Merge pull request #2574 from pipecat-ai/pk/expand-universal-llm-context-support-to-anthropic

Expand universal `LLMContext` support to Anthropic

CHANGELOG.md

@@ -7,6 +7,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Added
+
+- Expanded support for universal `LLMContext` to the Anthropic LLM service.
+  Using the universal `LLMContext` and associated `LLMContextAggregatorPair` is
+  a pre-requisite for using `LLMSwitcher` to switch between LLMs at runtime.
+
 ### Fixed
 
 - Fixed a `AWSBedrockLLMService` crash caused by an extra `await`.
@@ -19,7 +25,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 
 - Added multilingual support for AsyncAI in `AsyncAITTSService` and `AsyncAIHttpTTSService`.
-  - New `languages`:  `es`, `fr`, `de`, `it`.
+
+  - New `languages`: `es`, `fr`, `de`, `it`.
 
 - Added new frames `InputTransportMessageUrgentFrame` and
   `DailyInputTransportMessageUrgentFrame` for transport messages received from

examples/foundational/14b-function-calling-anthropic-video.py

@@ -97,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     llm = AnthropicLLMService(
         api_key=os.getenv("ANTHROPIC_API_KEY"),
         model="claude-3-7-sonnet-latest",
-        enable_prompt_caching_beta=True,
+        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
     )
     llm.register_function("get_weather", get_weather)
     llm.register_function("get_image", get_image)

examples/foundational/14z-function-calling-anthropic-universal-context.py

@@ -0,0 +1,211 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+
+import asyncio
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMRunFrame
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
+from pipecat.runner.types import RunnerArguments
+from pipecat.runner.utils import (
+    create_transport,
+    get_transport_client_id,
+    maybe_capture_participant_camera,
+)
+from pipecat.services.anthropic.llm import AnthropicLLMService
+from pipecat.services.cartesia.tts import CartesiaTTSService
+from pipecat.services.deepgram.stt import DeepgramSTTService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import BaseTransport, TransportParams
+from pipecat.transports.services.daily import DailyParams
+
+load_dotenv(override=True)
+
+
+# Global variable to store the client ID
+client_id = ""
+
+
+async def get_weather(params: FunctionCallParams):
+    location = params.arguments["location"]
+    await params.result_callback(f"The weather in {location} is currently 72 degrees and sunny.")
+
+
+async def get_image(params: FunctionCallParams):
+    question = params.arguments["question"]
+    logger.debug(f"Requesting image with user_id={client_id}, question={question}")
+
+    # Request the image frame
+    await params.llm.request_image_frame(
+        user_id=client_id,
+        function_name=params.function_name,
+        tool_call_id=params.tool_call_id,
+        text_content=question,
+    )
+
+    # Wait a short time for the frame to be processed
+    await asyncio.sleep(0.5)
+
+    # Return a result to complete the function call
+    await params.result_callback(
+        f"I've captured an image from your camera and I'm analyzing what you asked about: {question}"
+    )
+
+
+# We store functions so objects (e.g. SileroVADAnalyzer) don't get
+# instantiated. The function will be called when the desired transport gets
+# selected.
+transport_params = {
+    "daily": lambda: DailyParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+    "webrtc": lambda: TransportParams(
+        audio_in_enabled=True,
+        audio_out_enabled=True,
+        video_in_enabled=True,
+        vad_analyzer=SileroVADAnalyzer(),
+    ),
+}
+
+
+async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
+    logger.info(f"Starting bot")
+
+    stt = DeepgramSTTService(api_key=os.getenv("DEEPGRAM_API_KEY"))
+
+    tts = CartesiaTTSService(
+        api_key=os.getenv("CARTESIA_API_KEY"),
+        voice_id="71a7ad14-091c-4e8e-a314-022ece01c121",  # British Reading Lady
+    )
+
+    llm = AnthropicLLMService(
+        api_key=os.getenv("ANTHROPIC_API_KEY"),
+        model="claude-3-7-sonnet-latest",
+        params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
+    )
+    llm.register_function("get_weather", get_weather)
+    llm.register_function("get_image", get_image)
+
+    weather_function = FunctionSchema(
+        name="get_weather",
+        description="Get the current weather",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+        },
+        required=["location"],
+    )
+    get_image_function = FunctionSchema(
+        name="get_image",
+        description="Get an image from the video stream.",
+        properties={
+            "question": {
+                "type": "string",
+                "description": "The question that the user is asking about the image.",
+            }
+        },
+        required=["question"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function, get_image_function])
+
+    system_prompt = """\
+You are a helpful assistant who converses with a user and answers questions. Respond concisely to general questions.
+
+Your response will be turned into speech so use only simple words and punctuation.
+
+You have access to two tools: get_weather and get_image.
+
+You can respond to questions about the weather using the get_weather tool.
+
+You can answer questions about the user's video stream using the get_image tool. Some examples of phrases that \
+indicate you should use the get_image tool are:
+- What do you see?
+- What's in the video?
+- Can you describe the video?
+- Tell me about what you see.
+- Tell me something interesting about what you see.
+- What's happening in the video?
+
+If you need to use a tool, simply use the tool. Do not tell the user the tool you are using. Be brief and concise.
+    """
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": "Start the conversation by introducing yourself."},
+    ]
+
+    context = LLMContext(messages, tools)
+    context_aggregator = LLMContextAggregatorPair(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),  # Transport user input
+            stt,  # STT
+            context_aggregator.user(),  # User speech to text
+            llm,  # LLM
+            tts,  # TTS
+            transport.output(),  # Transport bot output
+            context_aggregator.assistant(),  # Assistant spoken responses and tool context
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            enable_metrics=True,
+            enable_usage_metrics=True,
+        ),
+        idle_timeout_secs=runner_args.pipeline_idle_timeout_secs,
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected: {client}")
+
+        await maybe_capture_participant_camera(transport, client)
+
+        global client_id
+        client_id = get_transport_client_id(transport, client)
+
+        # Kick off the conversation.
+        await task.queue_frames([LLMRunFrame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=runner_args.handle_sigint)
+
+    await runner.run(task)
+
+
+async def bot(runner_args: RunnerArguments):
+    """Main bot entry point compatible with Pipecat Cloud."""
+    transport = await create_transport(runner_args, transport_params)
+    await run_bot(transport, runner_args)
+
+
+if __name__ == "__main__":
+    from pipecat.runner.run import main
+
+    main()

