LLMs#

Large language models (LLMs) are deep learning algorithms that can recognize, summarize, translate, predict, and generate content using very large datasets.

Supported LLM Providers#

NVIDIA NeMo Agent toolkit supports the following LLM providers:

Provider	Type	Description
NVIDIA NIM	`nim`	NVIDIA Inference Microservice (NIM)
OpenAI	`openai`	OpenAI API
AWS Bedrock	`aws_bedrock`	AWS Bedrock API
Azure OpenAI	`azure_openai`	Azure OpenAI API
LiteLLM	`litellm`	LiteLLM API
HuggingFace	`huggingface`	HuggingFace API

LLM Configuration#

The LLM configuration is defined in the llms section of the workflow configuration file. The _type value refers to the LLM provider, and the model_name value always refers to the name of the model to use.

llms:
  nim_llm:
    _type: nim
    model_name: meta/llama-3.1-70b-instruct
  openai_llm:
    _type: openai
    model_name: gpt-4o-mini
  aws_bedrock_llm:
    _type: aws_bedrock
    model_name: meta/llama-3.1-70b-instruct
    region_name: us-east-1
  azure_openai_llm:
    _type: azure_openai
    azure_deployment: gpt-4o-mini
  litellm_llm:
    _type: litellm
    model_name: gpt-4o
  huggingface_llm:
    _type: huggingface
    model_name: Qwen/Qwen3Guard-Gen-0.6B

NVIDIA NIM#

You can use the following environment variables to configure the NVIDIA NIM LLM provider:

NVIDIA_API_KEY - The API key to access NVIDIA NIM resources

The NIM LLM provider is defined by the NIMModelConfig class.

model_name - The name of the model to use
temperature - The temperature to use for the model
top_p - The top-p value to use for the model
max_tokens - The maximum number of tokens to generate
api_key - The API key to use for the model
base_url - The base URL to use for the model
max_retries - The maximum number of retries for the request

Note

temperature and top_p are model-gated fields and may not be supported by all models. If unsupported and explicitly set, validation will fail. See Gated Fields for details.

OpenAI#

You can use the following environment variables to configure the OpenAI LLM provider:

OPENAI_API_KEY - The API key to access OpenAI resources

The OpenAI LLM provider is defined by the OpenAIModelConfig class.

model_name - The name of the model to use
temperature - The temperature to use for the model
top_p - The top-p value to use for the model
max_tokens - The maximum number of tokens to generate
seed - The seed to use for the model
api_key - The API key to use for the model
base_url - The base URL to use for the model
max_retries - The maximum number of retries for the request

Note

temperature and top_p are model-gated fields and may not be supported by all models. If unsupported and explicitly set, validation will fail. See Gated Fields for details.

AWS Bedrock#

The AWS Bedrock LLM provider is defined by the AWSBedrockModelConfig class.

model_name - The name of the model to use
temperature - The temperature to use for the model
top_p - The top-p value to use for the model. This field is ignored for LlamaIndex.
max_tokens - The maximum number of tokens to generate
context_size - The maximum number of tokens available for input. This is only required for LlamaIndex. This field is ignored for LangChain/LangGraph.
region_name - The region to use for the model
base_url - The base URL to use for the model
credentials_profile_name - The credentials profile name to use for the model
max_retries - The maximum number of retries for the request

Azure OpenAI#

You can use the following environment variables to configure the Azure OpenAI LLM provider:

AZURE_OPENAI_API_KEY - The API key to access Azure OpenAI resources
AZURE_OPENAI_ENDPOINT - The Azure OpenAI endpoint to access Azure OpenAI resources

The Azure OpenAI LLM provider is defined by the AzureOpenAIModelConfig class.

api_key - The API key to use for the model
api_version - The API version to use for the model
azure_endpoint - The Azure OpenAI endpoint to use for the model
azure_deployment - The name of the Azure OpenAI deployment to use
temperature - The temperature to use for the model
top_p - The top-p value to use for the model
seed - The seed to use for the model
max_retries - The maximum number of retries for the request

Note

temperature is model-gated and may not be supported by all models. See Gated Fields for details.

LiteLLM#

LiteLLM is a general purpose LLM provider that can be used with any model provider that is supported by LiteLLM. See the LiteLLM provider documentation for more information on how to use LiteLLM.

The LiteLLM LLM provider is defined by the LiteLlmModelConfig class.

model_name - The name of the model to use (dependent on the model provider)
api_key - The API key to use for the model (dependent on the model provider)
base_url - The base URL to use for the model
seed - The seed to use for the model
temperature - The temperature to use for the model
top_p - The top-p value to use for the model
max_retries - The maximum number of retries for the request

HuggingFace#

HuggingFace is a general-purpose LLM provider that can be used with any model supported by the HuggingFace API. See the HuggingFace documentation for more information.

The HuggingFace LLM provider is defined by the HuggingFaceConfig class.

model_name - The HuggingFace model name or path (for example, Qwen/Qwen3Guard-Gen-0.6B)
device - Device for model execution: cpu, cuda, cuda:0, or auto (default: auto)
dtype - Torch data type: float16, bfloat16, float32, or auto (default: auto)
max_new_tokens - Maximum number of new tokens to generate (default: 128)
temperature - Sampling temperature (default: 0.0)
trust_remote_code - Whether to trust remote code when loading the model (default: false)

Note

HuggingFace is a built-in NeMo Agent Toolkit LLM provider, but requires extra dependencies to run. They can be installed with:

pip install "transformers[torch,accelerate]~=4.57"

NVIDIA Dynamo (experimental)#

Dynamo is an inference engine agnostic LLM provider designed to optimize KV cache reuse of LLMs served on NVIDIA hardware. See the ai-dynamo repository for instructions on how to use Dynamo.

The Dynamo LLM provider is defined by the DynamoModelConfig class. The provider mirrors the implementation of the OpenAI provider, with additional prefix hints for Dynamo inference optimizations.

model_name - The name of the model to use
temperature - The temperature to use for the model
top_p - The top-p value to use for the model
max_tokens - The maximum number of tokens to generate
seed - The seed to use for the model
api_key - The API key to use for the model
base_url - The base URL to use for the model
max_retries - The maximum number of retries for the request
prefix_template - a template for conversation prefix IDs. Setting to null will disable use of prefix_template, prefix_total_requests, prefix_osl, and prefix_iat
prefix_total_requests - Expected number of requests for this conversation
prefix_osl - Output sequence length for the Dynamo router
prefix_iat - Inter-arrival time hint for the Dynamo router
request_timeout - HTTP request timeout in seconds for Dynamo LLM requests

Testing Provider#

`nat_test_llm`#

nat_test_llm is a development and testing provider intended for examples and CI. It is not intended for production use.

Installation: uv pip install nvidia-nat-test
Purpose: Deterministic cycling responses for quick validation
Not for production

Minimal YAML example with chat_completion:

llms:
  main:
    _type: nat_test_llm
    response_seq: [alpha, beta, gamma]
    delay_ms: 0
workflow:
  _type: chat_completion
  llm_name: main
  system_prompt: "Say only the answer."

Learn how to add your own LLM provider: Adding an LLM Provider

See a short tutorial using YAML and nat_test_llm: Test with nat_test_llm

LLMs#

Supported LLM Providers#

LLM Configuration#

NVIDIA NIM#

OpenAI#

AWS Bedrock#

Azure OpenAI#

LiteLLM#

HuggingFace#

NVIDIA Dynamo (experimental)#

Testing Provider#

nat_test_llm#

Related Topics#

`nat_test_llm`#