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Basic Information

LazyLLM is a low-code development tool designed to help developers assemble, deploy, and iterate on multi-agent applications built around large language models. It provides a workflow oriented to rapid prototyping followed by data-driven feedback and iterative optimization, enabling teams to build chatbots, retrieval augmented generation systems, story writers, multimodal assistants, and other AI services using composable modules. The project unifies local and online model usage, supports both inference and fine-tuning, and exposes building blocks such as Components, Modules, and Flows to describe data streams and execution. It includes ready examples and high-level abstractions for document management, retrievers, rerankers, formatters, and web interfaces so users can create complex multi-agent pipelines with minimal boilerplate.

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App Details

Features
LazyLLM offers a component and module model for assembling agents with predefined Flows like pipeline, parallel, diverter, warp, if, switch, and loop to manage data streams. It provides one-click deployment and image packaging for POC and production, cross platform execution across bare metal, Slurm, SenseCore and clouds, and launcher abstractions for local or remote scheduling. The repo supports RAG primitives such as Document, Retriever, and Reranker, web modules for chat and document UIs, and trainable modules for fine tuning. It also includes grid search for hyperparameter and model configuration, automatic selection of fine-tuning and inference frameworks, and compatibility with local frameworks like collie, peft, lightllm, vllm and online services for inference and embeddings.
Use Cases
LazyLLM reduces engineering overhead by providing reusable building blocks, registration and launcher mechanisms, and a consistent interface that lets developers switch between local and online models without rewriting code. It accelerates prototyping by enabling rapid assembly of multi-agent workflows and simplifies bad case analysis and iterative experiments through integrated fine tuning, grid search and automated framework selection. Novice users can create production value applications without deep infrastructure knowledge, while experienced researchers can customize modules and integrate advanced tools. The platform also streamlines deployment and scaling via one-click packaging and platform agnostic execution, helping teams move from proof of concept to deployed services faster.

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