Merge pull request #2619 from pipecat-ai/pk/aws-universal-context

Expand universal `LLMContext` support to AWS Bedrock

CHANGELOG.md

@@ -9,6 +9,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Added
 
+- Expanded support for universal `LLMContext` to the AWS Bedrock LLM service.
+  Using the universal `LLMContext` and associated `LLMContextAggregatorPair` is
+  a pre-requisite for using `LLMSwitcher` to switch between LLMs at runtime.
+
 - Added video streaming support to `LiveKitTransport`.
 
 - Added `OpenAIRealtimeLLMService` and `AzureRealtimeLLMService` which provide

examples/foundational/12d-describe-video-aws.py

@@ -13,6 +13,7 @@ from loguru import logger
 from pipecat.audio.vad.silero import SileroVADAnalyzer
 from pipecat.frames.frames import (
     Frame,
+    LLMContextFrame,
     TextFrame,
     TTSSpeakFrame,
     UserImageRawFrame,
@@ -21,10 +22,7 @@ from pipecat.frames.frames import (
 from pipecat.pipeline.pipeline import Pipeline
 from pipecat.pipeline.runner import PipelineRunner
 from pipecat.pipeline.task import PipelineParams, PipelineTask
-from pipecat.processors.aggregators.openai_llm_context import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    OpenAILLMContext,
-    OpenAILLMContextFrame,
-)
 from pipecat.processors.aggregators.user_response import UserResponseAggregator
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 from pipecat.runner.types import RunnerArguments
@@ -73,14 +71,14 @@ class UserImageProcessor(FrameProcessor):
         if isinstance(frame, UserImageRawFrame):
             if frame.request and frame.request.context:
                 # Note: AWS Bedrock does not yet support the universal LLMContext
-                context = OpenAILLMContext()
+                context = LLMContext()
                 context.add_image_frame_message(
                     image=frame.image,
                     text=frame.request.context,
                     size=frame.size,
                     format=frame.format,
                 )
-                frame = OpenAILLMContextFrame(context)
+                frame = LLMContextFrame(context)
                 await self.push_frame(frame)
         else:
             await self.push_frame(frame, direction)
@@ -121,6 +119,9 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     aws = AWSBedrockLLMService(
         aws_region="us-west-2",
         model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        # Note: usually, prefer providing latency="optimized" param.
+        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+        # which we need for image input.
         params=AWSBedrockLLMService.InputParams(temperature=0.8),
     )
 

examples/foundational/14aa-function-calling-aws-universal-context.py

@@ -0,0 +1,214 @@
+#
+# 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.aws.llm import AWSBedrockLLMService
+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 = AWSBedrockLLMService(
+        aws_region="us-west-2",
+        model="us.anthropic.claude-3-7-sonnet-20250219-v1:0",
+        # Note: usually, prefer providing latency="optimized" param.
+        # Here we can't because AWS Bedrock doesn't support it for Claude 3.7,
+        # which we need for image input.
+        params=AWSBedrockLLMService.InputParams(temperature=0.8),
+    )
+    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/services/anthropic_adapter.py

@@ -9,7 +9,7 @@
 import copy
 import json
 from dataclasses import dataclass
-from typing import Any, Dict, List, Optional, TypedDict
+from typing import Any, Dict, List, TypedDict
 
 from anthropic import NOT_GIVEN, NotGiven
 from anthropic.types.message_param import MessageParam
@@ -28,10 +28,7 @@ from pipecat.processors.aggregators.llm_context import (
 
 
 class AnthropicLLMInvocationParams(TypedDict):
-    """Context-based parameters for invoking Anthropic's LLM API.
+    """Context-based parameters for invoking Anthropic's LLM API."""
-
-    This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
-    """
 
     system: str | NotGiven
     messages: List[MessageParam]
@@ -50,8 +47,6 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
     ) -> 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.
@@ -76,8 +71,6 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
 
         Removes or truncates sensitive data like image content for safe logging.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Anthropic.
-
         Args:
             context: The LLM context containing messages.
 

src/pipecat/adapters/services/bedrock_adapter.py

@@ -6,21 +6,33 @@
 
 """AWS Bedrock LLM adapter for Pipecat."""
 
