Meta
Use Meta’s Muse models through MetaProvider and the Meta Model API. The provider prefix is meta:, and it uses the OpenAI-compatible Responses API.
Install Pydantic AI with the OpenAI SDK used by this integration:
pip install "pydantic-ai-slim[openai]"
uv add "pydantic-ai-slim[openai]"
Create an API key on the Meta developer platform.
You can set the META_API_KEY environment variable and use MetaProvider by name:
from pydantic_ai import Agent
agent = Agent('meta:muse-spark-1.3')
...
Or initialise the model and provider directly:
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIResponsesModel
from pydantic_ai.providers.meta import MetaProvider
model = OpenAIResponsesModel(
'muse-spark-1.3',
provider=MetaProvider(api_key='your-meta-api-key'),
)
agent = Agent(model)
...
Contributor mode models (muse-spark-1.3-contributor, muse-spark-1.2-contributor) are separate model names with their own pricing.
The meta: prefix uses OpenAIResponsesModel. Muse labels the progress updates it writes before a tool call as commentary, and Pydantic AI sends that label and Muse’s encrypted reasoning back on later requests, which keeps long tool-calling runs from ending on a progress update.
Meta also serves the Chat Completions API, which you can use with OpenAIChatModel and the same provider. It doesn’t return reasoning (only a reasoning_tokens count in usage), so you won’t get thinking parts.
Muse models always reason: Meta rejects reasoning_effort='none', so thinking=False is ignored. Efforts from 'minimal' to 'max' are supported through the unified thinking setting or openai_reasoning_effort. Meta only streams reasoning summaries when you request them:
from pydantic_ai import Agent
from pydantic_ai.models.openai import OpenAIResponsesModelSettings
agent = Agent(
'meta:muse-spark-1.3',
model_settings=OpenAIResponsesModelSettings(openai_reasoning_effort='high', openai_reasoning_summary='auto'),
)
...
Unlike OpenAI’s reasoning models, Muse accepts temperature and top_p while reasoning, so they’re sent as configured.
Meta only accepts tool_choice='auto'. When Pydantic AI would force a tool itself, such as an output tool, it falls back to 'auto'. An explicit forcing tool_choice ('required' or specific tools) raises a UserError, and tool_choice='none' is rejected by the API: it’s sent as 'none', or as an allowed_tools restriction to the output tools when the Responses API has some, and the API only takes 'auto'.
Muse supports the WebSearchTool, billed per search on top of token usage.