src/pipecat/adapters/base_llm_adapter.py

@@ -39,11 +39,12 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
     """
 
     @abstractmethod
-    def get_llm_invocation_params(self, context: LLMContext) -> TLLMInvocationParams:
+    def get_llm_invocation_params(self, context: LLMContext, **kwargs) -> TLLMInvocationParams:
         """Get provider-specific LLM invocation parameters from a universal LLM context.
 
         Args:
             context: The LLM context containing messages, tools, etc.
+            **kwargs: Additional provider-specific arguments that subclasses can use.
 
         Returns:
             Provider-specific parameters for invoking the LLM.

src/pipecat/adapters/services/anthropic_adapter.py

@@ -6,12 +6,25 @@
 
 """Anthropic LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List, TypedDict
+import copy
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, TypedDict
+
+from anthropic import NOT_GIVEN, NotGiven
+from anthropic.types.message_param import MessageParam
+from anthropic.types.tool_union_param import ToolUnionParam
+from loguru import logger
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
-from pipecat.processors.aggregators.llm_context import LLMContext
+from pipecat.processors.aggregators.llm_context import (
+    LLMContext,
+    LLMContextMessage,
+    LLMSpecificMessage,
+    LLMStandardMessage,
+)
 
 
 class AnthropicLLMInvocationParams(TypedDict):
@@ -20,7 +33,9 @@ class AnthropicLLMInvocationParams(TypedDict):
     This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
     """
 
-    pass
+    system: str | NotGiven
+    messages: List[MessageParam]
+    tools: List[ToolUnionParam] | NotGiven
 
 
 class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
@@ -30,20 +45,33 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
     to the specific format required by Anthropic's Claude models for function calling.
     """
 
-    def get_llm_invocation_params(self, context: LLMContext) -> AnthropicLLMInvocationParams:
+    def get_llm_invocation_params(
+        self, context: LLMContext, enable_prompt_caching: bool
+    ) -> AnthropicLLMInvocationParams:
         """Get Anthropic-specific LLM invocation parameters from a universal LLM context.
 
