Add supports_universal_context for OpenAILLMService subclasses so that we can gradually roll out support for universal LLMContext in a controlled manner.

Also update `get_chat_completions()` implementations with the new argument type.

src/pipecat/services/azure/llm.py

@@ -60,3 +60,12 @@ class AzureLLMService(OpenAILLMService):
             azure_endpoint=self._endpoint,
             api_version=self._api_version,
         )
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as Azure service does yet not support universal LLMContext.
+        """
+        return False

src/pipecat/services/cerebras/llm.py

@@ -9,9 +9,8 @@
 from typing import List
 
 from loguru import logger
-from openai.types.chat import ChatCompletionMessageParam
 
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.services.openai.llm import OpenAILLMService
 
 
@@ -54,25 +53,40 @@ class CerebrasLLMService(OpenAILLMService):
         logger.debug(f"Creating Cerebras client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for Cerebras chat completion request.
 
         Cerebras supports a subset of OpenAI parameters, focusing on core
         completion settings without advanced features like frequency/presence penalties.
+
+        Args:
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
+
+        Returns:
+            Dictionary of parameters for the chat completion request.
         """
         params = {
             "model": self.model_name,
             "stream": True,
-            "messages": messages,
-            "tools": context.tools,
-            "tool_choice": context.tool_choice,
             "seed": self._settings["seed"],
             "temperature": self._settings["temperature"],
             "top_p": self._settings["top_p"],
             "max_completion_tokens": self._settings["max_completion_tokens"],
         }
 
+        # Messages, tools, tool_choice
+        params.update(params_from_context)
+
         params.update(self._settings["extra"])
         return params
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as Cerebras service does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/deepseek/llm.py

@@ -9,9 +9,8 @@
 from typing import List
 
 from loguru import logger
-from openai.types.chat import ChatCompletionMessageParam
 
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.services.openai.llm import OpenAILLMService
 
 
@@ -54,19 +53,22 @@ class DeepSeekLLMService(OpenAILLMService):
         logger.debug(f"Creating DeepSeek client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    def _build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def _build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for DeepSeek chat completion request.
 
         DeepSeek doesn't support some OpenAI parameters like seed and max_completion_tokens.
+
+        Args:
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
+
+        Returns:
+            Dictionary of parameters for the chat completion request.
         """
         params = {
             "model": self.model_name,
             "stream": True,
-            "messages": messages,
-            "tools": context.tools,
-            "tool_choice": context.tool_choice,
             "stream_options": {"include_usage": True},
             "frequency_penalty": self._settings["frequency_penalty"],
             "presence_penalty": self._settings["presence_penalty"],
@@ -75,5 +77,17 @@ class DeepSeekLLMService(OpenAILLMService):
             "max_tokens": self._settings["max_tokens"],
         }
 
+        # Messages, tools, tool_choice
+        params.update(params_from_context)
+
         params.update(self._settings["extra"])
         return params
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as DeepSeekLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/fireworks/llm.py

@@ -9,9 +9,8 @@
 from typing import List
 
 from loguru import logger
-from openai.types.chat import ChatCompletionMessageParam
 
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.services.openai.llm import OpenAILLMService
 
 
@@ -54,20 +53,23 @@ class FireworksLLMService(OpenAILLMService):
         logger.debug(f"Creating Fireworks client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for Fireworks chat completion request.
 
         Fireworks doesn't support some OpenAI parameters like seed, max_completion_tokens,
         and stream_options.
+
+        Args:
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
+
+        Returns:
+            Dictionary of parameters for the chat completion request.
         """
         params = {
             "model": self.model_name,
             "stream": True,
-            "messages": messages,
-            "tools": context.tools,
-            "tool_choice": context.tool_choice,
             "frequency_penalty": self._settings["frequency_penalty"],
             "presence_penalty": self._settings["presence_penalty"],
             "temperature": self._settings["temperature"],
@@ -75,5 +77,17 @@ class FireworksLLMService(OpenAILLMService):
             "max_tokens": self._settings["max_tokens"],
         }
 
+        # Messages, tools, tool_choice
+        params.update(params_from_context)
+
         params.update(self._settings["extra"])
         return params
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as FireworksLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/google/llm_openai.py

@@ -61,6 +61,15 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService):
         """
         super().__init__(api_key=api_key, base_url=base_url, model=model, **kwargs)
 
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as GoogleLLMOpenAIBetaService does not yet support universal LLMContext.
+        """
+        return False
+
     async def _process_context(self, context: OpenAILLMContext):
         functions_list = []
         arguments_list = []
@@ -72,7 +81,7 @@ class GoogleLLMOpenAIBetaService(OpenAILLMService):
 
         await self.start_ttfb_metrics()
 
-        chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(
+        chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions_specific_context(
             context
         )
 

src/pipecat/services/google/llm_vertex.py

@@ -139,3 +139,12 @@ class GoogleVertexLLMService(OpenAILLMService):
         creds.refresh(Request())  # Ensure token is up-to-date, lifetime is 1 hour.
 
