KI-Agenten
kuberna-labs
Build, deploy, and monetize autonomous agents across Web3.
★ 51 SterneMIT
Anleitung ansehen →labs-OO-Agents ist ein Open-Source-Projekt aus der Kategorie KI-Agenten von NVIDIA-NeMo (NVIDIA-NeMo/labs-OO-Agents auf GitHub). Es hat 2.270 Sterne auf GitHub und 306 Forks, wird unter der Lizenz NOASSERTION veröffentlicht und wurde zuletzt am 7. Oktober 2026 auf GitHub aktualisiert.
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.
Die anderen Projekte der Kategorie KI-Agenten mit den meisten Sternen im LibreAI-Katalog — die realistische Auswahl, falls labs-OO-Agents nicht das Richtige ist.
A personal AI that really does things — on any OS, with your data staying yours.
An agentic skills framework & software development methodology that works.
Agent harness optimization system with skills, memory and security for Claude Code, Codex and more
The agent that grows with you — a Python framework for adaptive AI agents.
DeepSeek's plugin-based agent harness where everything is a plugin.
Verwandte Projekte der Kategorie KI-Agenten im Katalog, die labs-OO-Agents bei den Sternen am nächsten stehen — zum Weiterstöbern auf beiden Seiten.
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.
Kostenlos und offen — hol dir den Quellcode, gib dem Repository einen Stern und leg los.