AnthropicLLM Class — langchain Architecture

Source Code

libs/partners/anthropic/langchain_anthropic/llms.py lines 134–433

class AnthropicLLM(LLM, _AnthropicCommon):
    """Anthropic text completion large language model (legacy LLM).

    To use, you should have the environment variable `ANTHROPIC_API_KEY`
    set with your API key, or pass it as a named parameter to the constructor.

    Example:
        ```python
        from langchain_anthropic import AnthropicLLM

        model = AnthropicLLM(model="claude-sonnet-4-5")
        ```
    """

    model_config = ConfigDict(
        populate_by_name=True,
        arbitrary_types_allowed=True,
    )

    @model_validator(mode="before")
    @classmethod
    def raise_warning(cls, values: dict) -> Any:
        """Raise warning that this class is deprecated."""
        warnings.warn(
            "This Anthropic LLM is deprecated. "
            "Please use `from langchain_anthropic import ChatAnthropic` "
            "instead",
            stacklevel=2,
        )
        return values

    @property
    def _llm_type(self) -> str:
        """Return type of llm."""
        return "anthropic-llm"

    @property
    def lc_secrets(self) -> dict[str, str]:
        """Return a mapping of secret keys to environment variables."""
        return {"anthropic_api_key": "ANTHROPIC_API_KEY"}

    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Whether this class can be serialized by langchain."""
        return True

    @property
    def _identifying_params(self) -> dict[str, Any]:
        """Get the identifying parameters."""
        return {
            "model": self.model,
            "max_tokens": self.max_tokens,
            "temperature": self.temperature,
            "top_k": self.top_k,
            "top_p": self.top_p,
            "model_kwargs": self.model_kwargs,
            "streaming": self.streaming,
            "default_request_timeout": self.default_request_timeout,
            "max_retries": self.max_retries,
        }

    def _get_ls_params(
        self,
        stop: list[str] | None = None,
        **kwargs: Any,
    ) -> LangSmithParams:
        """Get standard params for tracing."""
        params = super()._get_ls_params(stop=stop, **kwargs)
        identifying_params = self._identifying_params
        if max_tokens := kwargs.get(
            "max_tokens",
            identifying_params.get("max_tokens"),
        ):
            params["ls_max_tokens"] = max_tokens
        return params

    def _format_messages(self, prompt: str) -> list[dict[str, str]]:
        """Convert prompt to Messages API format."""
        messages = []

        # Handle legacy prompts that might have HUMAN_PROMPT/AI_PROMPT markers