Split Anthropic entity (#147770)

homeassistant/components/anthropic/conversation.py

@@ -1,69 +1,17 @@
 """Conversation support for Anthropic."""
 
-from collections.abc import AsyncGenerator, Callable, Iterable
-import json
-from typing import Any, Literal, cast
-
-import anthropic
-from anthropic import AsyncStream
-from anthropic._types import NOT_GIVEN
-from anthropic.types import (
-    InputJSONDelta,
-    MessageDeltaUsage,
-    MessageParam,
-    MessageStreamEvent,
-    RawContentBlockDeltaEvent,
-    RawContentBlockStartEvent,
-    RawContentBlockStopEvent,
-    RawMessageDeltaEvent,
-    RawMessageStartEvent,
-    RawMessageStopEvent,
-    RedactedThinkingBlock,
-    RedactedThinkingBlockParam,
-    SignatureDelta,
-    TextBlock,
-    TextBlockParam,
-    TextDelta,
-    ThinkingBlock,
-    ThinkingBlockParam,
-    ThinkingConfigDisabledParam,
-    ThinkingConfigEnabledParam,
-    ThinkingDelta,
-    ToolParam,
-    ToolResultBlockParam,
-    ToolUseBlock,
-    ToolUseBlockParam,
-    Usage,
-)
-from voluptuous_openapi import convert
+from typing import Literal
 
 from homeassistant.components import conversation
 from homeassistant.config_entries import ConfigEntry, ConfigSubentry
 from homeassistant.const import CONF_LLM_HASS_API, MATCH_ALL
 from homeassistant.core import HomeAssistant
-from homeassistant.exceptions import HomeAssistantError
-from homeassistant.helpers import device_registry as dr, intent, llm
+from homeassistant.helpers import intent
 from homeassistant.helpers.entity_platform import AddConfigEntryEntitiesCallback
 
 from . import AnthropicConfigEntry
-from .const import (
-    CONF_CHAT_MODEL,
-    CONF_MAX_TOKENS,
-    CONF_PROMPT,
-    CONF_TEMPERATURE,
-    CONF_THINKING_BUDGET,
-    DOMAIN,
-    LOGGER,
-    MIN_THINKING_BUDGET,
-    RECOMMENDED_CHAT_MODEL,
-    RECOMMENDED_MAX_TOKENS,
-    RECOMMENDED_TEMPERATURE,
-    RECOMMENDED_THINKING_BUDGET,
-    THINKING_MODELS,
-)
-
-# Max number of back and forth with the LLM to generate a response
-MAX_TOOL_ITERATIONS = 10
+from .const import CONF_PROMPT, DOMAIN
+from .entity import AnthropicBaseLLMEntity
 
 
 async def async_setup_entry(
@@ -82,253 +30,10 @@ async def async_setup_entry(
         )
 
