The LLMLogObserver provides detailed logging of Large Language Model (LLM) activity within your Pipecat pipeline. It tracks the entire lifecycle of LLM interactions, from initial prompts to final responses.

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Frame Types Monitored

The observer tracks the following frame types (only from/to LLM service):
  • LLMFullResponseStartFrame: When the LLM begins generating a response
  • LLMFullResponseEndFrame: When the LLM completes its response
  • LLMTextFrame: Individual text chunks generated by the LLM
  • FunctionCallInProgressFrame: Function/tool calls made by the LLM
  • LLMMessagesFrame: Input messages sent to the LLM
  • OpenAILLMContextFrame: Context information for OpenAI LLM calls
  • FunctionCallResultFrame: Results returned from function calls

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Usage

from pipecat.observers.loggers.llm_log_observer import LLMLogObserver

task = PipelineTask(
    pipeline,
    params=PipelineParams(
        observers=[LLMLogObserver()],
    ),
)

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Log Output Format

The observer uses emojis and consistent formatting for easy log reading:
  • 🧠 [Source] → LLM START/END RESPONSE
  • 🧠 [Source] → LLM GENERATING: [text]
  • 🧠 [Source] → LLM FUNCTION CALL: [details]
  • 🧠 → [Destination] LLM MESSAGES FRAME: [messages]
  • 🧠 → [Destination] LLM CONTEXT FRAME: [context]
All log entries include timestamps for precise timing analysis.