UsageMetadataCallbackHandler Class — langchain Architecture

Source Code

libs/core/langchain_core/callbacks/usage.py lines 18–89

class UsageMetadataCallbackHandler(BaseCallbackHandler):
    """Callback Handler that tracks `AIMessage.usage_metadata`.

    Example:
        ```python
        from langchain.chat_models import init_chat_model
        from langchain_core.callbacks import UsageMetadataCallbackHandler

        llm_1 = init_chat_model(model="openai:gpt-4o-mini")
        llm_2 = init_chat_model(model="anthropic:claude-3-5-haiku-20241022")

        callback = UsageMetadataCallbackHandler()
        result_1 = llm_1.invoke("Hello", config={"callbacks": [callback]})
        result_2 = llm_2.invoke("Hello", config={"callbacks": [callback]})
        callback.usage_metadata
        ```

        ```txt
        {'gpt-4o-mini-2024-07-18': {'input_tokens': 8,
          'output_tokens': 10,
          'total_tokens': 18,
          'input_token_details': {'audio': 0, 'cache_read': 0},
          'output_token_details': {'audio': 0, 'reasoning': 0}},
         'claude-3-5-haiku-20241022': {'input_tokens': 8,
          'output_tokens': 21,
          'total_tokens': 29,
          'input_token_details': {'cache_read': 0, 'cache_creation': 0}}}
        ```

    !!! version-added "Added in `langchain-core` 0.3.49"

    """

    def __init__(self) -> None:
        """Initialize the `UsageMetadataCallbackHandler`."""
        super().__init__()
        self._lock = threading.Lock()
        self.usage_metadata: dict[str, UsageMetadata] = {}

    @override
    def __repr__(self) -> str:
        return str(self.usage_metadata)

    @override
    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> None:
        """Collect token usage."""
        # Check for usage_metadata (langchain-core >= 0.2.2)
        try:
            generation = response.generations[0][0]
        except IndexError:
            generation = None

        usage_metadata = None
        model_name = None
        if isinstance(generation, ChatGeneration):
            try:
                message = generation.message
                if isinstance(message, AIMessage):
                    usage_metadata = message.usage_metadata
                    model_name = message.response_metadata.get("model_name")
            except AttributeError:
                pass

        # update shared state behind lock
        if usage_metadata and model_name:
            with self._lock:
                if model_name not in self.usage_metadata:
                    self.usage_metadata[model_name] = usage_metadata
                else:
                    self.usage_metadata[model_name] = add_usage(
                        self.usage_metadata[model_name], usage_metadata
                    )