In this tutorial, we explore how we can build a fully functional conversational AI agent from scratch using the Pipecat framework. We walk through setting up a Pipeline that links together custom FrameProcessor classes, one for handling user input and generating responses with a HuggingFace model, and another for formatting and displaying the conversation flow. We also implement a ConversationInputGenerator to simulate dialogue, and use the PipelineRunner and PipelineTask to execute the data flow asynchronously. This structure showcases how Pipecat handles frame-based processing, enabling modular integration of components like language models, display logic, and future add-ons such as speech modules. Check out the FULL CODES here.
!pip install -q pipecat-ai transformers torch accelerate numpy
import asyncio
import logging
from typing import AsyncGenerator
import numpy as np
print(" Checking available Pipecat frames...")
try:
from pipecat.frames.frames import (
Frame,
TextFrame,
)
print("
Basic frames imported successfully")
except ImportError as e:
print(f"
Import error: {e}")
from pipecat.frames.frames import Frame, TextFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineTask
from pipecat.processors.frame_processor import FrameDirection, FrameProcessor
from transformers import pipeline as hf_pipeline
import torchWe begin by installing the required libraries, including Pipecat, Transformers, and PyTorch, and then set up our imports. We bring in Pipecat’s core components, such as Pipeline, PipelineRunner, and FrameProcessor, along with HuggingFace’s pipeline API for text generation. This prepares our environment to build and run the conversational AI agent seamlessly. Check out the FULL CODES here.
class SimpleChatProcessor(FrameProcessor):
"""Simple conversational AI processor using HuggingFace"""
def __init__(self):
super().__init__()
print("
Loading HuggingFace text generation model...")
self.chatbot = hf_pipeline(
"text-generation",
model="microsoft/DialoGPT-small",
pad_token_id=50256,
do_sample=True,
temperature=0.8,
max_length=100
)
self.conversation_history = ""
print(" Chat model loaded successfully!")
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
user_text = getattr(frame, "text", "").strip()
if user_text and not user_text.startswith("AI:"):
print(f"
USER: {user_text}")
try:
if self.conversation_history:
input_text = f"{self.conversation_history} User: {user_text} Bot:"
else:
input_text = f"User: {user_text} Bot:"
response = self.chatbot(
input_text,
max_new_tokens=50,
num_return_sequences=1,
temperature=0.7,
do_sample=True,
pad_token_id=self.chatbot.tokenizer.eos_token_id
)
generated_text = response[0]["generated_text"]
if "Bot:" in generated_text:
ai_response = generated_text.split("Bot:")[-1].strip()
ai_response = ai_response.split("User:")[0].strip()
if not ai_response:
ai_response = "That's interesting! Tell me more."
else:
ai_response = "I'd love to hear more about that!"
self.conversation_history = f"{input_text} {ai_response}"
await self.push_frame(TextFrame(text=f"AI: {ai_response}"), direction)
except Exception as e:
print(f" Chat error: {e}")
await self.push_frame(
TextFrame(text="AI: I'm having trouble processing that. Could you try rephrasing?"),
direction
)
else:
await self.push_frame(frame, direction)We implement SimpleChatProcessor, which loads the HuggingFace DialoGPT-small model for text generation and maintains conversation history for context. As each TextFrame arrives, we process the user’s input, generate a model response, clean it up, and push it forward in the Pipecat pipeline for display. This design ensures our AI agent can hold coherent, multi-turn conversations in real time. Check out the FULL CODES here.
class TextDisplayProcessor(FrameProcessor):
"""Displays text frames in a conversational format"""
def __init__(self):
super().__init__()
self.conversation_count = 0
async def process_frame(self, frame: Frame, direction: FrameDirection):
await super().process_frame(frame, direction)
if isinstance(frame, TextFrame):
text = getattr(frame, "text", "")
if text.startswith("AI:"):
print(f"
{text}")
self.conversation_count += 1
print(f" 
Exchange {self.conversation_count} complete\n")
await self.push_frame(frame, direction)
class ConversationInputGenerator:
"""Generates demo conversation inputs"""
def __init__(self):
self.demo_conversations = [
"Hello! How are you doing today?",
"What's your favorite thing to talk about?",
"Can you tell me something interesting about AI?",
"What makes conversation enjoyable for you?",
"Thanks for the great chat!"
