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OpenAI

Install

To use OpenAI models, you need to either install pydantic-ai, or install pydantic-ai-slim with the openai optional group:

pip install "pydantic-ai-slim[openai]"

uv add "pydantic-ai-slim[openai]"

Configuration

To use OpenAIModel through their main API, go to platform.openai.com and follow your nose until you find the place to generate an API key.

Environment variable

Once you have the API key, you can set it as an environment variable:

export OPENAI_API_KEY='your-api-key'

You can then use OpenAIModel by name:

from pydantic_ai import Agent

agent = Agent('openai:gpt-4o')
...

Or initialise the model directly with just the model name:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel

model = OpenAIModel('gpt-4o')
agent = Agent(model)
...

By default, the OpenAIModel uses the OpenAIProvider with the base_url set to https://api.openai.com/v1.

Configure the provider

If you want to pass parameters in code to the provider, you can programmatically instantiate the OpenAIProvider and pass it to the model:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel('gpt-4o', provider=OpenAIProvider(api_key='your-api-key'))
agent = Agent(model)
...

Custom OpenAI Client

OpenAIProvider also accepts a custom AsyncOpenAI client via the openai_client parameter, so you can customise the organization, project, base_url etc. as defined in the OpenAI API docs.

You could also use the AsyncAzureOpenAI client to use the Azure OpenAI API.

from openai import AsyncAzureOpenAI

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

client = AsyncAzureOpenAI(
    azure_endpoint='...',
    api_version='2024-07-01-preview',
    api_key='your-api-key',
)

model = OpenAIModel(
    'gpt-4o',
    provider=OpenAIProvider(openai_client=client),
)
agent = Agent(model)
...

OpenAI Responses API

PydanticAI also supports OpenAI's Responses API through the OpenAIResponsesModel class.

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIResponsesModel

model = OpenAIResponsesModel('gpt-4o')
agent = Agent(model)
...

The Responses API has built-in tools that you can use instead of building your own:

  • Web search: allow models to search the web for the latest information before generating a response.
  • File search: allow models to search your files for relevant information before generating a response.
  • Computer use: allow models to use a computer to perform tasks on your behalf.

You can use the OpenAIResponsesModelSettings class to make use of those built-in tools:

from openai.types.responses import WebSearchToolParam  # (1)!

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIResponsesModel, OpenAIResponsesModelSettings

model_settings = OpenAIResponsesModelSettings(
    openai_builtin_tools=[WebSearchToolParam(type='web_search_preview')],
)
model = OpenAIResponsesModel('gpt-4o')
agent = Agent(model=model, model_settings=model_settings)

result = agent.run_sync('What is the weather in Tokyo?')
print(result.output)
"""
As of 7:48 AM on Wednesday, April 2, 2025, in Tokyo, Japan, the weather is cloudy with a temperature of 53°F (12°C).
"""
  1. The file search tool and computer use tool can also be imported from openai.types.responses.

You can learn more about the differences between the Responses API and Chat Completions API in the OpenAI API docs.

OpenAI-compatible Models

Many models are compatible with the OpenAI API, and can be used with OpenAIModel in PydanticAI. Before getting started, check the installation and configuration instructions above.

To use another OpenAI-compatible API, you can make use of the base_url and api_key arguments from OpenAIProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'model_name',
    provider=OpenAIProvider(
        base_url='https://<openai-compatible-api-endpoint>.com', api_key='your-api-key'
    ),
)
agent = Agent(model)
...

You can also use the provider argument with a custom provider class like the DeepSeekProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.deepseek import DeepSeekProvider

model = OpenAIModel(
    'deepseek-chat',
    provider=DeepSeekProvider(api_key='your-deepseek-api-key'),
)
agent = Agent(model)
...

You can also customize any provider with a custom http_client:

from httpx import AsyncClient

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.deepseek import DeepSeekProvider

custom_http_client = AsyncClient(timeout=30)
model = OpenAIModel(
    'deepseek-chat',
    provider=DeepSeekProvider(
        api_key='your-deepseek-api-key', http_client=custom_http_client
    ),
)
agent = Agent(model)
...

