# Completion The `completion` and `acompletion` functions are the primary way to generate chat completions across all supported providers. They accept an OpenAI-compatible parameter set and return OpenAI-compatible response types. ### `any_llm.completion()` ``` def completion( model: str, messages: list[dict[str, Any] | ChatCompletionMessage], *, provider: str | LLMProvider | None = None, tools: list[dict[str, Any] | Callable[..., Any]] | None = None, tool_choice: str | dict[str, Any] | None = None, temperature: float | None = None, top_p: float | None = None, max_tokens: int | None = None, response_format: dict[str, Any] | type | None = None, stream: bool | None = None, n: int | None = None, stop: str | list[str] | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, seed: int | None = None, api_key: str | None = None, api_base: str | None = None, user: str | None = None, session_label: str | None = None, parallel_tool_calls: bool | None = None, logprobs: bool | None = None, top_logprobs: int | None = None, logit_bias: dict[str, float] | None = None, stream_options: dict[str, Any] | None = None, max_completion_tokens: int | None = None, reasoning_effort: Literal['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'auto'] | None = "auto", client_args: dict[str, Any] | None = None, **kwargs: Any, ) -> ChatCompletion | Iterator[ChatCompletionChunk] ``` ### `any_llm.acompletion()` Async variant with the same parameters. Returns `ChatCompletion | AsyncIterator[ChatCompletionChunk]`. ``` async def acompletion( model: str, messages: list[dict[str, Any] | ChatCompletionMessage], *, provider: str | LLMProvider | None = None, tools: list[dict[str, Any] | Callable[..., Any]] | None = None, tool_choice: str | dict[str, Any] | None = None, temperature: float | None = None, top_p: float | None = None, max_tokens: int | None = None, response_format: dict[str, Any] | type | None = None, stream: bool | None = None, n: int | None = None, stop: str | list[str] | None = None, presence_penalty: float | None = None, frequency_penalty: float | None = None, seed: int | None = None, api_key: str | None = None, api_base: str | None = None, user: str | None = None, session_label: str | None = None, parallel_tool_calls: bool | None = None, logprobs: bool | None = None, top_logprobs: int | None = None, logit_bias: dict[str, float] | None = None, stream_options: dict[str, Any] | None = None, max_completion_tokens: int | None = None, reasoning_effort: Literal['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'auto'] | None = "auto", client_args: dict[str, Any] | None = None, **kwargs: Any, ) -> ChatCompletion | AsyncIterator[ChatCompletionChunk] ``` ### Parameters | Parameter | Type | Default | Description | | ----------------------- | ------------------------------------------------------------------------------ | ---------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `model` | `str` | *required* | Model identifier. **Recommended**: Use with separate `provider` parameter (e.g., model='gpt-4', provider='openai'). **Alternative**: Combined format 'provider:model' (e.g., 'openai:gpt-4'). Legacy format 'provider/model' is also supported but deprecated. | | `messages` | `list[dict[str, Any] \| ChatCompletionMessage]` | *required* | List of messages for the conversation | | `provider` | `str \| LLMProvider \| None` | None | **Recommended**: Provider name to use for the request (e.g., 'openai', 'mistral'). When provided, the model parameter should contain only the model name. | | `tools` | `list[dict[str, Any] \| Callable[..., Any]] \| None` | None | List of tools for tool calling. Can be Python callables or OpenAI tool format dicts | | `tool_choice` | `str \| dict[str, Any] \| None` | None | Controls which tools the model can call | | `temperature` | `float \| None` | None | Controls randomness in the response (0.0 to 2.0) | | `top_p` | `float \| None` | None | Controls diversity via nucleus sampling (0.0 to 1.0) | | `max_tokens` | `int \| None` | None | Maximum number of tokens to generate | | `response_format` | `dict[str, Any] \| type \| None` | None | Format specification for the response | | `stream` | `bool \| None` | None | Whether to stream the response | | `n` | `int \| None` | None | Number of completions to generate | | `stop` | `str \| list[str] \| None` | None | Stop sequences for generation | | `presence_penalty` | `float \| None` | None | Penalize new tokens based on presence in text | | `frequency_penalty` | `float \| None` | None | Penalize new tokens based on frequency in text | | `seed` | `int \| None` | None | Random seed for reproducible results | | `api_key` | `str \| None` | None | API key for the provider | | `api_base` | `str \| None` | None | Base URL for the provider API | | `user` | `str \| None` | None | Unique identifier for the end user | | `session_label` | `str \| None` | None | Deprecated, no longer used. Previously used for platform traces. | | `parallel_tool_calls` | `bool \| None` | None | Whether to allow parallel tool calls | | `logprobs` | `bool \| None` | None | Include token-level log probabilities in the response | | `top_logprobs` | `int \| None` | None | Number of alternatives to return when logprobs are requested | | `logit_bias` | `dict[str, float] \| None` | None | Bias the likelihood of specified tokens during generation | | `stream_options` | `dict[str, Any] \| None` | None | Additional options controlling streaming behavior | | `max_completion_tokens` | `int \| None` | None | Maximum number of tokens for the completion | | `reasoning_effort` | `Literal['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'auto'] \| None` | "auto" | Reasoning effort level for models that support it. "auto" will map to each provider's default. | | `client_args` | `dict[str, Any] \| None` | None | Additional provider-specific arguments that will be passed to the provider's client instantiation. | | `**kwargs` | `Any` | *required* | Additional provider-specific arguments that will be passed to the provider's API call. | ### Return Value * **Non-streaming** (`stream=None` or `stream=False`): Returns a [`ChatCompletion`](/api-reference/completion-1.md) object. * **Streaming** (`stream=True`): Returns an `Iterator[ChatCompletionChunk]` (sync) or `AsyncIterator[ChatCompletionChunk]` (async). * **Structured output** (when `response_format` is a Pydantic model or dataclass): Returns a `ParsedChatCompletion[T]` with a `.choices[0].message.parsed` field containing the deserialized object. ### Usage #### Basic completion ```python from any_llm import completion response = completion( model="mistral-small-latest", provider="mistral", messages=[{"role": "user", "content": "What is the capital of France?"}], ) print(response.choices[0].message.content) ``` #### Streaming ```python for chunk in completion( model="gpt-4.1-mini", provider="openai", messages=[{"role": "user", "content": "Tell me a story."}], stream=True, ): print(chunk.choices[0].delta.content or "", end="") ``` #### Async ```python import asyncio from any_llm import acompletion async def main(): response = await acompletion( model="claude-sonnet-4-20250514", provider="anthropic", messages=[{"role": "user", "content": "Hello!"}], ) print(response.choices[0].message.content) asyncio.run(main()) ``` #### Structured output ```python from pydantic import BaseModel from any_llm import completion class CityInfo(BaseModel): name: str country: str population: int response = completion( model="gpt-4.1-mini", provider="openai", messages=[{"role": "user", "content": "Tell me about Paris."}], response_format=CityInfo, ) city = response.choices[0].message.parsed print(f"{city.name}, {city.country} - pop. {city.population}") ``` #### Tool calling ```python from any_llm import completion def get_weather(location: str, unit: str = "F") -> str: """Get weather information for a location. Args: location: The city or location to get weather for unit: Temperature unit, either 'C' or 'F' Returns: Current weather description """ return f"Weather in {location} is sunny and 75{unit}!" response = completion( model="mistral-small-latest", provider="mistral", messages=[{"role": "user", "content": "What's the weather in Paris?"}], tools=[get_weather], ) ``` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.mozilla.ai/api-reference/completion.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. 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