         This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
 
         Args:
             context: The LLM context containing messages, tools, etc.
+            enable_prompt_caching: Whether prompt caching should be enabled.
 
         Returns:
             Dictionary of parameters for invoking Anthropic's LLM API.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+        messages = self._from_universal_context_messages(self._get_messages(context))
+        return {
+            "system": messages.system,
+            "messages": (
+                self._with_cache_control_markers(messages.messages)
+                if enable_prompt_caching
+                else messages.messages
+            ),
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            "tools": self.from_standard_tools(context.tools),
+        }
 
-    def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
+    def get_messages_for_logging(self, 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.
@@ -56,7 +84,241 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
         Returns:
             List of messages in a format ready for logging about Anthropic.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+        # Get messages in Anthropic's format
+        messages = self._from_universal_context_messages(self._get_messages(context)).messages
+
+        # Sanitize messages for logging
+        messages_for_logging = []
+        for message in messages:
+            msg = copy.deepcopy(message)
+            if "content" in msg:
+                if isinstance(msg["content"], list):
+                    for item in msg["content"]:
+                        if item["type"] == "image":
+                            item["source"]["data"] = "..."
+            messages_for_logging.append(msg)
+        return messages_for_logging
+
+    def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
+        return context.get_messages("anthropic")
+
+    @dataclass
+    class ConvertedMessages:
+        """Container for Anthropic-formatted messages converted from universal context."""
+
+        messages: List[MessageParam]
+        system: str | NotGiven
+
+    def _from_universal_context_messages(
+        self, universal_context_messages: List[LLMContextMessage]
+    ) -> ConvertedMessages:
+        system = NOT_GIVEN
+        messages = []
+
+        # first, map messages using self._from_universal_context_message(m)
+        try:
+            messages = [self._from_universal_context_message(m) for m in universal_context_messages]
+        except Exception as e:
+            logger.error(f"Error mapping messages: {e}")
+
+        # See if we should pull the system message out of our messages list.
+        if messages and messages[0]["role"] == "system":
+            if len(messages) == 1:
+                # If we have only have a system message in the list, all we can really do
+                # without introducing too much magic is change the role to "user".
+                messages[0]["role"] = "user"
+            else:
+                # If we have more than one message, we'll pull the system message out of the
+                # list.
+                system = messages[0]["content"]
+                messages.pop(0)
+
+        # Convert any subsequent "system"-role messages to "user"-role
+        # messages, as Anthropic doesn't support system input messages.
+        for message in messages:
+            if message["role"] == "system":
+                message["role"] = "user"
+
+        # Merge consecutive messages with the same role.
+        i = 0
+        while i < len(messages) - 1:
+            current_message = messages[i]
+            next_message = messages[i + 1]
+            if current_message["role"] == next_message["role"]:
+                # Convert content to list of dictionaries if it's a string
+                if isinstance(current_message["content"], str):
+                    current_message["content"] = [
+                        {"type": "text", "text": current_message["content"]}
+                    ]
+                if isinstance(next_message["content"], str):
+                    next_message["content"] = [{"type": "text", "text": next_message["content"]}]
+                # Concatenate the content
+                current_message["content"].extend(next_message["content"])
+                # Remove the next message from the list
+                messages.pop(i + 1)
+            else:
+                i += 1
+
+        # Avoid empty content in messages
+        for message in messages:
+            if isinstance(message["content"], str) and message["content"] == "":
+                message["content"] = "(empty)"
+            elif isinstance(message["content"], list) and len(message["content"]) == 0:
+                message["content"] = [{"type": "text", "text": "(empty)"}]
+
+        return self.ConvertedMessages(messages=messages, system=system)
+
+    def _from_universal_context_message(self, message: LLMContextMessage) -> MessageParam:
+        if isinstance(message, LLMSpecificMessage):
+            return copy.deepcopy(message.message)
+        return self._from_standard_message(message)
+
+    def _from_standard_message(self, message: LLMStandardMessage) -> MessageParam:
+        """Convert standard universal context message to Anthropic format.
+
+        Handles conversion of text content, tool calls, and tool results.
+        Empty text content is converted to "(empty)".
+
+        Args:
+            message: Message in standard universal context format.
+
+        Returns:
+            Message in Anthropic format.