         return creds.token
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as GoogleVertexLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/grok/llm.py

@@ -190,3 +190,12 @@ class GrokLLMService(OpenAILLMService):
         user = OpenAIUserContextAggregator(context, params=user_params)
         assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
         return GrokContextAggregatorPair(_user=user, _assistant=assistant)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as GrokLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/groq/llm.py

@@ -49,3 +49,12 @@ class GroqLLMService(OpenAILLMService):
         """
         logger.debug(f"Creating Groq client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as GroqLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/nim/llm.py

@@ -47,6 +47,15 @@ class NimLLMService(OpenAILLMService):
         self._has_reported_prompt_tokens = False
         self._is_processing = False
 
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as NimLLMService does not yet support universal LLMContext.
+        """
+        return False
+
     async def _process_context(self, context: OpenAILLMContext):
         """Process a context through the LLM and accumulate token usage metrics.
 

src/pipecat/services/ollama/llm.py

@@ -43,3 +43,12 @@ class OLLamaLLMService(OpenAILLMService):
         """
         logger.debug(f"Creating Ollama client with api {base_url}")
         return super().create_client(base_url=base_url, **kwargs)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as OLLamaLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/openai/base_llm.py

@@ -190,12 +190,13 @@ class BaseOpenAILLMService(LLMService):
 
         Args:
             params_from_context: Parameters, derived from the LLM context, to
-                use for the chat completion. Contains messages, tools, and tool choice.
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
 
         Returns:
             Async stream of chat completion chunks.
         """
-        params = self.build_chat_completion_params(context, messages)
+        params = self.build_chat_completion_params(params_from_context)
 
         if self._retry_on_timeout:
             try:
@@ -213,7 +214,7 @@ class BaseOpenAILLMService(LLMService):
             return chunks
 
     def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
+        self, params_from_context: OpenAILLMInvocationParams
     ) -> dict:
         """Build parameters for chat completion request.
 
@@ -245,7 +246,7 @@ class BaseOpenAILLMService(LLMService):
         params.update(self._settings["extra"])
         return params
 
-    async def _stream_chat_completions(
+    async def _stream_chat_completions_specific_context(
         self, context: OpenAILLMContext
     ) -> AsyncStream[ChatCompletionChunk]:
         logger.debug(
@@ -303,7 +304,7 @@ class BaseOpenAILLMService(LLMService):
 
         # Generate chat completions using either OpenAILLMContext or universal LLMContext
         chunk_stream = await (
-            self._stream_chat_completions(context)
+            self._stream_chat_completions_specific_context(context)
             if isinstance(context, OpenAILLMContext)
             else self._stream_chat_completions_universal_context(context)
         )
@@ -389,6 +390,18 @@ class BaseOpenAILLMService(LLMService):
 
             await self.run_function_calls(function_calls)
 
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            Whether service supports universal LLMContext.
+        """
+        # Return True in subclasses that support universal LLMContext
+        # This property lets us gradually roll out support for universal
+        # LLMContext to OpenAI-like services in a controlled manner.
+        return False
+
     async def process_frame(self, frame: Frame, direction: FrameDirection):
         """Process frames for LLM completion requests.
 
@@ -408,7 +421,12 @@ class BaseOpenAILLMService(LLMService):
             context = frame.context
         elif isinstance(frame, LLMContextFrame):
             # Handle universal (LLM-agnostic) LLM context frames
-            context = frame.context
+            if self.supports_universal_context:
+                context = frame.context
+            else:
+                raise NotImplementedError(
+                    f"Universal LLMContext is not yet supported for {self.__class__.__name__}."
+                )
         elif isinstance(frame, LLMMessagesFrame):
             # NOTE: LLMMessagesFrame is deprecated, so we don't support the newer universal
             # LLMContext with it

src/pipecat/services/openai/llm.py

@@ -107,6 +107,15 @@ class OpenAILLMService(BaseOpenAILLMService):
         assistant = OpenAIAssistantContextAggregator(context, params=assistant_params)
         return OpenAIContextAggregatorPair(_user=user, _assistant=assistant)
 
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            True, as OpenAI service supports universal LLMContext.
+        """
+        return True
+
 
 class OpenAIUserContextAggregator(LLMUserContextAggregator):
     """OpenAI-specific user context aggregator.

src/pipecat/services/openpipe/llm.py

@@ -13,9 +13,8 @@ enabling integration with OpenPipe's fine-tuning and monitoring capabilities.
 from typing import Dict, List, Optional
 
 from loguru import logger
-from openai.types.chat import ChatCompletionMessageParam
 
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.services.openai.llm import OpenAILLMService
 
 try:
@@ -86,22 +85,21 @@ class OpenPipeLLMService(OpenAILLMService):
         )
         return client
 
-    def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for OpenPipe chat completion request.
 
         Adds OpenPipe-specific logging and tagging parameters.
 
