Completion Types

The completion types used by any_llm.completion() and any_llm.acompletion() are re-exports from the OpenAI Python SDK, extended where needed to support additional fields like reasoning content.

Primary Types

ChatCompletion

The response object for a non-streaming completion request. Extends openai.types.chat.ChatCompletion with support for reasoning content in the message choices.

Import: from any_llm.types.completion import ChatCompletion

Key fields:

Field	Type	Description
choices	list[Choice]

ChatCompletionChunk

A single chunk in a streaming completion response. Extends openai.types.chat.ChatCompletionChunk.

Import: from any_llm.types.completion import ChatCompletionChunk

Key fields:

Field	Type	Description
id	str	Completion identifier (same across all chunks).
choices	list[ChunkChoice]	Each chunk choice has a delta with incremental content, role, and optionally reasoning.
model	str	The model used.

ChatCompletionMessage

A message within a completion response. Extends openai.types.chat.ChatCompletionMessage with a reasoning field.

Import: from any_llm.types.completion import ChatCompletionMessage

Field	Type	Description
role	str	Message role (e.g., "assistant").
content	str | None	Text content of the message.
reasoning	Reasoning | None	Reasoning/thinking content (when the model supports it).
tool_calls	list[ChatCompletionMessageToolCall] | None	Tool calls requested by the model.
annotations	list[dict] | None	Annotations attached to the message.

ParsedChatCompletion

Returned when response_format is a Pydantic BaseModel subclass or a dataclass type. Extends ChatCompletion with a generic type parameter.

Import: from any_llm import ParsedChatCompletion

Access the parsed object via response.choices[0].message.parsed, which will be an instance of the type passed as response_format.

CreateEmbeddingResponse

Response object for embedding requests. Re-exported directly from openai.types.CreateEmbeddingResponse.

Import: from any_llm.types.completion import CreateEmbeddingResponse

Field	Type	Description
data	list[Embedding]	List of embedding objects, each with an embedding vector and index.
model	str	The model used.
usage	Usage	Token usage with prompt_tokens and total_tokens.

ReasoningEffort

A literal type controlling reasoning depth for models that support it.

Import: from any_llm.types.completion import ReasoningEffort

ReasoningEffort = Literal["none", "minimal", "low", "medium", "high", "xhigh", "auto"]

The value "auto" (the default) maps to each provider’s own default reasoning level.

Internal Types

CompletionParams

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

Import: from any_llm.types.completion import CompletionParams

Field	Type	Description
model_id	str	Model identifier (e.g., ‘mistral-small-latest’)
messages	list[dict[str, Any]]	List of messages for the conversation
tools	list[dict[str, Any] | Any] | None	List of tools for tool calling. Should be converted to OpenAI tool format dicts
tool_choice	str | dict[str, Any] | None	Controls which tools the model can call
temperature	float | None	Controls randomness in the response (0.0 to 2.0)
top_p	float | None	Controls diversity via nucleus sampling (0.0 to 1.0)
max_tokens	int | None	Maximum number of tokens to generate
response_format	dict[str, Any] | type | None	Format specification for the response. Accepts Pydantic BaseModel subclasses, dataclass types, or dicts.
stream	bool | None	Whether to stream the response
n	int | None	Number of completions to generate
stop	str | list[str] | None	Stop sequences for generation
presence_penalty	float | None	Penalize new tokens based on presence in text
frequency_penalty	float | None	Penalize new tokens based on frequency in text
seed	int | None	Random seed for reproducible results
user	str | None	Unique identifier for the end user
parallel_tool_calls	bool | None	Whether to allow parallel tool calls
logprobs	bool | None	Include token-level log probabilities in the response
top_logprobs	int | None	Number of top alternatives to return when logprobs are requested
logit_bias	dict[str, float] | None	Bias the likelihood of specified tokens during generation
stream_options	dict[str, Any] | None	Additional options controlling streaming behavior
max_completion_tokens	int | None	Maximum number of tokens for the completion (provider-dependent)
reasoning_effort	Literal['none', 'minimal', 'low', 'medium', 'high', 'xhigh', 'auto'] | None

Additional Re-exports

The following types are also available from any_llm.types.completion:

Type	Origin	Description
CompletionUsage	openai.types.CompletionUsage	Token usage counts.
Function	openai.types.chat	Function definition within a tool call.
Embedding	openai.types.Embedding	Single embedding vector with index.
ChoiceDeltaToolCall	openai.types.chat	Tool call delta in streaming chunks.

For full field-level documentation of the base OpenAI types, see the OpenAI Python SDK reference.