+
+        Examples:
+            Input standard format::
+
+                {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "id": "123",
+                            "function": {"name": "search", "arguments": '{"q": "test"}'}
+                        }
+                    ]
+                }
+
+            Output Anthropic format::
+
+                {
+                    "role": "assistant",
+                    "content": [
+                        {
+                            "type": "tool_use",
+                            "id": "123",
+                            "name": "search",
+                            "input": {"q": "test"}
+                        }
+                    ]
+                }
+        """
+        message = copy.deepcopy(message)
+        if message["role"] == "tool":
+            return {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": message["tool_call_id"],
+                        "content": message["content"],
+                    },
+                ],
+            }
+        if message.get("tool_calls"):
+            tc = message["tool_calls"]
+            ret = {"role": "assistant", "content": []}
+            for tool_call in tc:
+                function = tool_call["function"]
+                arguments = json.loads(function["arguments"])
+                new_tool_use = {
+                    "type": "tool_use",
+                    "id": tool_call["id"],
+                    "name": function["name"],
+                    "input": arguments,
+                }
+                ret["content"].append(new_tool_use)
+            return ret
+        content = message.get("content")
+        if isinstance(content, str):
+            # fix empty text
+            if content == "":
+                content = "(empty)"
+        elif isinstance(content, list):
+            for item in content:
+                # fix empty text
+                if item["type"] == "text" and item["text"] == "":
+                    item["text"] = "(empty)"
+                # handle image_url -> image conversion
+                if item["type"] == "image_url":
+                    item["type"] = "image"
+                    item["source"] = {
+                        "type": "base64",
+                        "media_type": "image/jpeg",
+                        "data": item["image_url"]["url"].split(",")[1],
+                    }
+                    del item["image_url"]
+            # In the case where there's a single image in the list (like what
+            # would result from a UserImageRawFrame), ensure that the image
+            # comes before text, as recommended by Anthropic docs
+            # (https://docs.anthropic.com/en/docs/build-with-claude/vision#example-one-image)
+            image_indices = [i for i, item in enumerate(content) if item["type"] == "image"]
+            text_indices = [i for i, item in enumerate(content) if item["type"] == "text"]
+            if len(image_indices) == 1 and text_indices:
+                img_idx = image_indices[0]
+                first_txt_idx = text_indices[0]
+                if img_idx > first_txt_idx:
+                    # Move image before the first text
+                    image_item = content.pop(img_idx)
+                    content.insert(first_txt_idx, image_item)
+
+        return message
+
+    def _with_cache_control_markers(self, messages: List[MessageParam]) -> List[MessageParam]:
+        """Add cache control markers to messages for prompt caching.
+
+        Args:
+            messages: List of messages in Anthropic format.
+
+        Returns:
+            List of messages with cache control markers added.
+        """
+
+        def add_cache_control_marker(message: MessageParam):
+            if isinstance(message["content"], str):
+                message["content"] = [{"type": "text", "text": message["content"]}]
+            message["content"][-1]["cache_control"] = {"type": "ephemeral"}
+
+        try:
+            # Add cache control markers to the most recent two user messages.
+            # - The marker at the most recent user message tells Anthropic to
+            #   cache the prompt up to that point.
+            # - The marker at the second-most-recent user message tells Anthropic
+            #   to look up the cached prompt that goes up to that point (the
+            #   point that *was* the last user message the previous turn).
+            # If we only added the marker to the last user message, we'd only
+            # ever be adding to the cache, never looking up from it.
+            # Why user messages? We're assuming that we're primarily running
+            # inference as soon as user turns come in. In Anthropic, turns
+            # strictly alternate between user and assistant.
+
+            messages_with_markers = copy.deepcopy(messages)
+
+            # Find the most recent two user messages
+            user_message_indices = []
+            for i in range(len(messages_with_markers) - 1, -1, -1):
+                if messages_with_markers[i]["role"] == "user":
+                    user_message_indices.append(i)
+                    if len(user_message_indices) == 2:
+                        break
+
+            # Add cache control markers to the identified user messages
+            for index in user_message_indices:
+                add_cache_control_marker(messages_with_markers[index])
+
+            return messages_with_markers
+        except Exception as e:
+            logger.error(f"Error adding cache control marker: {e}")
+            return messages_with_markers
 
