anthropic_adapter

Anthropic LLM adapter for Pipecat.

pipecat.adapters.services.anthropic_adapter.is_given(value: _T | NotGiven) → TypeGuard[_T]

Check whether a value was explicitly provided.

Typically used when checking whether a parameter or field typed with Anthropic’s NotGiven was set:

if is_given(system):
    ...

Also acts as a type guard: inside a true branch, the value is narrowed to exclude NotGiven (e.g. str | NotGiven becomes str).

Parameters:

value – The value to check.

Returns:

True if value is anything other than NOT_GIVEN.

class pipecat.adapters.services.anthropic_adapter.AnthropicLLMInvocationParams

Bases: TypedDict

Context-based parameters for invoking Anthropic’s LLM API.

system: str | NotGiven

messages: list[MessageParam]

tools: list[ToolParam | ToolBash20250124Param | CodeExecutionTool20250522Param | CodeExecutionTool20250825Param | CodeExecutionTool20260120Param | MemoryTool20250818Param | ToolTextEditor20250124Param | ToolTextEditor20250429Param | ToolTextEditor20250728Param | WebSearchTool20250305Param | WebFetchTool20250910Param | WebSearchTool20260209Param | WebFetchTool20260209Param | WebFetchTool20260309Param | ToolSearchToolBm25_20251119Param | ToolSearchToolRegex20251119Param]

class pipecat.adapters.services.anthropic_adapter.AnthropicLLMAdapter

Bases: BaseLLMAdapter[AnthropicLLMInvocationParams]

Adapter for converting tool schemas to Anthropic’s function-calling format.

This adapter handles the conversion of Pipecat’s standard function schemas to the specific format required by Anthropic’s Claude models for function calling.

property id_for_llm_specific_messages: str

Get the identifier used in LLMSpecificMessage instances for Anthropic.

get_llm_invocation_params(context: LLMContext, enable_prompt_caching: bool, system_instruction: str | None = None) → AnthropicLLMInvocationParams

Get Anthropic-specific LLM invocation parameters from a universal LLM context.

Parameters:

• context – The LLM context containing messages, tools, etc.

• enable_prompt_caching – Whether prompt caching should be enabled.

• system_instruction – Optional system instruction from service settings or run_inference.

Returns:

Dictionary of parameters for invoking Anthropic’s LLM API.

get_messages_for_logging(context: LLMContext) → list[dict[str, Any]]

Get messages from a universal LLM context in a format ready for logging about Anthropic.

Removes or truncates sensitive data like image content for safe logging.

Parameters:

context – The LLM context containing messages.

Returns:

List of messages in a format ready for logging about Anthropic.

class ConvertedMessages(messages: list[MessageParam], system: str | NotGiven)

Bases: object

Container for Anthropic-formatted messages converted from universal context.

messages: list[MessageParam]

system: str | NotGiven

to_provider_tools_format(tools_schema: ToolsSchema) → list[dict[str, Any]]

Convert function schemas to Anthropic’s function-calling format.

Parameters:

tools_schema – The tools schema containing functions to convert.

Returns:

List of function definitions formatted for Anthropic’s API.