LibreAI / Agentes de IA / NeMo-Agent-Toolkit
// agents · NVIDIA/NeMo-Agent-Toolkit

NeMo-Agent-Toolkit

NeMo-Agent-Toolkit é um projeto de agentes de IA de código aberto de NVIDIA (NVIDIA/NeMo-Agent-Toolkit no GitHub). Tem 2.661 estrelas no GitHub e 780 forks, é publicado sob a licença Apache-2.0 e foi atualizado pela última vez no GitHub em 8 de outubro de 2026.

NVIDIA's toolkit for connecting, profiling and optimizing teams of AI agents — across any framework

★ 2,7 mil estrelas⑂ 780 forksApache-2.0atualizado out 2026
▦ visual: o README deste projeto não inclui nenhuma captura incorporável,
então aqui vai um marcador. O guia abaixo está completo mesmo assim.
// dados do projeto

Dados do projeto

Estrelas no GitHub
2.661
Forks
780
Licença
Apache-2.0
Última atualização no GitHub
8 de outubro de 2026
Categoria
Agentes de IA
Tipo de ficha
Guia de instalação completo
Site oficial
Official site
// como funciona

Como funciona

  1. Clarify your goal

    Decide what you need: build a new workflow, or instrument, profile and evaluate an agent you already have in another framework.

  2. Get the toolkit

    pip install nvidia-nat, adding the extra for your framework; clone the repo if you want the examples.

  3. Install and configure

    Write a workflow.yml defining functions (tools), LLMs and the workflow agent, and set your model key (e.g. NVIDIA_API_KEY for NVIDIA NIMs from build.nvidia.com).

  4. Run your first workflow

    Run nat run --config_file workflow.yml --input "..." and inspect the result, then open the built-in UI to chat with and debug the workflow.

// recursos

Por que as pessoas gostam tanto

Framework agnostic

Works side-by-side with LangChain, LlamaIndex, CrewAI, Semantic Kernel, Google ADK and custom frameworks to add instrumentation without a rewrite.

Profiling and observability

Profile entire workflows from agent level down to individual tokens, trace execution and understand runtime behavior in production.

Evaluation and optimization

An offline evaluation system, a hyper-parameter and prompt optimizer, and reinforcement-learning fine-tuning support for agent-specific models.

Protocol support

Integrate MCP tools into agents or publish workflows as MCP servers (FastMCP publishing), and build distributed agent teams with the A2A protocol with authentication.

Performance primitives

Agent Performance Primitives accelerate graph frameworks (LangChain, CrewAI, Agno) with parallel execution, speculative branching and node-level priority routing.

Reusable components and UI

Build tools and workflows once and reuse them; a built-in chat UI lets you interact with agents and debug workflows visually.

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// execute você mesmo

Execute você mesmo

  1. Clone the repo

    Clone the toolkit if you want the examples locally; the library installs from PyPI in step 2.

    git clone https://github.com/NVIDIA/NeMo-Agent-Toolkit.git
    cd NeMo-Agent-Toolkit
  2. Install from PyPI

    Install the core package, adding a framework extra such as [langchain] if needed (Python 3.11–3.13).

    pip install nvidia-nat
    # e.g. pip install "nvidia-nat[langchain]"
  3. Run the Hello World workflow

    Set your NVIDIA key, save the README's workflow.yml (a ReAct agent with a Wikipedia tool), and run it with the nat CLI.

    export NVIDIA_API_KEY=<your-key>
    nat run --config_file workflow.yml --input "List five subspecies of Aardvarks"
// casos de uso

O que você pode criar

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// perguntas frequentes

Perguntas frequentes

Who maintains the NeMo Agent Toolkit?

NVIDIA maintains it, as part of the NeMo ecosystem; docs are at docs.nvidia.com/nemo/agent-toolkit.

Is it free?

Yes, Apache-2.0 licensed. Models you call (NVIDIA NIMs or other providers) have their own pricing.

Does it need a paid API key?

The Hello World uses NVIDIA NIMs with an NVIDIA_API_KEY from build.nvidia.com; the toolkit itself works with the LLM providers you configure in your workflow.

How does it compare to LangSmith alone?

LangSmith-style tracing is one piece (and the toolkit integrates with LangSmith natively). The toolkit also adds workflow composition, profiling, evaluation, optimization and protocol publishing across frameworks.

How do I get started?

pip install nvidia-nat, write a workflow.yml, and run nat run — or open the Colab introduction notebook with no local setup.

// continue explorando

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// alternativas

Alternativas em Agentes de IA

Os outros projetos de Agentes de IA com mais estrelas no catálogo do LibreAI — a lista realista de alternativas se NeMo-Agent-Toolkit não for o ideal para você.

// mais em agents

Mais em Agentes de IA

Projetos de Agentes de IA próximos no catálogo, os mais próximos em estrelas de NeMo-Agent-Toolkit: continue explorando dos dois lados dele.

Baixe o NeMo-Agent-Toolkit

Grátis e aberto: pegue o código-fonte, dê uma estrela no repositório e comece a criar.