-from typing import Any, Dict, List, TypedDict
+import base64
+import copy
+import json
+from dataclasses import dataclass
+from typing import Any, Dict, List, Literal, Optional, TypedDict
+
+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,
+    LLMContextToolChoice,
+    LLMSpecificMessage,
+    LLMStandardMessage,
+)
 
 
 class AWSBedrockLLMInvocationParams(TypedDict):
-    """Context-based parameters for invoking AWS Bedrock's LLM API.
+    """Context-based parameters for invoking AWS Bedrock's LLM API."""
 
-    This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
+    system: Optional[List[dict[str, Any]]]  # [{"text": "system message"}]
-    """
+    messages: List[dict[str, Any]]
-
+    tools: List[dict[str, Any]]
-    pass
+    tool_choice: LLMContextToolChoice
 
 
 class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
@@ -33,30 +45,239 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
     def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
         """Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
-
         Args:
             context: The LLM context containing messages, tools, etc.
 
         Returns:
             Dictionary of parameters for invoking AWS Bedrock's LLM API.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+        messages = self._from_universal_context_messages(self._get_messages(context))
+        return {
+            "system": messages.system,
+            "messages": messages.messages,
+            # NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
+            "tools": self.from_standard_tools(context.tools) or [],
+            # To avoid refactoring in AWSBedrockLLMService, we just pass through tool_choice.
+            # Eventually (when we don't have to maintain the non-LLMContext code path) we should do
+            # the conversion to Bedrock's expected format here rather than in AWSBedrockLLMService.
+            "tool_choice": context.tool_choice,
+        }
 
     def get_messages_for_logging(self, context) -> List[Dict[str, Any]]:
         """Get messages from a universal LLM context in a format ready for logging about AWS Bedrock.
 
         Removes or truncates sensitive data like image content for safe logging.
 
-        This is a placeholder until support for universal LLMContext machinery is added for Bedrock.
-
         Args:
             context: The LLM context containing messages.
 