     @staticmethod
     def _to_anthropic_function_format(function: FunctionSchema) -> Dict[str, Any]:

src/pipecat/adapters/services/gemini_adapter.py

@@ -67,7 +67,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
         return {
             "system_instruction": messages.system_instruction,
             "messages": messages.messages,
-            # NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
             "tools": self.from_standard_tools(context.tools),
         }
 
@@ -192,14 +192,14 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
     def _from_standard_message(
         self, message: LLMStandardMessage, already_have_system_instruction: bool
     ) -> Content | str:
-        """Convert universal context message to Google Content object.
+        """Convert standard universal context message to Google Content object.
 
         Handles conversion of text, images, and function calls to Google's
         format.
         System instructions are returned as a plain string.
 
         Args:
-            message: Message in universal context format.
+            message: Message in standard universal context format.
             already_have_system_instruction: Whether we already have a system instruction
 
         Returns:
@@ -308,5 +308,4 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
                     audio_bytes = base64.b64decode(input_audio["data"])
                     parts.append(Part(inline_data=Blob(mime_type="audio/wav", data=audio_bytes)))
 
-        message = Content(role=role, parts=parts)
-        return message
+        return Content(role=role, parts=parts)

src/pipecat/pipeline/llm_switcher.py

@@ -30,25 +30,17 @@ class LLMSwitcher(ServiceSwitcher[StrategyType]):
         """Get the currently active LLM, if any."""
         return self.strategy.active_service
 
-    async def run_inference(
-        self, context: LLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context, using the currently active LLM.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
         """
         if self.active_llm:
-            return await self.active_llm.run_inference(
-                context=context, system_instruction=system_instruction
-            )
+            return await self.active_llm.run_inference(context=context)
         return None
 
     def register_function(

src/pipecat/services/anthropic/llm.py

@@ -24,7 +24,10 @@ from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
-from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
+from pipecat.adapters.services.anthropic_adapter import (
+    AnthropicLLMAdapter,
+    AnthropicLLMInvocationParams,
+)
 from pipecat.frames.frames import (
     ErrorFrame,
     Frame,
@@ -112,7 +115,12 @@ class AnthropicLLMService(LLMService):
         """Input parameters for Anthropic model inference.
 
         Parameters:
-            enable_prompt_caching_beta: Whether to enable beta prompt caching feature.
+            enable_prompt_caching: Whether to enable the prompt caching feature.
+            enable_prompt_caching_beta (deprecated): Whether to enable the beta prompt caching feature.
+
+                .. deprecated:: 0.0.84
+                    Use the `enable_prompt_caching` parameter instead.
+
             max_tokens: Maximum tokens to generate. Must be at least 1.
             temperature: Sampling temperature between 0.0 and 1.0.
             top_k: Top-k sampling parameter.
@@ -120,13 +128,26 @@ class AnthropicLLMService(LLMService):
             extra: Additional parameters to pass to the API.
         """
 
