AI Agents
kuberna-labs
Build, deploy, and monetize autonomous agents across Web3.
★ 51 starsMIT
View guide →NVIDIA's object-oriented, Pythonic approach to building AI agents.
labs-OO-Agents is an open-source ai agents project by NVIDIA-NeMo (NVIDIA-NeMo/labs-OO-Agents on GitHub). It has 2,270 GitHub stars and 306 forks, is released under the NOASSERTION license, and was last updated on GitHub on 7 October 2026.
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.
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