 
-def _format_tool(
-    tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
-) -> ToolParam:
-    """Format tool specification."""
-    return ToolParam(
-        name=tool.name,
-        description=tool.description or "",
-        input_schema=convert(tool.parameters, custom_serializer=custom_serializer),
-    )
-
-
-def _convert_content(
-    chat_content: Iterable[conversation.Content],
-) -> list[MessageParam]:
-    """Transform HA chat_log content into Anthropic API format."""
-    messages: list[MessageParam] = []
-
-    for content in chat_content:
-        if isinstance(content, conversation.ToolResultContent):
-            tool_result_block = ToolResultBlockParam(
-                type="tool_result",
-                tool_use_id=content.tool_call_id,
-                content=json.dumps(content.tool_result),
-            )
-            if not messages or messages[-1]["role"] != "user":
-                messages.append(
-                    MessageParam(
-                        role="user",
-                        content=[tool_result_block],
-                    )
-                )
-            elif isinstance(messages[-1]["content"], str):
-                messages[-1]["content"] = [
-                    TextBlockParam(type="text", text=messages[-1]["content"]),
-                    tool_result_block,
-                ]
-            else:
-                messages[-1]["content"].append(tool_result_block)  # type: ignore[attr-defined]
-        elif isinstance(content, conversation.UserContent):
-            # Combine consequent user messages
-            if not messages or messages[-1]["role"] != "user":
-                messages.append(
-                    MessageParam(
-                        role="user",
-                        content=content.content,
-                    )
-                )
-            elif isinstance(messages[-1]["content"], str):
-                messages[-1]["content"] = [
-                    TextBlockParam(type="text", text=messages[-1]["content"]),
-                    TextBlockParam(type="text", text=content.content),
-                ]
-            else:
-                messages[-1]["content"].append(  # type: ignore[attr-defined]
-                    TextBlockParam(type="text", text=content.content)
-                )
-        elif isinstance(content, conversation.AssistantContent):
-            # Combine consequent assistant messages
-            if not messages or messages[-1]["role"] != "assistant":
-                messages.append(
-                    MessageParam(
-                        role="assistant",
-                        content=[],
-                    )
-                )
-
-            if content.content:
-                messages[-1]["content"].append(  # type: ignore[union-attr]
-                    TextBlockParam(type="text", text=content.content)
-                )
-            if content.tool_calls:
-                messages[-1]["content"].extend(  # type: ignore[union-attr]
-                    [
-                        ToolUseBlockParam(
-                            type="tool_use",
-                            id=tool_call.id,
-                            name=tool_call.tool_name,
-                            input=tool_call.tool_args,
-                        )
-                        for tool_call in content.tool_calls
-                    ]
-                )
-        else:
-            # Note: We don't pass SystemContent here as its passed to the API as the prompt
-            raise TypeError(f"Unexpected content type: {type(content)}")
-
-    return messages
-
-
-async def _transform_stream(  # noqa: C901 - This is complex, but better to have it in one place
-    chat_log: conversation.ChatLog,
-    result: AsyncStream[MessageStreamEvent],
-    messages: list[MessageParam],
-) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
-    """Transform the response stream into HA format.
-
-    A typical stream of responses might look something like the following:
-    - RawMessageStartEvent with no content
-    - RawContentBlockStartEvent with an empty ThinkingBlock (if extended thinking is enabled)
-    - RawContentBlockDeltaEvent with a ThinkingDelta
-    - RawContentBlockDeltaEvent with a ThinkingDelta
-    - RawContentBlockDeltaEvent with a ThinkingDelta
-    - ...
-    - RawContentBlockDeltaEvent with a SignatureDelta
-    - RawContentBlockStopEvent
-    - RawContentBlockStartEvent with a RedactedThinkingBlock (occasionally)
-    - RawContentBlockStopEvent (RedactedThinkingBlock does not have a delta)
-    - RawContentBlockStartEvent with an empty TextBlock
-    - RawContentBlockDeltaEvent with a TextDelta
-    - RawContentBlockDeltaEvent with a TextDelta
-    - RawContentBlockDeltaEvent with a TextDelta
-    - ...
-    - RawContentBlockStopEvent
-    - RawContentBlockStartEvent with ToolUseBlock specifying the function name
-    - RawContentBlockDeltaEvent with a InputJSONDelta
-    - RawContentBlockDeltaEvent with a InputJSONDelta
-    - ...
-    - RawContentBlockStopEvent
-    - RawMessageDeltaEvent with a stop_reason='tool_use'
-    - RawMessageStopEvent(type='message_stop')
-
-    Each message could contain multiple blocks of the same type.
-    """
-    if result is None:
-        raise TypeError("Expected a stream of messages")
-
-    current_message: MessageParam | None = None
-    current_block: (
-        TextBlockParam
-        | ToolUseBlockParam
-        | ThinkingBlockParam
-        | RedactedThinkingBlockParam
-        | None
-    ) = None
