LazyAGILazyAGIOfficialยท1 Agent Skills Included

LazyLLM

Build multi-agent AI apps, RAG pipelines, and model fine-tuning fast

Builds multi-agent AI applications, RAG knowledge-base Q&A systems, and model fine-tuning pipelines with minimal Python code. Eliminates manual wiring of LLMs, retrievers, tools, and deployment servers through unified modules and one-command deployment. Lets developers switch between online and local models, orchestrate complex flows, and ship production AI apps faster.
npx skills add LazyAGI/LazyLLM --all -g -y
Available:

Instructs the AI agent on LazyLLM's layered architecture, mandatory pre-coding reading steps, lazy-loading rules, and approved code patterns so it modifies or extends the framework correctly.

All Skills in This Repository (1)

Pure Emerald Level Indicators

Frequently Asked Questions

FAQPage Schema
How to install LazyLLM?โ–ผ

Run `npx skills add LazyAGI/LazyLLM --all -g -y` in your terminal to install all skills in this suite globally.

What can you build with LazyLLM?โ–ผ

You can build chatbots, multi-agent systems, RAG knowledge-base Q&A apps, and fine-tuned model services using simple Python pipelines with one-command deployment.

Does LazyLLM support both online and local models?โ–ผ

Yes. OnlineModule connects to providers like OpenAI, Qwen, and DeepSeek, while TrainableModule deploys local models with vLLM or LMDeploy, both sharing the same calling interface.

How do I create an AI agent with tools in LazyLLM?โ–ผ

Register any function with the fc_register decorator, then pass it to a built-in agent like ReactAgent or PlanAndSolveAgent along with an LLM to start handling tool-calling tasks.

Can beginners use LazyLLM without deep AI expertise?โ–ผ

Yes. Its low-code design lets you assemble agents, RAG pipelines, and web interfaces from pre-built modules with just a few lines of Python.

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