Messages Types

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

Primary Types

Full response from the Messages API.

Import: from any_llm.types.messages import MessageResponse

Content block in a Messages API response.

Import: from any_llm.types.messages import MessageContentBlock

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.
`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.

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

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