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=<key_from_build.nvidia.com>

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:

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/<example_directory>
nat run --config_file examples/<example_directory>/configs/config.yml --input "test"