ИИ-агенты
kuberna-labs
Build, deploy, and monetize autonomous agents across Web3.
★ 51 звездаMIT
Смотреть руководство →labs-OO-Agents — проект с открытым исходным кодом из категории «ИИ-агенты» от NVIDIA-NeMo (NVIDIA-NeMo/labs-OO-Agents на GitHub). У него 2 270 звёзд на GitHub и 306 форков, он распространяется по лицензии NOASSERTION и последний раз обновлялся на GitHub 7 октября 2026.
NVIDIA's object-oriented, Pythonic approach to building AI agents.
Start with one concrete task rather than a grand architecture.
Clone the repo and check the examples folder for a quickstart.
Install packages, set environment variables, and point it at your model.
Execute the demo workflow, then adapt it to your own data.
Agents as classes with state and behavior.
Idiomatic Python design.
Experimental work from the NeMo team.
Build complex agents from objects.
Pushed in October 2026.
Study NVIDIA's agent design idioms.
Download the source and move into the project folder.
git clone https://github.com/NVIDIA-NeMo/labs-OO-Agents.git
cd labs-OO-AgentsCreate a virtual environment and install the requirements.
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txtTreat these as starting points - the README documents the exact commands.
python examples/quickstart.pyNVIDIA-NeMo/labs-OO-Agents is its home on GitHub, with 306 forks so far. The contributors tab and recent commits show how actively it is maintained.
GitHub does not assert a license for this repo, so treat the code as all rights reserved until the author adds one.
The framework itself is free - but you will need a model to drive it, which usually means your own API key or a local model.
Most frameworks are functional or graph-based; this one is explicitly object-oriented. If OO design clicks for you, it may be the most natural fit - otherwise pick by ecosystem.
Follow the README quickstart, then browse the examples directory for patterns that match what you want to build.
Build, deploy, and monetize autonomous agents across Web3.
NVIDIA's reference stack for running AI agents more securely inside OpenShell sandboxes
A team workspace for building AI agents together.
Python library for building multimodal language agents — VLM, video and mobile, fast to prototype and ship
Guardrails for building secure, robust agents.
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Другие проекты из категории «ИИ-агенты» с наибольшим числом звёзд в каталоге LibreAI — реалистичный список альтернатив, если labs-OO-Agents вам не подходит.
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Соседние проекты из категории «ИИ-агенты» в каталоге, ближайшие по числу звёзд к labs-OO-Agents — продолжайте изучать полку по обе стороны от него.
Self-managing, unbounded context and long-term memory for coding agents: one session, for life.
Agno's self-learning data agent: grounded text-to-SQL answers over your database that improve with use.
A memory agent for your agent fleet: it curates, reconciles, and expires shared memories across tools.
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Infisical's credential vault and HTTP proxy, so AI agents call APIs without ever holding your real secrets.
Self-hosted AI vulnerability research: agents find, validate, deduplicate, and rank real security issues.
Бесплатно и открыто — забирайте исходный код, ставьте звезду репозиторию и начинайте создавать.