-        enable_prompt_caching_beta: Optional[bool] = False
+        enable_prompt_caching: Optional[bool] = None
+        enable_prompt_caching_beta: Optional[bool] = None
         max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
         temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
         top_k: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0)
         top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
         extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
+        def model_post_init(self, __context):
+            """Post-initialization to handle deprecated parameters."""
+            if self.enable_prompt_caching_beta is not None:
+                import warnings
+
+                warnings.simplefilter("always")
+                warnings.warn(
+                    "enable_prompt_caching_beta is deprecated. Use enable_prompt_caching instead.",
+                    DeprecationWarning,
+                    stacklevel=2,
+                )
+
     def __init__(
         self,
         *,
@@ -159,7 +180,15 @@ class AnthropicLLMService(LLMService):
         self._retry_on_timeout = retry_on_timeout
         self._settings = {
             "max_tokens": params.max_tokens,
-            "enable_prompt_caching_beta": params.enable_prompt_caching_beta or False,
+            "enable_prompt_caching": (
+                params.enable_prompt_caching
+                if params.enable_prompt_caching is not None
+                else (
+                    params.enable_prompt_caching_beta
+                    if params.enable_prompt_caching_beta is not None
+                    else False
+                )
+            ),
             "temperature": params.temperature,
             "top_k": params.top_k,
             "top_p": params.top_p,
@@ -199,34 +228,28 @@ class AnthropicLLMService(LLMService):
             response = await api_call(**params)
             return response
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
         """
         messages = []
-        system = []
+        system = NOT_GIVEN
         if isinstance(context, LLMContext):
-            # Future code will be something like this:
-            # adapter = self.get_llm_adapter()
-            # params: AnthropicLLMInvocationParams = adapter.get_llm_invocation_params(context)
-            # messages = params["messages"]
-            # system = params["system_instruction"]
-            raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+            adapter: AnthropicLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(
+                context, enable_prompt_caching=self._settings["enable_prompt_caching"]
+            )
+            messages = params["messages"]
+            system = params["system"]
         else:
             context = AnthropicLLMContext.upgrade_to_anthropic(context)
             messages = context.messages
-            system = getattr(context, "system", None) or system_instruction
+            system = getattr(context, "system", NOT_GIVEN)
 
         # LLM completion
         response = await self._client.messages.create(
@@ -239,15 +262,6 @@ class AnthropicLLMService(LLMService):
 
         return response.content[0].text
 
-    @property
-    def enable_prompt_caching_beta(self) -> bool:
-        """Check if prompt caching beta feature is enabled.
-
-        Returns:
-            True if prompt caching is enabled.
-        """
-        return self._enable_prompt_caching_beta
-
     def create_context_aggregator(
         self,
         context: OpenAILLMContext,
@@ -277,8 +291,31 @@ class AnthropicLLMService(LLMService):
         assistant = AnthropicAssistantContextAggregator(context, params=assistant_params)
         return AnthropicContextAggregatorPair(_user=user, _assistant=assistant)
 
+    def _get_llm_invocation_params(
+        self, context: OpenAILLMContext | LLMContext
+    ) -> AnthropicLLMInvocationParams:
+        # Universal LLMContext
+        if isinstance(context, LLMContext):
+            adapter: AnthropicLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(
+                context, enable_prompt_caching=self._settings["enable_prompt_caching"]
+            )
+            return params
+
+        # Anthropic-specific context
+        messages = (
+            context.get_messages_with_cache_control_markers()
+            if self._settings["enable_prompt_caching"]
+            else context.messages
+        )
+        return AnthropicLLMInvocationParams(
+            system=context.system,
+            messages=messages,
+            tools=context.tools,
+        )
+
     @traced_llm
-    async def _process_context(self, context: OpenAILLMContext):
+    async def _process_context(self, context: OpenAILLMContext | LLMContext):
         # Usage tracking. We track the usage reported by Anthropic in prompt_tokens and
         # completion_tokens. We also estimate the completion tokens from output text
         # and use that estimate if we are interrupted, because we almost certainly won't
@@ -294,24 +331,22 @@ class AnthropicLLMService(LLMService):
             await self.push_frame(LLMFullResponseStartFrame())
             await self.start_processing_metrics()
 
