ИИ-агенты
agent-native
A framework for building agentic apps
★ 7,0 тыс. звёздUnknown
Смотреть руководство →labs-molt — проект с открытым исходным кодом из категории «ИИ-агенты» от NVIDIA-NeMo (NVIDIA-NeMo/labs-molt на GitHub). У него 1 171 звезда на GitHub и 113 форков, он распространяется по лицензии Apache-2.0 и последний раз обновлялся на GitHub 7 октября 2026.
A lean, HuggingFace-native RL framework for agentic research.
Choose one real workload to test the project against.
Clone it locally and read through the getting-started guide.
Install the stack it needs and set any environment variables.
Execute the starter example and check results before going further.
Reinforcement learning designed for agent training.
Built on the HF ecosystem.
9k lines — readable and hackable.
FSDP and parallelism support.
From NVIDIA's NeMo team.
Permissive licensing.
Download the source and move into the project folder.
git clone https://github.com/NVIDIA-NeMo/labs-molt.git
cd labs-moltSet up a Python environment with the training dependencies.
pip install -r requirements.txt
# check README for torch/CUDA requirementsRun a training example per the README.
# needs GPU — see README for the example configsIt is maintained by NVIDIA-NeMo at NVIDIA-NeMo/labs-molt, with 113 forks.
Yes. It is released under the Apache-2.0 license, which permits commercial use, modification, and redistribution. Read the LICENSE file for the exact terms.
No — but RL training needs serious GPU compute, and that is the real cost.
Compare on simplicity and HuggingFace integration — its 9k-line scope is the differentiator.
Check the GPU requirements, install dependencies, and follow the training examples.
A framework for building agentic apps
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Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если labs-molt вам не подходит.
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Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к labs-molt — продолжайте изучать полку по обе стороны от него.
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Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.