]
async def generate_conversation(self) -> AsyncGenerator[TextFrame, None]:
print("
Starting conversation simulation...\n")
for i, user_input in enumerate(self.demo_conversations):
yield TextFrame(text=user_input)
if i < len(self.demo_conversations) - 1:
await asyncio.sleep(2)We create TextDisplayProcessor to neatly format and display AI responses, tracking the number of exchanges in the conversation. Alongside it, ConversationInputGenerator simulates a sequence of user messages as TextFrame objects, adding short pauses between them to mimic a natural back-and-forth flow during the demo. Check out the FULL CODES here.
class SimpleAIAgent:
"""Simple conversational AI agent using Pipecat"""
def __init__(self):
self.chat_processor = SimpleChatProcessor()
self.display_processor = TextDisplayProcessor()
self.input_generator = ConversationInputGenerator()
def create_pipeline(self) -> Pipeline:
return Pipeline([self.chat_processor, self.display_processor])
async def run_demo(self):
print("
Simple Pipecat AI Agent Demo")
print("
Conversational AI with HuggingFace")
print("=" * 50)
pipeline = self.create_pipeline()
runner = PipelineRunner()
task = PipelineTask(pipeline)
async def produce_frames():
async for frame in self.input_generator.generate_conversation():
await task.queue_frame(frame)
await task.stop_when_done()
try:
print("
Running conversation demo...\n")
await asyncio.gather(
runner.run(task),
produce_frames(),
)
except Exception as e:
print(f"
Demo error: {e}")
logging.error(f"Pipeline error: {e}")
print(" Demo completed successfully!")In SimpleAIAgent, we tie everything together by combining the chat processor, display processor, and input generator into a single Pipecat Pipeline. The run_demo method launches the PipelineRunner to process frames asynchronously while the input generator feeds simulated user messages. This orchestrated setup allows the agent to process inputs, generate responses, and display them in real time, completing the end-to-end conversational flow. Check out the FULL CODES here.
async def main():
logging.basicConfig(level=logging.INFO)
print(" Pipecat AI Agent Tutorial")
print("
Google Colab Compatible")
print(" Free HuggingFace Models")
print("
Simple & Working Implementation")
print("=" * 60)
try:
agent = SimpleAIAgent()
await agent.run_demo()
print("\n
Tutorial Complete!")
print("\n
What You Just Saw:")
print("✓ Pipecat pipeline architecture in action")
print("✓ Custom FrameProcessor implementations")
print("✓ HuggingFace conversational AI integration")
print("✓ Real-time text processing pipeline")
print("✓ Modular, extensible design")
print("\n Next Steps:")
print("• Add real speech-to-text input")
print("• Integrate text-to-speech output")
print("• Connect to better language models")
print("• Add memory and context management")
print("• Deploy as a web service")
except Exception as e:
print(f" Tutorial failed: {e}")
import traceback
traceback.print_exc()
try:
import google.colab
print("
Google Colab detected - Ready to run!")
ENV = "colab"
except ImportError:
print("
Local environment detected")
ENV = "local"
print("\n" + "="*60)
print(" READY TO RUN!")
print("Execute this cell to start the AI conversation demo")
print("="*60)
print("\n Starting the AI Agent Demo...")
await main()We define the main function to initialize logging, set up the SimpleAIAgent, and run the demo while printing helpful progress and summary messages. We also detect whether the code is running in Google Colab or locally, display environment details, and then call await main() to start the full conversational AI pipeline execution.
In conclusion, we have a working conversational AI agent where user inputs (or simulated text frames) are passed through a processing pipeline, the HuggingFace DialoGPT model generates responses, and the results are displayed in a structured conversational format. The implementation demonstrates how Pipecat’s architecture supports asynchronous processing, stateful conversation handling, and clean separation of concerns between different processing stages. With this foundation, we can now integrate more advanced features, such as real-time speech-to-text, text-to-speech synthesis, context persistence, or richer model backends, while retaining a modular and extensible code structure.
Check out the FULL CODES here. Feel free to check out our GitHub Page for Tutorials, Codes and Notebooks. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter.
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