         Args:
-            context: The LLM context containing tools and configuration.
-            messages: List of chat completion messages to send.
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
 
         Returns:
             Dictionary of parameters for the chat completion request.
         """
         # Start with base parameters
-        params = super().build_chat_completion_params(context, messages)
+        params = super().build_chat_completion_params(params_from_context)
 
         # Add OpenPipe-specific parameters
         params["openpipe"] = {
@@ -110,3 +108,12 @@ class OpenPipeLLMService(OpenAILLMService):
         }
 
         return params
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as OpenPipeLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/openrouter/llm.py

@@ -61,3 +61,12 @@ class OpenRouterLLMService(OpenAILLMService):
         """
         logger.debug(f"Creating OpenRouter client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as OpenRouterLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/perplexity/llm.py

@@ -11,11 +11,9 @@ an OpenAI-compatible interface. It handles Perplexity's unique token usage
 reporting patterns while maintaining compatibility with the Pipecat framework.
 """
 
-from typing import List
-
 from openai import NOT_GIVEN
-from openai.types.chat import ChatCompletionMessageParam
 
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.metrics.metrics import LLMTokenUsage
 from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.openai.llm import OpenAILLMService
@@ -53,17 +51,23 @@ class PerplexityLLMService(OpenAILLMService):
         self._has_reported_prompt_tokens = False
         self._is_processing = False
 
-    def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for Perplexity chat completion request.
 
         Perplexity uses a subset of OpenAI parameters and doesn't support tools.
+
+        Args:
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
+
+        Returns:
+            Dictionary of parameters for the chat completion request.
         """
         params = {
             "model": self.model_name,
             "stream": True,
-            "messages": messages,
+            "messages": params_from_context["messages"],
         }
 
         # Add OpenAI-compatible parameters if they're set
@@ -80,6 +84,15 @@ class PerplexityLLMService(OpenAILLMService):
 
         return params
 
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as PerplexityLLMService does not yet support universal LLMContext.
+        """
+        return False
+
     async def _process_context(self, context: OpenAILLMContext):
         """Process a context through the LLM and accumulate token usage metrics.
 

src/pipecat/services/qwen/llm.py

@@ -50,3 +50,12 @@ class QwenLLMService(OpenAILLMService):
         """
         logger.debug(f"Creating Qwen client with base URL: {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as QwenLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/sambanova/llm.py

@@ -7,12 +7,13 @@
 """SambaNova LLM service implementation using OpenAI-compatible interface."""
 
 import json
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, Optional
 
 from loguru import logger
 from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
+from openai.types.chat import ChatCompletionChunk
 
+from pipecat.adapters.services.open_ai_adapter import OpenAILLMInvocationParams
 from pipecat.frames.frames import (
     LLMTextFrame,
 )
@@ -67,17 +68,16 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
         logger.debug(f"Creating SambaNova client with API {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
 
-    def build_chat_completion_params(
-        self, context: OpenAILLMContext, messages: List[ChatCompletionMessageParam]
-    ) -> dict:
+    def build_chat_completion_params(self, params_from_context: OpenAILLMInvocationParams) -> dict:
         """Build parameters for SambaNova chat completion request.
 
         SambaNova doesn't support some OpenAI parameters like frequency_penalty,
         presence_penalty, and seed.
 
         Args:
-            context: The LLM context containing tools and configuration.
-            messages: List of chat completion messages to send.
+            params_from_context: Parameters, derived from the LLM context, to
+                use for the chat completion. Contains messages, tools, and tool
+                choice.
 
         Returns:
             Dictionary of parameters for the chat completion request.
@@ -85,9 +85,6 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
         params = {
             "model": self.model_name,
             "stream": True,
-            "messages": messages,
-            "tools": context.tools,
-            "tool_choice": context.tool_choice,
             "stream_options": {"include_usage": True},
             "temperature": self._settings["temperature"],
             "top_p": self._settings["top_p"],
@@ -95,6 +92,9 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
             "max_completion_tokens": self._settings["max_completion_tokens"],
         }
 
+        # Messages, tools, tool_choice
+        params.update(params_from_context)
+
         params.update(self._settings["extra"])
         return params
 
@@ -122,7 +122,7 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
 
         await self.start_ttfb_metrics()
 
-        chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions(
+        chunk_stream: AsyncStream[ChatCompletionChunk] = await self._stream_chat_completions_specific_context(
             context
         )
 
@@ -210,3 +210,12 @@ class SambaNovaLLMService(OpenAILLMService):  # type: ignore
                 )
 
             await self.run_function_calls(function_calls)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as SambaNovaLLMService does not yet support universal LLMContext.
+        """
+        return False

src/pipecat/services/together/llm.py

@@ -49,3 +49,12 @@ class TogetherLLMService(OpenAILLMService):
         """
         logger.debug(f"Creating Together.ai client with api {base_url}")
         return super().create_client(api_key, base_url, **kwargs)
+
+    @property
+    def supports_universal_context(self) -> bool:
+        """Check if this service supports universal LLMContext.
+
+        Returns:
+            False, as TogetherLLMService does not yet support universal LLMContext.
+        """
+        return False