Ollama

To use Ollama, you must first download the Ollama client, and then download a model using the Ollama model library.

You must also ensure the Ollama server is running when trying to make requests to it. For more information, please see the Ollama documentation.

Example local usage

With ollama installed, you can run the server with the model you want to use:

ollama run llama3.2

(this will pull the llama3.2 model if you don't already have it downloaded)

Then run your code, here's a minimal example:

from pydantic import BaseModel

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider


class CityLocation(BaseModel):
    city: str
    country: str


ollama_model = OpenAIModel(
    model_name='llama3.2', provider=OpenAIProvider(base_url='http://localhost:11434/v1')
)
agent = Agent(ollama_model, output_type=CityLocation)

result = agent.run_sync('Where were the olympics held in 2012?')
print(result.output)
#> city='London' country='United Kingdom'
print(result.usage())
"""
Usage(requests=1, request_tokens=57, response_tokens=8, total_tokens=65, details=None)
"""

Example using a remote server

from pydantic import BaseModel

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

ollama_model = OpenAIModel(
    model_name='qwen2.5-coder:7b',  # (1)!
    provider=OpenAIProvider(base_url='http://192.168.1.74:11434/v1'),  # (2)!
)


class CityLocation(BaseModel):
    city: str
    country: str


agent = Agent(model=ollama_model, output_type=CityLocation)

result = agent.run_sync('Where were the olympics held in 2012?')
print(result.output)
#> city='London' country='United Kingdom'
print(result.usage())
"""
Usage(requests=1, request_tokens=57, response_tokens=8, total_tokens=65, details=None)
"""
  1. The name of the model running on the remote server
  2. The url of the remote server

Azure AI Foundry

If you want to use Azure AI Foundry as your provider, you can do so by using the AzureProvider class.

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.azure import AzureProvider

model = OpenAIModel(
    'gpt-4o',
    provider=AzureProvider(
        azure_endpoint='your-azure-endpoint',
        api_version='your-api-version',
        api_key='your-api-key',
    ),
)
agent = Agent(model)
...

OpenRouter

To use OpenRouter, first create an API key at openrouter.ai/keys.

Once you have the API key, you can use it with the OpenAIProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'anthropic/claude-3.5-sonnet',
    provider=OpenAIProvider(
        base_url='https://openrouter.ai/api/v1',
        api_key='your-openrouter-api-key',
    ),
)
agent = Agent(model)
...

Grok (xAI)

Go to xAI API Console and create an API key. Once you have the API key, you can use it with the OpenAIProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'grok-2-1212',
    provider=OpenAIProvider(base_url='https://api.x.ai/v1', api_key='your-xai-api-key'),
)
agent = Agent(model)
...

Perplexity

Follow the Perplexity getting started guide to create an API key. Then, you can query the Perplexity API with the following:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'sonar-pro',
    provider=OpenAIProvider(
        base_url='https://api.perplexity.ai',
        api_key='your-perplexity-api-key',
    ),
)
agent = Agent(model)
...

Fireworks AI

Go to Fireworks.AI and create an API key in your account settings. Once you have the API key, you can use it with the OpenAIProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'accounts/fireworks/models/qwq-32b',  # model library available at https://fireworks.ai/models
    provider=OpenAIProvider(
        base_url='https://api.fireworks.ai/inference/v1',
        api_key='your-fireworks-api-key',
    ),
)
agent = Agent(model)
...

Together AI

Go to Together.ai and create an API key in your account settings. Once you have the API key, you can use it with the OpenAIProvider:

from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIModel
from pydantic_ai.providers.openai import OpenAIProvider

model = OpenAIModel(
    'meta-llama/Llama-3.3-70B-Instruct-Turbo-Free',  # model library available at https://www.together.ai/models
    provider=OpenAIProvider(
        base_url='https://api.together.xyz/v1',
        api_key='your-together-api-key',
    ),
)
agent = Agent(model)
...