Agentes de IA
kuberna-labs
Build, deploy, and monetize autonomous agents across Web3.
★ 51 estrellasMIT
Ver la guía →labs-OO-Agents es un proyecto de agentes de IA de código abierto de NVIDIA-NeMo (NVIDIA-NeMo/labs-OO-Agents en GitHub). Tiene 2.270 estrellas en GitHub y 306 bifurcaciones, se publica bajo la licencia NOASSERTION y se actualizó por última vez en GitHub el 7 de octubre de 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.
A visual all-in-one platform for building, debugging, and deploying AI agents.
Los demás proyectos de Agentes de IA con más estrellas del catálogo de LibreAI: la lista realista de alternativas si labs-OO-Agents no es lo que buscas.
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The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Proyectos de Agentes de IA cercanos en el catálogo, los más próximos en estrellas a labs-OO-Agents: sigue explorando a ambos lados.
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.
Unla MCP Gateway: turn existing APIs and MCP servers into MCP endpoints with zero code changes.
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.
Gratis y abierto: consigue el código fuente, dale una estrella al repositorio y empieza a crear.