examples: update Strands Agents with universal context and add evals

examples/foundational/07m-interruptible-aws-strands.py

@@ -9,14 +9,12 @@ from dotenv import load_dotenv
 from loguru import logger
 
 from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.frames.frames import LLMMessagesAppendFrame, 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_response import (
+from pipecat.processors.aggregators.llm_context import LLMContext
-    LLMAssistantContextAggregator,
+from pipecat.processors.aggregators.llm_response_universal import LLMContextAggregatorPair
-    LLMUserContextAggregator,
-)
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.processors.frameworks.strands_agents import StrandsAgentsProcessor
 from pipecat.runner.types import RunnerArguments
 from pipecat.runner.utils import create_transport
@@ -115,19 +113,18 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
         )
 
     # Setup context aggregators for message handling
-    context = OpenAILLMContext()
+    context = LLMContext()
-    tma_in = LLMUserContextAggregator(context=context)
+    context_aggregator = LLMContextAggregatorPair(context)
-    tma_out = LLMAssistantContextAggregator(context=context)
 
     pipeline = Pipeline(
         [
             transport.input(),  # Transport user input
             stt,  # Speech-to-text
-            tma_in,  # User context aggregator
+            context_aggregator.user(),  # User responses
             llm,  # Strands Agents processor
             tts,  # Text-to-speech
             transport.output(),  # Transport bot output
-            tma_out,  # Assistant context aggregator
+            context_aggregator.assistant(),  # Assistant spoken responses
         ]
     )
 
@@ -143,6 +140,20 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, client):
         logger.info(f"Client connected")
+        # Kick off the conversation.
+        await task.queue_frames(
+            [
+                LLMMessagesAppendFrame(
+                    messages=[
+                        {
+                            "role": "user",
+                            "content": f"Greet the user and introduce yourself.",
+                        }
+                    ],
+                    run_llm=True,
+                )
+            ]
+        )
 
     @transport.event_handler("on_client_disconnected")
     async def on_client_disconnected(transport, client):

scripts/evals/run-release-evals.py

@@ -83,6 +83,7 @@ TESTS_07 = [
     ("07k-interruptible-lmnt.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07l-interruptible-groq.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07m-interruptible-aws.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
+    ("07m-interruptible-aws-strands.py", PROMPT_WEATHER, EVAL_WEATHER, BOT_SPEAKS_FIRST),
     ("07n-interruptible-gemini.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07n-interruptible-google.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),
     ("07o-interruptible-assemblyai.py", PROMPT_SIMPLE_MATH, EVAL_SIMPLE_MATH, BOT_SPEAKS_FIRST),

src/pipecat/processors/frameworks/strands_agents.py

@@ -10,12 +10,12 @@ from loguru import logger
 
 from pipecat.frames.frames import (
     Frame,
+    LLMContextFrame,
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMTextFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
-from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContextFrame
 from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
 
 try:
@@ -71,9 +71,11 @@ class StrandsAgentsProcessor(FrameProcessor):
             direction: The direction of frame flow in the pipeline.
         """
         await super().process_frame(frame, direction)
-        if isinstance(frame, OpenAILLMContextFrame):
+        if isinstance(frame, LLMContextFrame):
-            text = frame.context.messages[-1]["content"]
+            messages = frame.context.get_messages()
-            await self._ainvoke(str(text).strip())
+            if messages:
+                last_message = messages[-1]
+                await self._ainvoke(str(last_message["content"]).strip())
         else:
             await self.push_frame(frame, direction)