73 lines
3.1 KiB
Markdown
73 lines
3.1 KiB
Markdown
# Models
|
|
|
|
The Agents SDK comes with out-of-the-box support for OpenAI models in two flavors:
|
|
|
|
- **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).
|
|
- The [`OpenAIChatCompletionsModel`][agents.models.openai_chatcompletions.OpenAIChatCompletionsModel], which calls OpenAI APIs using the [Chat Completions API](https://platform.openai.com/docs/api-reference/chat).
|
|
|
|
## Mixing and matching models
|
|
|
|
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:
|
|
|
|
1. Passing the name of an OpenAI model.
|
|
2. Passing any model name + a [`ModelProvider`][agents.models.interface.ModelProvider] that can map that name to a Model instance.
|
|
3. Directly providing a [`Model`][agents.models.interface.Model] implementation.
|
|
|
|
!!!note
|
|
|
|
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.
|
|
|
|
```python
|
|
from agents import Agent, Runner, AsyncOpenAI, OpenAIChatCompletionsModel
|
|
import asyncio
|
|
|
|
spanish_agent = Agent(
|
|
name="Spanish agent",
|
|
instructions="You only speak Spanish.",
|
|
model="o3-mini", # (1)!
|
|
)
|
|
|
|
english_agent = Agent(
|
|
name="English agent",
|
|
instructions="You only speak English",
|
|
model=OpenAIChatCompletionsModel( # (2)!
|
|
model="gpt-4o",
|
|
openai_client=AsyncOpenAI()
|
|
),
|
|
)
|
|
|
|
triage_agent = Agent(
|
|
name="Triage agent",
|
|
instructions="Handoff to the appropriate agent based on the language of the request.",
|
|
handoffs=[spanish_agent, english_agent],
|
|
model="gpt-3.5-turbo",
|
|
)
|
|
|
|
async def main():
|
|
result = await Runner.run(triage_agent, input="Hola, ¿cómo estás?")
|
|
print(result.final_output)
|
|
```
|
|
|
|
1. Sets the name of an OpenAI model directly.
|
|
2. Provides a [`Model`][agents.models.interface.Model] implementation.
|
|
|
|
## Using other LLM providers
|
|
|
|
Many providers also support the OpenAI API format, which means you can pass a `base_url` to the existing OpenAI model implementations and use them easily. `ModelSettings` is used to configure tuning parameters (e.g., temperature, top_p) for the model you select.
|
|
|
|
```python
|
|
external_client = AsyncOpenAI(
|
|
api_key="EXTERNAL_API_KEY",
|
|
base_url="https://api.external.com/v1/",
|
|
)
|
|
|
|
spanish_agent = Agent(
|
|
name="Spanish agent",
|
|
instructions="You only speak Spanish.",
|
|
model=OpenAIChatCompletionsModel(
|
|
model="EXTERNAL_MODEL_NAME",
|
|
openai_client=external_client,
|
|
),
|
|
model_settings=ModelSettings(temperature=0.5),
|
|
)
|
|
```
|