+            params_from_context = self._get_llm_invocation_params(context)
+
+            if isinstance(context, LLMContext):
+                adapter = self.get_llm_adapter()
+                context_type_for_logging = "universal"
+                messages_for_logging = adapter.get_messages_for_logging(context)
+            else:
+                context_type_for_logging = "LLM-specific"
+                messages_for_logging = context.get_messages_for_logging()
             logger.debug(
-                f"{self}: Generating chat [{context.system}] | {context.get_messages_for_logging()}"
+                f"{self}: Generating chat from {context_type_for_logging} context [{params_from_context['system']}] | {messages_for_logging}"
             )
 
-            messages = context.messages
-            if self._settings["enable_prompt_caching_beta"]:
-                messages = context.get_messages_with_cache_control_markers()
-
-            api_call = self._client.messages.create
-            if self._settings["enable_prompt_caching_beta"]:
-                api_call = self._client.beta.prompt_caching.messages.create
-
             await self.start_ttfb_metrics()
 
             params = {
-                "tools": context.tools or [],
-                "system": context.system,
-                "messages": messages,
                 "model": self.model_name,
                 "max_tokens": self._settings["max_tokens"],
                 "stream": True,
@@ -320,9 +355,12 @@ class AnthropicLLMService(LLMService):
                 "top_p": self._settings["top_p"],
             }
 
+            # Messages, system, tools
+            params.update(params_from_context)
+
             params.update(self._settings["extra"])
 
-            response = await self._create_message_stream(api_call, params)
+            response = await self._create_message_stream(self._client.messages.create, params)
 
             await self.stop_ttfb_metrics()
 
@@ -405,7 +443,10 @@ class AnthropicLLMService(LLMService):
                         prompt_tokens + cache_creation_input_tokens + cache_read_input_tokens
                     )
                     if total_input_tokens >= 1024:
-                        context.turns_above_cache_threshold += 1
+                        if hasattr(
+                            context, "turns_above_cache_threshold"
+                        ):  # LLMContext doesn't have this attribute
+                            context.turns_above_cache_threshold += 1
 
             await self.run_function_calls(function_calls)
 
@@ -451,7 +492,7 @@ class AnthropicLLMService(LLMService):
         if isinstance(frame, OpenAILLMContextFrame):
             context: "AnthropicLLMContext" = AnthropicLLMContext.upgrade_to_anthropic(frame.context)
         elif isinstance(frame, LLMContextFrame):
-            raise NotImplementedError("Universal LLMContext is not yet supported for Anthropic.")
+            context = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             context = AnthropicLLMContext.from_messages(frame.messages)
         elif isinstance(frame, VisionImageRawFrame):
@@ -464,7 +505,7 @@ class AnthropicLLMService(LLMService):
             await self._update_settings(frame.settings)
         elif isinstance(frame, LLMEnablePromptCachingFrame):
             logger.debug(f"Setting enable prompt caching to: [{frame.enable}]")
-            self._settings["enable_prompt_caching_beta"] = frame.enable
+            self._settings["enable_prompt_caching"] = frame.enable
         else:
             await self.push_frame(frame, direction)
 

src/pipecat/services/aws/llm.py

@@ -792,17 +792,11 @@ class AWSBedrockLLMService(LLMService):
         """
         return True
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -815,14 +809,14 @@ class AWSBedrockLLMService(LLMService):
                 # adapter = self.get_llm_adapter()
                 # params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
                 # messages = params["messages"]
-                # system = params["system_instruction"]
+                # system = params["system_instruction"] # [{"text": "system message"}]
                 raise NotImplementedError(
                     "Universal LLMContext is not yet supported for AWS Bedrock."
                 )
             else:
                 context = AWSBedrockLLMContext.upgrade_to_bedrock(context)
                 messages = context.messages
-                system = getattr(context, "system", None) or system_instruction
+                system = getattr(context, "system", None)  # [{"text": "system message"}]
 
