wip: First stab at langchain support

Is this a service or processor?
How to deal with conversation history? LC has sophisticated means of this, but might get in the way of `LLMResponseAggregator`

tests/test_langchain.py

@@ -0,0 +1,57 @@
+import pytest
+from langchain.prompts import ChatPromptTemplate
+from langchain_core.language_models import FakeStreamingListLLM
+
+from pipecat.frames.frames import (StopTaskFrame, TranscriptionFrame,
+                                   UserStartedSpeakingFrame,
+                                   UserStoppedSpeakingFrame)
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineTask
+from pipecat.processors.aggregators.llm_response import (
+    LLMAssistantResponseAggregator, LLMUserResponseAggregator)
+from pipecat.processors.logger import FrameLogger
+from pipecat.services.langchain import LangchainProcessor
+
+
+@pytest.fixture
+def fake_llm():
+    responses = ["Hello dear human"]
+    return FakeStreamingListLLM(responses=responses)
+
+
+@pytest.mark.asyncio
+async def test_langchain(fake_llm: FakeStreamingListLLM):
+    fl_in = FrameLogger("Inner")
+    fl_out = FrameLogger("Outer")
+
+    messages = [("system", "Say hello to {name}"), ("human", "{input}")]
+    prompt = ChatPromptTemplate.from_messages(messages).partial(name="Thomas")
+    chain = prompt | fake_llm
+    proc = LangchainProcessor(chain=chain)
+
+    tma_in = LLMUserResponseAggregator(messages)
+    tma_out = LLMAssistantResponseAggregator(messages)
+
+    pipeline = Pipeline(
+        [
+            fl_in,
+            tma_in,
+            proc,
+            tma_out,
+            fl_out,
+        ]
+    )
+
+    task = PipelineTask(pipeline)
+    await task.queue_frames(
+        [
+            UserStartedSpeakingFrame(),
+            TranscriptionFrame(text="Hi World", user_id="user", timestamp="now"),
+            UserStoppedSpeakingFrame(),
+            StopTaskFrame(),
+        ]
+    )
+
+    runner = PipelineRunner()
+    await runner.run(task)