Build and orchestrate multi-agent AI applications with LazyLLM.
npx skills add https://github.com/LazyAGI/LazyLLM --skill lazyllm-skill
Please help me install this Agent Skill. Skill: lazyllm-skill Source: https://github.com/LazyAGI/LazyLLM/tree/main/docs/lazyllm-skill Command: npx skills add https://github.com/LazyAGI/LazyLLM --skill lazyllm-skill
LazyLLM provides a unified toolkit to build and orchestrate multi-agent AI applications, simplifying complex workflows that involve Flow, RAG, and Agent domains.
Start by loading the LazyLLM Skill and issuing a high-level instruction such as: "Create a multi-agent workflow that fetches docs, runs a plan, and answers a user question."
High-intent search queries and answers about installing and using this skill.
To build a multi-agent workflow, you can use LazyLLM for Flow orchestration and RAG systems to retrieve documents, plan tasks with agents, and synthesize responses for knowledge-based QA.
Yes, you can orchestrate parallel and branching data pipelines using LazyLLM Flow orchestration, which supports linear, branching, parallel, and loop pipelines for complex multi-agent workflows.
The best way to set up a RAG knowledge system is using LazyLLM to handle document retrieval and vectorization, enabling knowledge-based QA with integrated agent planning and response synthesis.
No external dependencies are required to start developing multi-agent systems; LazyLLM relies on standard Python tooling and its own scripts, references, and assets to implement and deploy solutions.
Yes, LazyLLM supports Agent development for both single and multi-agent systems, providing built-in tools, memory, planning capabilities, and web interfaces to streamline complex tasks.
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