> ## Documentation Index
> Fetch the complete documentation index at: https://docs.pipecat.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Log Observer

> Logging LLM activity in Pipecat

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.

## 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
* **LLMContextFrame**: Updated context sent to the LLM
* **FunctionCallResultFrame**: Results returned from function calls

## Usage

```python theme={null}
from pipecat.observers.loggers.llm_log_observer import LLMLogObserver

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

## 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.