         Returns:
             List of messages in a format ready for logging about AWS Bedrock.
         """
-        raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+        # 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.get("image"):
+                            item["image"]["source"]["bytes"] = "..."
+            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[dict[str, Any]]
+        system: Optional[str]
+
+    def _from_universal_context_messages(
+        self, universal_context_messages: List[LLMContextMessage]
+    ) -> ConvertedMessages:
+        system = None
+        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":
+            system = messages[0]["content"]
+            messages.pop(0)
+
+        # Convert any subsequent "system"-role messages to "user"-role
+        # messages, as AWS Bedrock 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) -> dict[str, Any]:
+        if isinstance(message, LLMSpecificMessage):
+            return copy.deepcopy(message.message)
+        return self._from_standard_message(message)
+
+    def _from_standard_message(self, message: LLMStandardMessage) -> dict[str, Any]:
+        """Convert standard format message to AWS Bedrock format.
+
+        Handles conversion of text content, tool calls, and tool results.
+        Empty text content is converted to "(empty)".
+
+        Args:
+            message: Message in standard format.
+
+        Returns:
+            Message in AWS Bedrock format.
+
+        Examples:
+            Standard format input::
+
+                {
+                    "role": "assistant",
+                    "tool_calls": [
+                        {
+                            "id": "123",
+                            "function": {"name": "search", "arguments": '{"q": "test"}'}
+                        }
+                    ]
+                }
+
+            AWS Bedrock format output::
+
+                {
+                    "role": "assistant",
+                    "content": [
+                        {
+                            "toolUse": {
+                                "toolUseId": "123",
+                                "name": "search",
+                                "input": {"q": "test"}
+                            }
+                        }
+                    ]
+                }
+        """
+        message = copy.deepcopy(message)
+        if message["role"] == "tool":
+            # Try to parse the content as JSON if it looks like JSON
+            try:
+                if message["content"].strip().startswith("{") and message[
+                    "content"
+                ].strip().endswith("}"):
+                    content_json = json.loads(message["content"])
+                    tool_result_content = [{"json": content_json}]
+                else:
+                    tool_result_content = [{"text": message["content"]}]
+            except:
+                tool_result_content = [{"text": message["content"]}]
+
+            return {
+                "role": "user",
+                "content": [
+                    {
+                        "toolResult": {
+                            "toolUseId": message["tool_call_id"],
+                            "content": tool_result_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 = {
+                    "toolUse": {
+                        "toolUseId": tool_call["id"],
+                        "name": function["name"],
+                        "input": arguments,
+                    }
+                }
+                ret["content"].append(new_tool_use)
+            return ret
+
+        # Handle text content
+        content = message.get("content")
+        if isinstance(content, str):
+            if content == "":
+                return {"role": message["role"], "content": [{"text": "(empty)"}]}
+            else:
+                return {"role": message["role"], "content": [{"text": content}]}
+        elif isinstance(content, list):
+            new_content = []
+            for item in content:
+                # fix empty text
+                if item.get("type", "") == "text":
+                    text_content = item["text"] if item["text"] != "" else "(empty)"
+                    new_content.append({"text": text_content})
+                # handle image_url -> image conversion
+                if item["type"] == "image_url":
+                    new_item = {
+                        "image": {
+                            "format": "jpeg",
+                            "source": {
+                                "bytes": base64.b64decode(item["image_url"]["url"].split(",")[1])
+                            },
+                        }
+                    }
+                    new_content.append(new_item)
+            # 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
+            image_indices = [i for i, item in enumerate(new_content) if "image" in item]
+            text_indices = [i for i, item in enumerate(new_content) if "text" in item]
+            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 = new_content.pop(img_idx)
+                new_content.insert(first_txt_idx, image_item)
+            return {"role": message["role"], "content": new_content}
+
+        return message
 
     @staticmethod
     def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:

src/pipecat/services/aws/llm.py

@@ -25,7 +25,10 @@ from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
-from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
+from pipecat.adapters.services.bedrock_adapter import (
+    AWSBedrockLLMAdapter,
+    AWSBedrockLLMInvocationParams,
+)
 from pipecat.frames.frames import (
     Frame,
     FunctionCallCancelFrame,
@@ -812,14 +815,10 @@ class AWSBedrockLLMService(LLMService):
             messages = []
             system = []
             if isinstance(context, LLMContext):
-                # Future code will be something like this:
+                adapter: AWSBedrockLLMAdapter = self.get_llm_adapter()
-                # adapter = self.get_llm_adapter()
+                params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
-                # params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
+                messages = params["messages"]
-                # messages = params["messages"]
+                system = params["system"]  # [{"text": "system message"}]
-                # 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
@@ -940,8 +939,25 @@ class AWSBedrockLLMService(LLMService):
             }
         }
 
+    def _get_llm_invocation_params(
+        self, context: OpenAILLMContext | LLMContext
+    ) -> AWSBedrockLLMInvocationParams:
+        # Universal LLMContext
+        if isinstance(context, LLMContext):
+            adapter: AWSBedrockLLMAdapter = self.get_llm_adapter()
+            params = adapter.get_llm_invocation_params(context)
+            return params
+
+        # AWS Bedrock-specific context
+        return AWSBedrockLLMInvocationParams(
+            system=getattr(context, "system", None),
+            messages=context.messages,
+            tools=context.tools or [],
+            tool_choice=context.tool_choice,
+        )
+
     @traced_llm
-    async def _process_context(self, context: AWSBedrockLLMContext):
+    async def _process_context(self, context: AWSBedrockLLMContext | LLMContext):
         # Usage tracking
         prompt_tokens = 0
         completion_tokens = 0
@@ -958,6 +974,12 @@ class AWSBedrockLLMService(LLMService):
 
             await self.start_ttfb_metrics()
 
