> For the complete documentation index, see [llms.txt](https://docs.mozilla.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mozilla.ai/api-reference/messages.md). # 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 | list[dict[str, Any]] | 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, cache_control: dict[str, Any] | None = None, output_format: type | 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 | ParsedMessage[Any] | Iterator[RawMessageStartEvent | RawMessageDeltaEvent | RawMessageStopEvent | RawContentBlockStartEvent | RawContentBlockDeltaEvent | RawContentBlockStopEvent] ``` ### `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 | list[dict[str, Any]] | 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, cache_control: dict[str, Any] | None = None, output_format: type | 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 | ParsedMessage[Any] | AsyncIterator[RawMessageStartEvent | RawMessageDeltaEvent | RawMessageStopEvent | RawContentBlockStartEvent | RawContentBlockDeltaEvent | RawContentBlockStopEvent] ``` ### 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 \| list[dict[str, Any]] \| None` | None | System prompt (string or list of content blocks with optional cache\_control). | | `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. | | `cache_control` | `dict[str, Any] \| None` | None | Cache control configuration for prompt caching. | | `output_format` | `type \| dict[str, Any] \| None` | None | Structured output, mirroring Anthropic's `messages.parse`/`output_config`. Either a Pydantic `BaseModel`/dataclass **type** (typed `parsed_output`) or a raw Anthropic `output_config` **dict** for non-Pydantic JSON schemas (`parsed_output` holds the parsed JSON). The call returns Anthropic's `ParsedMessage`. Not supported with streaming. | | `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`](/api-reference/completion-1/messages.md) object. * **Streaming** (`stream=True`): Returns an `Iterator[MessageStreamEvent]` (sync) or `AsyncIterator[MessageStreamEvent]` (async). ### Usage #### Basic message ```python 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 ```python 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 ```python 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()) ``` --- # Agent Instructions This documentation is published with GitBook. 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