modules from lazyllm, with WebModule serving as the deployment endpoint.
Getting started
Install options:
- "pip3 install lazyllm" (to install only the package and necessary dependencies)
- "pip3 install lazyllm" followed by "lazyllm install full" (to install all dependencies)
Installation from source:
git clone git@github.com:LazyAGI/LazyLLM.git
cd LazyLLM
pip install -r requirements.txt
Windows/macOS setup is referenced via a tutorial:
(See: https://docs.lazyllm.ai/zh-cn/stable/Home/environment)
The README provides code examples for starting chat modules, including:
import lazyllm
chat = lazyllm.OnlineChatModule()
lazyllm.WebModule(chat).start().wait()
And for local models with inference frameworks:
import lazyllm
chat = lazyllm.TrainableModule('internlm2-chat-7b')
lazyllm.WebModule(chat, port=23466).start().wait()
For quick CLI use when lazyllm is in PATH:
lazyllm run chatbot
Recent releases
Latest five releases (based on repository releases):
- v1.2.2 (2026-08-02)
- v1.2.1 (2026-07-31)
- v1.2.0a4 (2026-07-31)
- v1.2.0 (2026-07-31)
- v1.2.0a3 (2026-07-30)
Traction
Stars: 3860 Forks: 402 Open issues: 42
Architecture
The project presents an architecture diagram in docs/assets/Architecture.en.png and describes the design philosophy emphasizing rapid prototyping, uniform UX across platforms, and selective tool integration to balance quality and practicality.
Caveats
License: Apache-2.0 Created: 2024-06-04 Last push: 2026-08-04
Other notes:
- Language: Python
- Topics include agents, ai-agent, data, deep-learning, terminology around frameworks and databases, and integration with LangChain, llamaindex, and RAG components.