🎁 Get the FREE AI Skills Starter Guide — Subscribe →
BytesAgainBytesAgain
BytesAgain › Skills › NAT
🦀 ClawHub

NAT

by @sauravdev

NVIDIA NeMo Agent Toolkit (NAT) — install, create workflows, add tools, run agents, evaluate performance, and publish as A2A/MCP servers. Use when: (1) insta...

Versionv1.0.0
Downloads298
#legal#automation
TERMINAL
clawhub install nat-skill

📖 About This Skill

name: nat description: "NVIDIA NeMo Agent Toolkit (NAT) — install, create workflows, add tools, run agents, evaluate performance, and publish as A2A/MCP servers. Use when: (1) installing or setting up NAT, (2) creating or editing workflow YAML configs, (3) adding built-in or custom tools/functions, (4) running agents with nat run, (5) evaluating or profiling workflows, (6) publishing workflows as A2A or MCP servers, (7) creating custom functions or function groups, (8) integrating with LangChain, LlamaIndex, CrewAI, or other frameworks. Trigger keywords: NAT, NeMo Agent Toolkit, nvidia-nat, nat run, nat workflow, nat eval, nat a2a, nat profiler, workflow.yml, react_agent, tool_calling_agent." metadata: { "openclaw": { "requires": { "anyBins": ["nat", "pip", "uv"] }, "primaryEnv": "NVIDIA_API_KEY" } }

NVIDIA NeMo Agent Toolkit (NAT)

A flexible library for connecting enterprise agents to data sources and tools across any framework.

  • Repo: https://github.com/NVIDIA/NeMo-Agent-Toolkit
  • Docs: https://docs.nvidia.com/nemo/agent-toolkit/latest/

    • Installation

    # Core (pick one)
uv pip install nvidia-nat        # recommended
pip install nvidia-nat
    With framework extras
    
uv pip install "nvidia-nat[langchain]"    # LangChain/LangGraph
uv pip install "nvidia-nat[llama-index]"  # LlamaIndex
uv pip install "nvidia-nat[crewai]"       # CrewAI
uv pip install "nvidia-nat[mcp]"          # MCP
uv pip install "nvidia-nat[a2a]"          # A2A
uv pip install "nvidia-nat[mem0ai]"       # Mem0 memory
uv pip install "nvidia-nat[eval,profiling]"  # Eval + profiling
    Verify
    
nat --help && nat --version

    For development install from source, see references/install-from-source.md.

    Quick Start

    export NVIDIA_API_KEY=

    Create workflow.yml:

    functions:
  wikipedia_search:
    _type: wiki_search
    max_results: 2
    llms:
  nim_llm:
    _type: nim
    model_name: meta/llama-3.1-70b-instruct
    temperature: 0.0
    workflow:
  _type: react_agent
  tool_names: [wikipedia_search]
  llm_name: nim_llm
  verbose: true
  parse_agent_response_max_retries: 3


    nat run --config_file workflow.yml --input "List five subspecies of Aardvarks"

    Workflow Configuration Structure

    Four main YAML sections:

    | Section | Purpose | |---|---| | functions | Tools (web search, calculators, custom) | | llms | LLM provider configs (NIM, OpenAI, Azure, Bedrock) | | embedders | Embedding models for vector storage | | workflow | Agent type + wiring of tools and LLMs |

    Agent Types (_type in workflow)

  • react_agent — Reasoning and acting
  • reasoning_agent — Advanced reasoning
  • rewwo_agent — Reasoning Without Observation
  • responses_api_agent — OpenAI Responses API
  • tool_calling_agent — Direct tool calling
  • automatic_memory_wrapper_agent — Adds memory
  • router_agent — Routes to different workflows
  • sequential_executor — Sequential tool execution

    • Built-in Tools (_type in functions)

    wiki_search, webpage_query, tavily_internet_search, arxiv_search, current_datetime, calculator, text_file_ingest, and many more framework-specific tools.

    List all available components:

    nat info components -t function      # Tools
nat info components -t llm_provider  # LLMs
nat info components -t embedder      # Embedders

    Common CLI Commands

    # Run workflow
nat run --config_file workflow.yml --input "question"
    Override params without editing YAML
    
nat run --config_file workflow.yml --input "question" \
  --override llms.nim_llm.temperature 0.7 \
  --override llms.nim_llm.model_name meta/llama-3.3-70b-instruct
    Create new workflow template
    
nat workflow create --workflow-dir examples my_workflow
    Evaluate
    
nat eval --config_file eval_config.yml
    Profile
    
nat profiler --config_file workflow.yml --input "test"
    Red team
    
nat red-team --config_file workflow.yml
    Workflow management
    
nat workflow reinstall my_workflow
nat workflow delete my_workflow

    Custom Tools and Function Groups

    For creating custom tools, function groups, and advanced patterns, see:

  • references/custom-tools.md — Writing custom functions, registration, and installation
  • references/function-groups.md — Shared config, namespacing, include/exclude, access levels

    • A2A Server

    Publish workflows as A2A agents for discovery and invocation by other A2A clients.

    # Start A2A server
nat a2a serve --config_file workflow.yml
    Discover agent
    
nat a2a client discover --url http://localhost:10000
    Call agent
    
nat a2a client call --url http://localhost:10000 --message "What is 42 * 67?"

    For full A2A configuration (auth, concurrency, Kubernetes), see references/a2a-server.md.

    Examples

    The repo includes examples organized by category: Getting Started, Agents, Advanced Agents, Control Flow, Frameworks, MCP/A2A, Evaluation, and more. See references/examples.md for the full catalog and how to run them.

    # Run any example
uv pip install -e examples/
nat run --config_file examples//configs/config.yml --input "test"

    💡 Examples

    The repo includes examples organized by category: Getting Started, Agents, Advanced Agents, Control Flow, Frameworks, MCP/A2A, Evaluation, and more. See references/examples.md for the full catalog and how to run them.

    # Run any example
uv pip install -e examples/
nat run --config_file examples//configs/config.yml --input "test"