-    current_tool_args: str
-    input_usage: Usage | None = None
-
-    async for response in result:
-        LOGGER.debug("Received response: %s", response)
-
-        if isinstance(response, RawMessageStartEvent):
-            if response.message.role != "assistant":
-                raise ValueError("Unexpected message role")
-            current_message = MessageParam(role=response.message.role, content=[])
-            input_usage = response.message.usage
-        elif isinstance(response, RawContentBlockStartEvent):
-            if isinstance(response.content_block, ToolUseBlock):
-                current_block = ToolUseBlockParam(
-                    type="tool_use",
-                    id=response.content_block.id,
-                    name=response.content_block.name,
-                    input="",
-                )
-                current_tool_args = ""
-            elif isinstance(response.content_block, TextBlock):
-                current_block = TextBlockParam(
-                    type="text", text=response.content_block.text
-                )
-                yield {"role": "assistant"}
-                if response.content_block.text:
-                    yield {"content": response.content_block.text}
-            elif isinstance(response.content_block, ThinkingBlock):
-                current_block = ThinkingBlockParam(
-                    type="thinking",
-                    thinking=response.content_block.thinking,
-                    signature=response.content_block.signature,
-                )
-            elif isinstance(response.content_block, RedactedThinkingBlock):
-                current_block = RedactedThinkingBlockParam(
-                    type="redacted_thinking", data=response.content_block.data
-                )
-                LOGGER.debug(
-                    "Some of Claude’s internal reasoning has been automatically "
-                    "encrypted for safety reasons. This doesn’t affect the quality of "
-                    "responses"
-                )
-        elif isinstance(response, RawContentBlockDeltaEvent):
-            if current_block is None:
-                raise ValueError("Unexpected delta without a block")
-            if isinstance(response.delta, InputJSONDelta):
-                current_tool_args += response.delta.partial_json
-            elif isinstance(response.delta, TextDelta):
-                text_block = cast(TextBlockParam, current_block)
-                text_block["text"] += response.delta.text
-                yield {"content": response.delta.text}
-            elif isinstance(response.delta, ThinkingDelta):
-                thinking_block = cast(ThinkingBlockParam, current_block)
-                thinking_block["thinking"] += response.delta.thinking
-            elif isinstance(response.delta, SignatureDelta):
-                thinking_block = cast(ThinkingBlockParam, current_block)
-                thinking_block["signature"] += response.delta.signature
-        elif isinstance(response, RawContentBlockStopEvent):
-            if current_block is None:
-                raise ValueError("Unexpected stop event without a current block")
-            if current_block["type"] == "tool_use":
-                # tool block
-                tool_args = json.loads(current_tool_args) if current_tool_args else {}
-                current_block["input"] = tool_args
-                yield {
-                    "tool_calls": [
-                        llm.ToolInput(
-                            id=current_block["id"],
-                            tool_name=current_block["name"],
-                            tool_args=tool_args,
-                        )
-                    ]
-                }
-            elif current_block["type"] == "thinking":
-                # thinking block
-                LOGGER.debug("Thinking: %s", current_block["thinking"])
-
-            if current_message is None:
-                raise ValueError("Unexpected stop event without a current message")
-            current_message["content"].append(current_block)  # type: ignore[union-attr]
-            current_block = None
-        elif isinstance(response, RawMessageDeltaEvent):
-            if (usage := response.usage) is not None:
-                chat_log.async_trace(_create_token_stats(input_usage, usage))
-            if response.delta.stop_reason == "refusal":
-                raise HomeAssistantError("Potential policy violation detected")
-        elif isinstance(response, RawMessageStopEvent):
-            if current_message is not None:
-                messages.append(current_message)
-                current_message = None
-
-
-def _create_token_stats(
-    input_usage: Usage | None, response_usage: MessageDeltaUsage
-) -> dict[str, Any]:
-    """Create token stats for conversation agent tracing."""
-    input_tokens = 0
-    cached_input_tokens = 0
-    if input_usage:
-        input_tokens = input_usage.input_tokens
-        cached_input_tokens = input_usage.cache_creation_input_tokens or 0
-    output_tokens = response_usage.output_tokens
-    return {
-        "stats": {
-            "input_tokens": input_tokens,
-            "cached_input_tokens": cached_input_tokens,
-            "output_tokens": output_tokens,
-        }
-    }
-
-
 class AnthropicConversationEntity(
-    conversation.ConversationEntity, conversation.AbstractConversationAgent
+    conversation.ConversationEntity,
+    conversation.AbstractConversationAgent,
+    AnthropicBaseLLMEntity,
 ):
     """Anthropic conversation agent."""
 