             # Determine if we're using Claude or Nova based on model ID
             model_id = self.model_name
@@ -839,7 +833,7 @@ class AWSBedrockLLMService(LLMService):
             }
 
             if system:
-                request_params["system"] = [{"text": system}]
+                request_params["system"] = system
 
             async with self._aws_session.client(
                 service_name="bedrock-runtime", **self._aws_params

src/pipecat/services/google/llm.py

@@ -733,17 +733,11 @@ class GoogleLLMService(LLMService):
     def _create_client(self, api_key: str, http_options: Optional[HttpOptions] = None):
         self._client = genai.Client(api_key=api_key, http_options=http_options)
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context. If both are provided, the
-              one in the context takes precedence.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -758,7 +752,7 @@ class GoogleLLMService(LLMService):
         else:
             context = GoogleLLMContext.upgrade_to_google(context)
             messages = context.messages
-            system = getattr(context, "system_message", None) or system_instruction
+            system = getattr(context, "system_message", None)
 
         generation_config = GenerateContentConfig(system_instruction=system)
 
@@ -858,8 +852,7 @@ class GoogleLLMService(LLMService):
         self, context: OpenAILLMContext
     ) -> AsyncIterator[GenerateContentResponse]:
         logger.debug(
-            # f"{self}: Generating chat [{self._system_instruction}] | {context.get_messages_for_logging()}"
-            f"{self}: Generating chat from OpenAI context {context.get_messages_for_logging()}"
+            f"{self}: Generating chat from LLM-specific context [{context.system_message}] | {context.get_messages_for_logging()}"
         )
 
         params = GeminiLLMInvocationParams(
@@ -874,13 +867,12 @@ class GoogleLLMService(LLMService):
         self, context: LLMContext
     ) -> AsyncIterator[GenerateContentResponse]:
         adapter = self.get_llm_adapter()
-        logger.debug(
-            # f"{self}: Generating chat [{self._system_instruction}] | {context.get_messages_for_logging()}"
-            f"{self}: Generating chat from universal context {adapter.get_messages_for_logging(context)}"
-        )
-
         params: GeminiLLMInvocationParams = adapter.get_llm_invocation_params(context)
 
+        logger.debug(
+            f"{self}: Generating chat from universal context [{params['system_instruction']}] | {adapter.get_messages_for_logging(context)}"
+        )
+
         return await self._stream_content(params)
 
     @traced_llm

src/pipecat/services/llm_service.py

@@ -195,18 +195,13 @@ class LLMService(AIService):
         """
         return self._adapter
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Must be implemented by subclasses.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.

src/pipecat/services/openai/base_llm.py

@@ -245,16 +245,11 @@ class BaseOpenAILLMService(LLMService):
         params.update(self._settings["extra"])
         return params
 
-    async def run_inference(
-        self, context: LLMContext | OpenAILLMContext, system_instruction: Optional[str] = None
-    ) -> Optional[str]:
+    async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
         """Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.
 
         Args:
             context: The LLM context containing conversation history.
-            system_instruction: Optional system instruction to guide the LLM's
-              behavior. You could also (again, optionally) provide a system
-              instruction directly in the context.
 
         Returns:
             The LLM's response as a string, or None if no response is generated.
@@ -279,7 +274,7 @@ class BaseOpenAILLMService(LLMService):
         self, context: OpenAILLMContext
     ) -> AsyncStream[ChatCompletionChunk]:
         logger.debug(
-            f"{self}: Generating chat from OpenAI context {context.get_messages_for_logging()}"
+            f"{self}: Generating chat from LLM-specific context {context.get_messages_for_logging()}"
         )
 
         messages: List[ChatCompletionMessageParam] = context.get_messages()