Messages

The messages and amessages functions use the Anthropic Messages API format. All providers support this through automatic conversion, so you can use the same Anthropic-style message format regardless of backend.

any_llm.messages()

def messages(
    model: str,
    messages: list[dict[str, Any]],
    max_tokens: int,
    *,
    provider: str | LLMProvider | None = None,
    system: str | None = None,
    temperature: float | None = None,
    top_p: float | None = None,
    top_k: int | None = None,
    stream: bool | None = None,
    stop_sequences: list[str] | None = None,
    tools: list[dict[str, Any]] | None = None,
    tool_choice: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
    thinking: dict[str, Any] | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> MessageResponse | Iterator[MessageStreamEvent]

any_llm.amessages()

Async variant with the same parameters. Returns MessageResponse | AsyncIterator[MessageStreamEvent].

async def amessages(
    model: str,
    messages: list[dict[str, Any]],
    max_tokens: int,
    *,
    provider: str | LLMProvider | None = None,
    system: str | None = None,
    temperature: float | None = None,
    top_p: float | None = None,
    top_k: int | None = None,
    stream: bool | None = None,
    stop_sequences: list[str] | None = None,
    tools: list[dict[str, Any]] | None = None,
    tool_choice: dict[str, Any] | None = None,
    metadata: dict[str, Any] | None = None,
    thinking: dict[str, Any] | None = None,
    api_key: str | None = None,
    api_base: str | None = None,
    client_args: dict[str, Any] | None = None,
    **kwargs: Any,
) -> MessageResponse | AsyncIterator[MessageStreamEvent]

Parameters

Parameter	Type	Default	Description
model	str	required	Model identifier. Recommended: Use with separate provider parameter. Alternative: Combined format ‘provider:model’.
messages	list[dict[str, Any]]	required	List of messages in Anthropic format.
max_tokens	int	required	Maximum number of tokens to generate.
provider	str | LLMProvider | None	None	Provider name to use for the request.
system	str | None	None	System prompt.
temperature	float | None	None	Controls randomness (0.0 to 1.0).
top_p	float | None	None	Controls diversity via nucleus sampling.
top_k	int | None	None	Only sample from the top K options.
stream	bool | None	None	Whether to stream the response.
stop_sequences	list[str] | None	None	Custom stop sequences.
tools	list[dict[str, Any]] | None	None	List of tools in Anthropic format.
tool_choice	dict[str, Any] | None	None	Controls which tool the model uses.
metadata	dict[str, Any] | None	None	Request metadata.
thinking	dict[str, Any] | None	None	Thinking/reasoning configuration.
api_key	str | None	None	API key for the provider.
api_base	str | None	None	Base URL for the provider API.
client_args	dict[str, Any] | None	None	Additional provider-specific arguments for client instantiation.
**kwargs	Any	required	Additional provider-specific arguments.

Return Value

• Non-streaming: Returns a MessageResponse object.
• Streaming (stream=True): Returns an Iterator[MessageStreamEvent] (sync) or AsyncIterator[MessageStreamEvent] (async).

Usage

Basic message

from any_llm.api import messages

response = messages(
    model="claude-sonnet-4-20250514",
    provider="anthropic",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=1024,
)
print(response.content[0].text)

With system prompt

response = messages(
    model="claude-sonnet-4-20250514",
    provider="anthropic",
    messages=[{"role": "user", "content": "Translate to French: Hello"}],
    max_tokens=1024,
    system="You are a professional translator.",
)

Async

import asyncio
from any_llm.api import amessages

async def main():
    response = await amessages(
        model="claude-sonnet-4-20250514",
        provider="anthropic",
        messages=[{"role": "user", "content": "Hello!"}],
        max_tokens=1024,
    )
    print(response.content[0].text)

asyncio.run(main())