@@ -336,17 +41,7 @@ class AnthropicConversationEntity(
 
     def __init__(self, entry: AnthropicConfigEntry, subentry: ConfigSubentry) -> None:
         """Initialize the agent."""
-        self.entry = entry
-        self.subentry = subentry
-        self._attr_name = subentry.title
-        self._attr_unique_id = subentry.subentry_id
-        self._attr_device_info = dr.DeviceInfo(
-            identifiers={(DOMAIN, subentry.subentry_id)},
-            name=subentry.title,
-            manufacturer="Anthropic",
-            model="Claude",
-            entry_type=dr.DeviceEntryType.SERVICE,
-        )
+        super().__init__(entry, subentry)
         if self.subentry.data.get(CONF_LLM_HASS_API):
             self._attr_supported_features = (
                 conversation.ConversationEntityFeature.CONTROL
@@ -395,73 +90,6 @@ class AnthropicConversationEntity(
             continue_conversation=chat_log.continue_conversation,
         )
 
-    async def _async_handle_chat_log(
-        self,
-        chat_log: conversation.ChatLog,
-    ) -> None:
-        """Generate an answer for the chat log."""
-        options = self.subentry.data
-
-        tools: list[ToolParam] | None = None
-        if chat_log.llm_api:
-            tools = [
-                _format_tool(tool, chat_log.llm_api.custom_serializer)
-                for tool in chat_log.llm_api.tools
-            ]
-
-        system = chat_log.content[0]
-        if not isinstance(system, conversation.SystemContent):
-            raise TypeError("First message must be a system message")
-        messages = _convert_content(chat_log.content[1:])
-
-        client = self.entry.runtime_data
-
-        thinking_budget = options.get(CONF_THINKING_BUDGET, RECOMMENDED_THINKING_BUDGET)
-        model = options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL)
-
-        # To prevent infinite loops, we limit the number of iterations
-        for _iteration in range(MAX_TOOL_ITERATIONS):
-            model_args = {
-                "model": model,
-                "messages": messages,
-                "tools": tools or NOT_GIVEN,
-                "max_tokens": options.get(CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS),
-                "system": system.content,
-                "stream": True,
-            }
-            if model in THINKING_MODELS and thinking_budget >= MIN_THINKING_BUDGET:
-                model_args["thinking"] = ThinkingConfigEnabledParam(
-                    type="enabled", budget_tokens=thinking_budget
-                )
-            else:
-                model_args["thinking"] = ThinkingConfigDisabledParam(type="disabled")
-                model_args["temperature"] = options.get(
-                    CONF_TEMPERATURE, RECOMMENDED_TEMPERATURE
-                )
-
-            try:
-                stream = await client.messages.create(**model_args)
-            except anthropic.AnthropicError as err:
-                raise HomeAssistantError(
-                    f"Sorry, I had a problem talking to Anthropic: {err}"
-                ) from err
-
-            messages.extend(
-                _convert_content(
-                    [
-                        content
-                        async for content in chat_log.async_add_delta_content_stream(
-                            self.entity_id,
-                            _transform_stream(chat_log, stream, messages),
-                        )
-                        if not isinstance(content, conversation.AssistantContent)
-                    ]
-                )
-            )
-
-            if not chat_log.unresponded_tool_results:
-                break
-
     async def _async_entry_update_listener(
         self, hass: HomeAssistant, entry: ConfigEntry
     ) -> None:

homeassistant/components/anthropic/entity.py

@@ -0,0 +1,393 @@
+"""Base entity for Anthropic."""
+
+from collections.abc import AsyncGenerator, Callable, Iterable
+import json
+from typing import Any, cast
+
+import anthropic
+from anthropic import AsyncStream
+from anthropic._types import NOT_GIVEN
+from anthropic.types import (
+    InputJSONDelta,
+    MessageDeltaUsage,
+    MessageParam,
+    MessageStreamEvent,
+    RawContentBlockDeltaEvent,
+    RawContentBlockStartEvent,
+    RawContentBlockStopEvent,
+    RawMessageDeltaEvent,
+    RawMessageStartEvent,
+    RawMessageStopEvent,
+    RedactedThinkingBlock,
+    RedactedThinkingBlockParam,
+    SignatureDelta,
+    TextBlock,
+    TextBlockParam,
+    TextDelta,
+    ThinkingBlock,
+    ThinkingBlockParam,
+    ThinkingConfigDisabledParam,
+    ThinkingConfigEnabledParam,
+    ThinkingDelta,
+    ToolParam,
+    ToolResultBlockParam,
+    ToolUseBlock,
+    ToolUseBlockParam,
+    Usage,
+)
+from voluptuous_openapi import convert
+
+from homeassistant.components import conversation
+from homeassistant.config_entries import ConfigSubentry
+from homeassistant.exceptions import HomeAssistantError
+from homeassistant.helpers import device_registry as dr, llm
+from homeassistant.helpers.entity import Entity
+
+from . import AnthropicConfigEntry
+from .const import (
+    CONF_CHAT_MODEL,
+    CONF_MAX_TOKENS,
+    CONF_TEMPERATURE,
+    CONF_THINKING_BUDGET,
+    DOMAIN,
+    LOGGER,
+    MIN_THINKING_BUDGET,
+    RECOMMENDED_CHAT_MODEL,
+    RECOMMENDED_MAX_TOKENS,
+    RECOMMENDED_TEMPERATURE,
+    RECOMMENDED_THINKING_BUDGET,
+    THINKING_MODELS,
+)
+
+# Max number of back and forth with the LLM to generate a response
+MAX_TOOL_ITERATIONS = 10
+
+
+def _format_tool(
+    tool: llm.Tool, custom_serializer: Callable[[Any], Any] | None
+) -> ToolParam:
+    """Format tool specification."""
+    return ToolParam(
+        name=tool.name,
+        description=tool.description or "",
+        input_schema=convert(tool.parameters, custom_serializer=custom_serializer),
+    )
+
+
+def _convert_content(
+    chat_content: Iterable[conversation.Content],
+) -> list[MessageParam]:
+    """Transform HA chat_log content into Anthropic API format."""
+    messages: list[MessageParam] = []
+
+    for content in chat_content:
+        if isinstance(content, conversation.ToolResultContent):
+            tool_result_block = ToolResultBlockParam(
+                type="tool_result",
+                tool_use_id=content.tool_call_id,
+                content=json.dumps(content.tool_result),
+            )
+            if not messages or messages[-1]["role"] != "user":
+                messages.append(
+                    MessageParam(
+                        role="user",
+                        content=[tool_result_block],
+                    )
+                )
+            elif isinstance(messages[-1]["content"], str):
+                messages[-1]["content"] = [
+                    TextBlockParam(type="text", text=messages[-1]["content"]),
+                    tool_result_block,
+                ]
+            else:
+                messages[-1]["content"].append(tool_result_block)  # type: ignore[attr-defined]
+        elif isinstance(content, conversation.UserContent):
+            # Combine consequent user messages
+            if not messages or messages[-1]["role"] != "user":
+                messages.append(
+                    MessageParam(
+                        role="user",
+                        content=content.content,
+                    )
+                )
+            elif isinstance(messages[-1]["content"], str):
+                messages[-1]["content"] = [
+                    TextBlockParam(type="text", text=messages[-1]["content"]),
+                    TextBlockParam(type="text", text=content.content),
+                ]
+            else:
+                messages[-1]["content"].append(  # type: ignore[attr-defined]
+                    TextBlockParam(type="text", text=content.content)
+                )
+        elif isinstance(content, conversation.AssistantContent):
+            # Combine consequent assistant messages
+            if not messages or messages[-1]["role"] != "assistant":
+                messages.append(
