via MarkTechPost
NVIDIA AI Releases NOOA: An Object-Oriented Python Framework That Turns an AI Agent Into a Single Python Class
NVIDIA Labs has open-sourced NOOA (NVIDIA Object-Oriented Agents), a model-agnostic Python framework designed to streamline AI agent development. Traditionally, building an agent involves juggling prompt templates, tool schemas, callback hooks, and workflow graphs. NOOA consolidates these elements into a single Python class, where methods define the actions an agent can take, fields represent agent state, docstrings serve as prompts, and type annotations enforce runtime contracts. A method with an ellipsis (`...`) body is completed at runtime by an LLM-driven loop, while methods with normal bodies execute as deterministic Python. This unified interface allows developers and models to share a common ground, making agent behavior testable, traceable, refactorable, and version-controlled like any conventional software.
## Performance and Efficiency
In benchmarking, NOOA achieves impressive results: 82.2% on SWE-bench Verified, 86.8% on CyberGym L1, and a mean RHAE of 85.1% on ARC-AGI-3. Notably, it accomplishes this while using roughly half the tokens consumed by the open harnesses it was compared against, highlighting its efficiency.
## Deployment and Safety
NOOA is deployable, but with a critical caveat: it requires OS-level isolation. The framework is released under the Apache 2.0 license and can be installed via `pip install nooa` (v0.0.8, released July 30, 2026). It requires Python 3.12–3.13. On PyPI, it is classified as alpha status, and NVIDIA describes it as a research preview. Since agents can execute LLM-generated code, NVIDIA explicitly states that its AST checks and module deny-lists are defense-in-depth guardrails—not a containment boundary. The actual containment boundary must be a container, a VM, or NVIDIA OpenShell. Models are pluggable through LiteLLM, supporting hosted APIs, Ollama, and vLLM endpoints.
As the AI landscape evolves into 2026, NOOA represents a significant step toward making agent development more accessible and robust, aligning with the industry's push for modular, maintainable AI systems.