Using different models

smolagents provides a flexible framework that allows you to use various language models from different providers. This guide will show you how to use different model types with your agents.

Available model types

smolagents supports several model types out of the box:

  1. InferenceClientModel: Uses Hugging Face’s Inference API to access models
  2. TransformersModel: Runs models locally using the Transformers library
  3. VLLMModel: Uses vLLM for fast inference with optimized serving
  4. MLXModel: Optimized for Apple Silicon devices using MLX
  5. LiteLLMModel: Provides access to hundreds of LLMs through LiteLLM
  6. LiteLLMRouterModel: Distributes requests among multiple models
  7. OpenAIModel: Provides access to any provider that implements an OpenAI-compatible API
  8. AzureOpenAIModel: Uses Azure’s OpenAI service
  9. AmazonBedrockModel: Connects to AWS Bedrock’s API

All model classes support passing additional keyword arguments (like temperature, max_tokens, top_p, etc.) directly at instantiation time. These parameters are automatically forwarded to the underlying model’s completion calls, allowing you to configure model behavior such as creativity, response length, and sampling strategies.

Using Google Gemini Models

As explained in the Google Gemini API documentation (https://ai.google.dev/gemini-api/docs/openai), Google provides an OpenAI-compatible API for Gemini models, allowing you to use the OpenAIModel with Gemini models by setting the appropriate base URL.

First, install the required dependencies:

pip install 'smolagents[openai]'

Then, get a Gemini API key and set it in your code:

GEMINI_API_KEY = <YOUR-GEMINI-API-KEY>

Now, you can initialize the Gemini model using the OpenAIModel class and setting the api_base parameter to the Gemini API base URL:

from smolagents import OpenAIModel

model = OpenAIModel(
    model_id="gemini-2.0-flash",
    # Google Gemini OpenAI-compatible API base URL
    api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
    api_key=GEMINI_API_KEY,
)

Using OpenRouter Models

OpenRouter provides access to a wide variety of language models through a unified OpenAI-compatible API. You can use the OpenAIModel to connect to OpenRouter by setting the appropriate base URL.

First, install the required dependencies:

pip install 'smolagents[openai]'

Then, get an OpenRouter API key and set it in your code:

OPENROUTER_API_KEY = <YOUR-OPENROUTER-API-KEY>

Now, you can initialize any model available on OpenRouter using the OpenAIModel class:

from smolagents import OpenAIModel

model = OpenAIModel(
    # You can use any model ID available on OpenRouter
    model_id="openai/gpt-4o",
    # OpenRouter API base URL
    api_base="https://openrouter.ai/api/v1",
    api_key=OPENROUTER_API_KEY,
)

Using xAI’s Grok Models

xAI’s Grok models can be accessed through LiteLLMModel.

Some models (such as “grok-4” and “grok-3-mini”) don’t support the stop parameter, so you’ll need to use REMOVE_PARAMETER to exclude it from API calls.

First, install the required dependencies:

pip install smolagents[litellm]

Then, get an xAI API key and set it in your code:

XAI_API_KEY = <YOUR-XAI-API-KEY>

Now, you can initialize Grok models using the LiteLLMModel class and remove the stop parameter if applicable:

from smolagents import LiteLLMModel, REMOVE_PARAMETER

# Using Grok-4
model = LiteLLMModel(
    model_id="xai/grok-4",
    api_key=XAI_API_KEY,
    stop=REMOVE_PARAMETER,  # Remove stop parameter as grok-4 model doesn't support it
    temperature=0.7
)

# Or using Grok-3-mini
model_mini = LiteLLMModel(
    model_id="xai/grok-3-mini",
    api_key=XAI_API_KEY,
    stop=REMOVE_PARAMETER,  # Remove stop parameter as grok-3-mini model doesn't support it
    max_tokens=1000
)
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