+                    MessageParam(
+                        role="assistant",
+                        content=[],
+                    )
+                )
+
+            if content.content:
+                messages[-1]["content"].append(  # type: ignore[union-attr]
+                    TextBlockParam(type="text", text=content.content)
+                )
+            if content.tool_calls:
+                messages[-1]["content"].extend(  # type: ignore[union-attr]
+                    [
+                        ToolUseBlockParam(
+                            type="tool_use",
+                            id=tool_call.id,
+                            name=tool_call.tool_name,
+                            input=tool_call.tool_args,
+                        )
+                        for tool_call in content.tool_calls
+                    ]
+                )
+        else:
+            # Note: We don't pass SystemContent here as its passed to the API as the prompt
+            raise TypeError(f"Unexpected content type: {type(content)}")
+
+    return messages
+
+
+async def _transform_stream(  # noqa: C901 - This is complex, but better to have it in one place
+    chat_log: conversation.ChatLog,
+    result: AsyncStream[MessageStreamEvent],
+    messages: list[MessageParam],
+) -> AsyncGenerator[conversation.AssistantContentDeltaDict]:
+    """Transform the response stream into HA format.
+
+    A typical stream of responses might look something like the following:
+    - RawMessageStartEvent with no content
+    - RawContentBlockStartEvent with an empty ThinkingBlock (if extended thinking is enabled)
+    - RawContentBlockDeltaEvent with a ThinkingDelta
+    - RawContentBlockDeltaEvent with a ThinkingDelta
+    - RawContentBlockDeltaEvent with a ThinkingDelta
+    - ...
+    - RawContentBlockDeltaEvent with a SignatureDelta
+    - RawContentBlockStopEvent
+    - RawContentBlockStartEvent with a RedactedThinkingBlock (occasionally)
+    - RawContentBlockStopEvent (RedactedThinkingBlock does not have a delta)
+    - RawContentBlockStartEvent with an empty TextBlock
+    - RawContentBlockDeltaEvent with a TextDelta
+    - RawContentBlockDeltaEvent with a TextDelta
+    - RawContentBlockDeltaEvent with a TextDelta
+    - ...
+    - RawContentBlockStopEvent
+    - RawContentBlockStartEvent with ToolUseBlock specifying the function name
+    - RawContentBlockDeltaEvent with a InputJSONDelta
+    - RawContentBlockDeltaEvent with a InputJSONDelta
+    - ...
+    - RawContentBlockStopEvent
+    - RawMessageDeltaEvent with a stop_reason='tool_use'
+    - RawMessageStopEvent(type='message_stop')
+
+    Each message could contain multiple blocks of the same type.
+    """
+    if result is None:
+        raise TypeError("Expected a stream of messages")
+
+    current_message: MessageParam | None = None
+    current_block: (
+        TextBlockParam
+        | ToolUseBlockParam
+        | ThinkingBlockParam
+        | RedactedThinkingBlockParam
+        | None
+    ) = None
+    current_tool_args: str
+    input_usage: Usage | None = None
+
+    async for response in result:
+        LOGGER.debug("Received response: %s", response)
+
+        if isinstance(response, RawMessageStartEvent):
+            if response.message.role != "assistant":
+                raise ValueError("Unexpected message role")
+            current_message = MessageParam(role=response.message.role, content=[])
+            input_usage = response.message.usage
+        elif isinstance(response, RawContentBlockStartEvent):
+            if isinstance(response.content_block, ToolUseBlock):
+                current_block = ToolUseBlockParam(
+                    type="tool_use",
+                    id=response.content_block.id,
+                    name=response.content_block.name,
+                    input="",
+                )
+                current_tool_args = ""
+            elif isinstance(response.content_block, TextBlock):
+                current_block = TextBlockParam(
+                    type="text", text=response.content_block.text
+                )
+                yield {"role": "assistant"}
+                if response.content_block.text:
+                    yield {"content": response.content_block.text}