+            params_from_context = self._get_llm_invocation_params(context)
+            messages = params_from_context["messages"]
+            system = params_from_context["system"]
+            tools = params_from_context["tools"]
+            tool_choice = params_from_context["tool_choice"]
+
             # Set up inference config
             inference_config = {
                 "maxTokens": self._settings["max_tokens"],
@@ -968,19 +990,18 @@ class AWSBedrockLLMService(LLMService):
             # Prepare request parameters
             request_params = {
                 "modelId": self.model_name,
-                "messages": context.messages,
+                "messages": messages,
                 "inferenceConfig": inference_config,
                 "additionalModelRequestFields": self._settings["additional_model_request_fields"],
             }
 
             # Add system message
-            system = getattr(context, "system", None)
             if system:
                 request_params["system"] = system
 
             # Check if messages contain tool use or tool result content blocks
             has_tool_content = False
-            for message in context.messages:
+            for message in messages:
                 if isinstance(message.get("content"), list):
                     for content_item in message["content"]:
                         if "toolUse" in content_item or "toolResult" in content_item:
@@ -990,7 +1011,6 @@ class AWSBedrockLLMService(LLMService):
                     break
 
             # Handle tools: use current tools, or no-op if tool content exists but no current tools
-            tools = context.tools or []
             if has_tool_content and not tools:
                 tools = [self._create_no_op_tool()]
                 using_noop_tool = True
@@ -999,17 +1019,15 @@ class AWSBedrockLLMService(LLMService):
                 tool_config = {"tools": tools}
 
                 # Only add tool_choice if we have real tools (not just no-op)
-                if not using_noop_tool and context.tool_choice:
+                if not using_noop_tool and tool_choice:
-                    if context.tool_choice == "auto":
+                    if tool_choice == "auto":
                         tool_config["toolChoice"] = {"auto": {}}
-                    elif context.tool_choice == "none":
+                    elif tool_choice == "none":
                         # Skip adding toolChoice for "none"
                         pass
-                    elif (
+                    elif isinstance(tool_choice, dict) and "function" in tool_choice:
-                        isinstance(context.tool_choice, dict) and "function" in context.tool_choice
-                    ):
                         tool_config["toolChoice"] = {
-                            "tool": {"name": context.tool_choice["function"]["name"]}
+                            "tool": {"name": tool_choice["function"]["name"]}
                         }
 
                 request_params["toolConfig"] = tool_config
@@ -1019,9 +1037,16 @@ class AWSBedrockLLMService(LLMService):
                 request_params["performanceConfig"] = {"latency": self._settings["latency"]}
 
             # Log request params with messages redacted for logging
-            log_params = dict(request_params)
+            if isinstance(context, LLMContext):
-            log_params["messages"] = context.get_messages_for_logging()
+                adapter = self.get_llm_adapter()
-            logger.debug(f"Calling AWS Bedrock model with: {log_params}")
+                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 from {context_type_for_logging} context [{system}] | {messages_for_logging}"
+            )
 
             async with self._aws_session.client(
                 service_name="bedrock-runtime", **self._aws_params
@@ -1129,7 +1154,7 @@ class AWSBedrockLLMService(LLMService):
         if isinstance(frame, OpenAILLMContextFrame):
             context = AWSBedrockLLMContext.upgrade_to_bedrock(frame.context)
         if isinstance(frame, LLMContextFrame):
-            raise NotImplementedError("Universal LLMContext is not yet supported for AWS Bedrock.")
+            context = frame.context
         elif isinstance(frame, LLMMessagesFrame):
             context = AWSBedrockLLMContext.from_messages(frame.messages)
         elif isinstance(frame, LLMUpdateSettingsFrame):