66 lines
4.2 KiB
Markdown
66 lines
4.2 KiB
Markdown
# Models
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The Agents SDK comes with out-of-the-box support for OpenAI models in two flavors:
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- **Recommended**: the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel], which calls OpenAI APIs using the new [Responses API](https://platform.openai.com/docs/api-reference/responses).
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- The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat).
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## Mixing and matching models
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Within a single workflow, you may want to use different models for each agent. For example, you could use a smaller, faster model for triage, while using a larger, more capable model for complex tasks. When configuring an [`Agent`][agents.Agent], you can select a specific model by either:
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1. Passing the name of an OpenAI model.
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2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance.
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3. Directly providing a [`Model`][agents.models.interface.Model] implementation.
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!!!note
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While our SDK supports both the [`OpenAIResponsesModel`][agents.models.openai_responses.OpenAIResponsesModel] and the [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel] shapes, we recommend using a single model shape for each workflow because the two shapes support a different set of features and tools. If your workflow requires mixing and matching model shapes, make sure that all the features you're using are available on both.
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```python
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from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
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import asyncio
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spanish_agent = Agent(
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name="Spanish agent",
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instructions="You only speak Spanish.",
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model="o3-mini", # (1)!
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)
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english_agent = Agent(
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name="English agent",
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instructions="You only speak English",
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model=OpenAIChatCompletionsModel( # (2)!
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model="gpt-4o",
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openai_client=AsyncOpenAI()
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),
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)
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triage_agent = Agent(
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name="Triage agent",
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instructions="Handoff to the appropriate agent based on the language of the request.",
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handoffs=[spanish_agent, english_agent],
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model="gpt-3.5-turbo",
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)
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async def main():
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result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
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print(result.final_output)
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```
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1. Sets the name of an OpenAI model directly.
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2. Provides a [`Model`][agents.models.interface.Model] implementation.
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## Using other LLM providers
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You can use other LLM providers in 3 ways (examples [here](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/)):
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1. [`set_default_openai_client`][agents.set_default_openai_client] is useful in cases where you want to globally use an instance of `AsyncOpenAI` as the LLM client. This is for cases where the LLM provider has an OpenAI compatible API endpoint, and you can set the `base_url` and `api_key`. See a configurable example in [examples/model_providers/custom_example_global.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_global.py).
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2. [`ModelProvider`][agents.models.interface.ModelProvider] is at the `Runner.run` level. This lets you say "use a custom model provider for all agents in this run". See a configurable example in [examples/model_providers/custom_example_provider.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_provider.py).
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3. [`Agent.model`][agents.agent.Agent.model] lets you specify the model on a specific Agent instance. This enables you to mix and match different providers for different agents. See a configurable example in [examples/model_providers/custom_example_agent.py](https://github.com/openai/openai-agents-python/tree/main/examples/model_providers/custom_example_agent.py).
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In cases where you do not have an API key from `platform.openai.com`, we recommend disabling tracing via `set_tracing_disabled()`, or setting up a [different tracing processor](tracing.md).
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!!! note
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In these examples, we use the Chat Completions API/model, because most LLM providers don't yet support the Responses API. If your LLM provider does support it, we recommend using Responses.
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