+            elif isinstance(response.content_block, ThinkingBlock):
+                current_block = ThinkingBlockParam(
+                    type="thinking",
+                    thinking=response.content_block.thinking,
+                    signature=response.content_block.signature,
+                )
+            elif isinstance(response.content_block, RedactedThinkingBlock):
+                current_block = RedactedThinkingBlockParam(
+                    type="redacted_thinking", data=response.content_block.data
+                )
+                LOGGER.debug(
+                    "Some of Claude’s internal reasoning has been automatically "
+                    "encrypted for safety reasons. This doesn’t affect the quality of "
+                    "responses"
+                )
+        elif isinstance(response, RawContentBlockDeltaEvent):
+            if current_block is None:
+                raise ValueError("Unexpected delta without a block")
+            if isinstance(response.delta, InputJSONDelta):
+                current_tool_args += response.delta.partial_json
+            elif isinstance(response.delta, TextDelta):
+                text_block = cast(TextBlockParam, current_block)
+                text_block["text"] += response.delta.text
+                yield {"content": response.delta.text}
+            elif isinstance(response.delta, ThinkingDelta):
+                thinking_block = cast(ThinkingBlockParam, current_block)
+                thinking_block["thinking"] += response.delta.thinking
+            elif isinstance(response.delta, SignatureDelta):
+                thinking_block = cast(ThinkingBlockParam, current_block)
+                thinking_block["signature"] += response.delta.signature
+        elif isinstance(response, RawContentBlockStopEvent):
+            if current_block is None:
+                raise ValueError("Unexpected stop event without a current block")
+            if current_block["type"] == "tool_use":
+                # tool block
+                tool_args = json.loads(current_tool_args) if current_tool_args else {}
+                current_block["input"] = tool_args
+                yield {
+                    "tool_calls": [
+                        llm.ToolInput(
+                            id=current_block["id"],
+                            tool_name=current_block["name"],
+                            tool_args=tool_args,
+                        )
+                    ]
+                }
+            elif current_block["type"] == "thinking":
+                # thinking block
+                LOGGER.debug("Thinking: %s", current_block["thinking"])
+
+            if current_message is None:
+                raise ValueError("Unexpected stop event without a current message")
+            current_message["content"].append(current_block)  # type: ignore[union-attr]
+            current_block = None
+        elif isinstance(response, RawMessageDeltaEvent):
+            if (usage := response.usage) is not None:
+                chat_log.async_trace(_create_token_stats(input_usage, usage))
+            if response.delta.stop_reason == "refusal":
+                raise HomeAssistantError("Potential policy violation detected")
+        elif isinstance(response, RawMessageStopEvent):
+            if current_message is not None:
+                messages.append(current_message)
+                current_message = None
+
+
+def _create_token_stats(
+    input_usage: Usage | None, response_usage: MessageDeltaUsage
+) -> dict[str, Any]:
+    """Create token stats for conversation agent tracing."""
+    input_tokens = 0
+    cached_input_tokens = 0
+    if input_usage:
+        input_tokens = input_usage.input_tokens
+        cached_input_tokens = input_usage.cache_creation_input_tokens or 0
+    output_tokens = response_usage.output_tokens
+    return {
+        "stats": {
+            "input_tokens": input_tokens,
+            "cached_input_tokens": cached_input_tokens,
+            "output_tokens": output_tokens,
+        }
+    }
+
+
+class AnthropicBaseLLMEntity(Entity):
+    """Anthropic base LLM entity."""
+
+    def __init__(self, entry: AnthropicConfigEntry, subentry: ConfigSubentry) -> None:
+        """Initialize the entity."""
