ИИ-агенты
future-agi
One open platform to simulate, evaluate, guardrail, trace, and optimise LLM and agent apps, self-hostable.
★ 2,1 тыс. звёздApache-2.0
Смотреть руководство →LazyLLM — проект с открытым исходным кодом из категории «ИИ-агенты» от LazyAGI (LazyAGI/LazyLLM на GitHub). У него 3 887 звёзд на GitHub и 424 форка, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 24 сентября 2026.
A low-code tool for building multi-agent LLM apps — chatbots, RAG and fine-tuning in one flow.
Decide the first concrete outcome you want from it, so you can pick the right example, template or mode instead of exploring everything at once.
Clone the repository (or install the published package) and skim its README quick start for the requirements for your platform.
Run the documented install step and set any required configuration — such as provider keys, data sources or local services — before the first run.
Execute the smallest documented example end to end, confirm the output is what you expected, then adapt it to your real data or workflow.
Built-in data flow and functional modules let even newcomers assemble multi-agent applications quickly.
A lightweight gateway starts all submodule services (LLM, embedding and more) without manual URL wiring for POCs.
Examples and components for chat interfaces, document management and model configuration via environment variables or ~/.lazyllm/config.json.
Ready-made retrieval-augmented generation flows with document handling and local or online model deployment.
Prototype → data feedback → iterative optimisation loop, with fine-tuning support at critical stages.
The same iterated algorithms can move to production with multi-user, fault-tolerance and high-concurrency support.
Get the source so you can inspect, configure and run it locally.
git clone https://github.com/LazyAGI/LazyLLM.git
cd LazyLLMInstall the base package, or the full dependency set for all features.
pip3 install lazyllm
# full install: pip3 install lazyllm && lazyllm install fullSet a provider key (or use a locally deployed model via vLLM/LightLLM), then run a chatbot or RAG example from the docs.
export LAZYLLM_OPENAI_API_KEY=your-key # see README for the exact variable for your providerIt is maintained by LazyAGI on GitHub (LazyAGI/LazyLLM), where the source, issues and releases live.
Yes — it is free and open source under Apache-2.0, which permits use, modification and commercial redistribution, with licence/notice retention.
It depends — hosted models need that provider's API key, while locally deployed open models via vLLM or LightLLM need no paid key.
Compared with LangChain or LlamaIndex used directly, LazyLLM's distinguishing focus is: a low-code tool for building multi-agent LLM apps — chatbots, RAG and fine-tuning in one flow. Pick it when that focus matches your need; the alternative may suit you better if its broader or different scope is the priority.
Clone https://github.com/LazyAGI/LazyLLM (or install its published package), follow the README quick start to install and configure it, then run the smallest documented example — the Run section on this page mirrors those steps.
One open platform to simulate, evaluate, guardrail, trace, and optimise LLM and agent apps, self-hostable.
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