Messages

Data models for the Anthropic Messages API

The Messages API types are Pydantic models used by any_llm.api.messages() and any_llm.api.amessages().

Primary Types

MessageResponse

Full response from the Messages API.

Import: from any_llm.types.messages import MessageResponse

MessageContentBlock

Content block in a Messages API response.

Import: from any_llm.types.messages import MessageContentBlock

MessageUsage

Token usage information for Messages API.

Import: from any_llm.types.messages import MessageUsage

Field	Type	Description
cache_creation	CacheCreation | None	Breakdown of cached tokens by TTL
cache_creation_input_tokens	int | None	The number of input tokens used to create the cache entry.
cache_read_input_tokens	int | None	The number of input tokens read from the cache.
inference_geo	str | None	The geographic region where inference was performed for this request.
input_tokens	int	The number of input tokens which were used.
output_tokens	int	The number of output tokens which were used.
output_tokens_details	OutputTokensDetails | None	Breakdown of output tokens by category. output_tokens remains the inclusive, authoritative total used for billing. This object provides a read-only decomposition for observability — for example, how many of the billed output tokens were spent on internal reasoning that may have been summarized before being returned to you.
server_tool_use	ServerToolUsage | None	The number of server tool requests.
service_tier	Literal['standard', 'priority', 'batch'] | None	If the request used the priority, standard, or batch tier.

MessageStreamEvent

Union of Anthropic SDK stream event types, re-exported from the anthropic package:

• MessageStartEvent — type: 'message_start', message: Message

• MessageDeltaEvent — type: 'message_delta', delta: Delta, usage: MessageDeltaUsage

• MessageStopEvent — type: 'message_stop'

• ContentBlockStartEvent — type: 'content_block_start', index: int, content_block: ContentBlock

• ContentBlockDeltaEvent — type: 'content_block_delta', index: int, delta: RawContentBlockDelta

• ContentBlockStopEvent — type: 'content_block_stop', index: int

Import: from any_llm.types.messages import MessageStreamEvent

Internal Types

MessagesParams

Normalized parameters for the Anthropic Messages API, used internally to pass structured parameters from the public API to provider implementations.

Import: from any_llm.types.messages import MessagesParams

Field	Type	Description
model	str	Model identifier
messages	list[dict[str, Any]]	List of messages for the conversation
max_tokens	int	Maximum number of tokens to generate (required by Anthropic API)
system	str | list[dict[str, Any]] | None	System prompt (string or list of content blocks with optional cache_control)
temperature	float | None	Controls randomness in the response (0.0 to 1.0)
top_p	float | None	Controls diversity via nucleus sampling
top_k	int | None	Only sample from the top K options for each subsequent token
stream	bool | None	Whether to stream the response
stop_sequences	list[str] | None	Custom text sequences that will cause the model to stop generating
tools	list[dict[str, Any]] | None	List of tools in Anthropic format ({name, description, input_schema})
tool_choice	dict[str, Any] | None	Controls which tool the model uses
metadata	dict[str, Any] | None	Request metadata
thinking	dict[str, Any] | None	Thinking/reasoning configuration
cache_control	dict[str, Any] | None	Cache control configuration for prompt caching
output_format	type | None	Structured-output type, mirroring Anthropic's messages.parse(output_format=...). A Pydantic BaseModel subclass or a dataclass type. The Anthropic provider passes it to native messages.parse (which builds output_config.format from it); other providers route it through the completion bridge. Either way the result is Anthropic's ParsedMessage, with the typed object on its parsed_output property.