+        self.entry = entry
+        self.subentry = subentry
+        self._attr_name = subentry.title
+        self._attr_unique_id = subentry.subentry_id
+        self._attr_device_info = dr.DeviceInfo(
+            identifiers={(DOMAIN, subentry.subentry_id)},
+            name=subentry.title,
+            manufacturer="Anthropic",
+            model="Claude",
+            entry_type=dr.DeviceEntryType.SERVICE,
+        )
+
+    async def _async_handle_chat_log(
+        self,
+        chat_log: conversation.ChatLog,
+    ) -> None:
+        """Generate an answer for the chat log."""
+        options = self.subentry.data
+
+        tools: list[ToolParam] | None = None
+        if chat_log.llm_api:
+            tools = [
+                _format_tool(tool, chat_log.llm_api.custom_serializer)
+                for tool in chat_log.llm_api.tools
+            ]
+
+        system = chat_log.content[0]
+        if not isinstance(system, conversation.SystemContent):
+            raise TypeError("First message must be a system message")
+        messages = _convert_content(chat_log.content[1:])
+
+        client = self.entry.runtime_data
+
+        thinking_budget = options.get(CONF_THINKING_BUDGET, RECOMMENDED_THINKING_BUDGET)
+        model = options.get(CONF_CHAT_MODEL, RECOMMENDED_CHAT_MODEL)
+
+        # To prevent infinite loops, we limit the number of iterations
+        for _iteration in range(MAX_TOOL_ITERATIONS):
+            model_args = {
+                "model": model,
+                "messages": messages,
+                "tools": tools or NOT_GIVEN,
+                "max_tokens": options.get(CONF_MAX_TOKENS, RECOMMENDED_MAX_TOKENS),
+                "system": system.content,
+                "stream": True,
+            }
+            if model in THINKING_MODELS and thinking_budget >= MIN_THINKING_BUDGET:
+                model_args["thinking"] = ThinkingConfigEnabledParam(
+                    type="enabled", budget_tokens=thinking_budget
+                )
+            else:
+                model_args["thinking"] = ThinkingConfigDisabledParam(type="disabled")
+                model_args["temperature"] = options.get(
+                    CONF_TEMPERATURE, RECOMMENDED_TEMPERATURE
+                )
+
+            try:
+                stream = await client.messages.create(**model_args)
+            except anthropic.AnthropicError as err:
+                raise HomeAssistantError(
+                    f"Sorry, I had a problem talking to Anthropic: {err}"
+                ) from err
+
+            messages.extend(
+                _convert_content(
+                    [
+                        content
+                        async for content in chat_log.async_add_delta_content_stream(
+                            self.entity_id,
+                            _transform_stream(chat_log, stream, messages),
+                        )
+                        if not isinstance(content, conversation.AssistantContent)
+                    ]
+                )
+            )
+
+            if not chat_log.unresponded_tool_results:
+                break

tests/components/anthropic/test_conversation.py

@@ -316,7 +316,7 @@ async def test_conversation_agent(
     assert agent.supported_languages == "*"
 
 
-@patch("homeassistant.components.anthropic.conversation.llm.AssistAPI._async_get_tools")
+@patch("homeassistant.components.anthropic.entity.llm.AssistAPI._async_get_tools")
 @pytest.mark.parametrize(
     ("tool_call_json_parts", "expected_call_tool_args"),
     [
@@ -430,7 +430,7 @@ async def test_function_call(
     )
 
 
-@patch("homeassistant.components.anthropic.conversation.llm.AssistAPI._async_get_tools")
+@patch("homeassistant.components.anthropic.entity.llm.AssistAPI._async_get_tools")
 async def test_function_exception(
     mock_get_tools,
     hass: HomeAssistant,
@@ -760,7 +760,7 @@ async def test_redacted_thinking(
     assert chat_log.content[2].content == "How can I help you today?"
 
 
-@patch("homeassistant.components.anthropic.conversation.llm.AssistAPI._async_get_tools")
+@patch("homeassistant.components.anthropic.entity.llm.AssistAPI._async_get_tools")
 async def test_extended_thinking_tool_call(
     mock_get_tools,
     hass: HomeAssistant,