This guide shows how to assemble a fully working conversational AI agent from scratch with the Pipecat framework. It demonstrates how to connect custom FrameProcessor subclasses in a Pipeline: one interpreter for user prompts that calls a HuggingFace model, and another to render the chat history. A ConversationInputGenerator feeds simulated TextFrame objects to model the dialogue, while PipelineRunner and PipelineTask handle asynchronous data flow. Together, these pieces illustrate Pipecat’s frame-oriented processing, letting developers plug in components—including language models, output formatting, and future extensions like voice modules—in a clean, modular fashion.
To get started, install Pipecat, Transformers, and PyTorch, then import the essentials: Pipeline, PipelineRunner, FrameProcessor from Pipecat, and HuggingFace’s pipeline API for text generation. With these libraries in place, the environment is ready to construct and execute the conversation pipeline end to end. An optional GPU setup can be enabled by configuring PyTorch to detect and utilize any available CUDA device for acceleration.
We next define SimpleChatProcessor, which loads the DialoGPT-small checkpoint via HuggingFace for response generation. It preserves chat context so each incoming TextFrame triggers a call to the model, then formats and cleans the output before emitting a new frame along the pipeline, ensuring multi-turn coherence in real time. It uses the underlying tokenizer to correctly encode inputs and maintains a sliding window of prior exchanges to keep model context within limits.
TextDisplayProcessor takes responsibility for formatting and printing each message exchange, keeping track of turn counts. Meanwhile, ConversationInputGenerator simulates a user session by yielding a series of TextFrame instances and inserting brief pauses, creating a realistic back-and-forth flow suitable for demos without manual input.
In SimpleAIAgent, these pieces come together. The pipeline is constructed by chaining the input generator, chat processor, and display processor. Its run_demo method leverages PipelineRunner to drive frame processing asynchronously, while the generator feeds simulated messages. This setup orchestrates the complete cycle: accept input, generate a response, and display the result in sequence. Optionally, PipelineTask can be invoked to manage discrete processing jobs within the runner.
The main routine configures logging, instantiates SimpleAIAgent, and launches the demo loop. It checks for a Colab environment versus local execution, outputs relevant system information, and reports Python and package versions to help troubleshoot dependency issues. Then it invokes await main() to kick off the whole conversational AI workflow.
The result is a functioning chat agent where TextFrame objects carrying user text pass through a defined pipeline, the DialoGPT model provides replies, and TextDisplayProcessor outputs each turn cleanly. This design highlights Pipecat’s strengths: asynchronous frame handling, separation of concerns across processors, and flexible extensibility. Future enhancements—such as integrating real-time speech-to-text, adding text-to-speech output, persisting conversation history, or swapping in a different language model backend—can slot into the pipeline with minimal changes, preserving a clear, maintainable code structure.