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	Optional user session label metadata for platform traces; exported as anyllm.user_session_label
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 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

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

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

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

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

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],
)