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Implementation:Langchain ai Langchain AnthropicLLM

From Leeroopedia
Knowledge Sources
Domains LLM Framework, Anthropic, Text Completion
Last Updated 2026-02-11 00:00 GMT

Overview

The AnthropicLLM class provides a deprecated legacy text-completion wrapper around the Anthropic Messages API, supporting synchronous, asynchronous, and streaming text generation.

Description

This module defines the AnthropicLLM class and its base class _AnthropicCommon in the langchain-anthropic partner package. AnthropicLLM extends LangChain's LLM base class to provide a simple text-in/text-out interface to the Anthropic API. It is explicitly marked as deprecated; users should use ChatAnthropic instead. The class internally converts text prompts to the Anthropic Messages API format, handling both legacy prompt markers (Human:/Assistant:) and plain text.

Usage

This class is deprecated. Users should import ChatAnthropic from langchain_anthropic instead. The legacy import remains available for backward compatibility.

Code Reference

Source Location

Signature

class _AnthropicCommon(BaseLanguageModel):
    client: Any = None
    async_client: Any = None
    model: str = Field(default="claude-sonnet-4-5", alias="model_name")
    max_tokens: int = Field(default=1024, alias="max_tokens_to_sample")
    temperature: float | None = None
    top_k: int | None = None
    top_p: float | None = None
    streaming: bool = False
    default_request_timeout: float | None = None
    max_retries: int = 2
    anthropic_api_url: str | None = Field(alias="base_url", ...)
    anthropic_api_key: SecretStr = Field(alias="api_key", ...)
    ...

class AnthropicLLM(LLM, _AnthropicCommon):
    def _call(self, prompt: str, stop: list[str] | None = None, ...) -> str: ...
    async def _acall(self, prompt: str, stop: list[str] | None = None, ...) -> str: ...
    def _stream(self, prompt: str, stop: list[str] | None = None, ...) -> Iterator[GenerationChunk]: ...
    async def _astream(self, prompt: str, stop: list[str] | None = None, ...) -> AsyncIterator[GenerationChunk]: ...

Import

from langchain_anthropic import AnthropicLLM  # Deprecated

# Recommended replacement:
from langchain_anthropic import ChatAnthropic

I/O Contract

Inputs

Name Type Required Description
model str No Model name to use (default: "claude-sonnet-4-5"). Alias: model_name
max_tokens int No Maximum tokens to predict per generation (default: 1024). Alias: max_tokens_to_sample
temperature float or None No Randomness in generation (default: None)
top_k int or None No Number of most likely tokens to consider (default: None)
top_p float or None No Nucleus sampling threshold (default: None)
streaming bool No Whether to stream results (default: False)
default_request_timeout float or None No Timeout for requests in seconds (default: None, Anthropic default is 600s)
max_retries int No Number of retries for failed requests (default: 2)
anthropic_api_url str or None No Base URL for API requests. Alias: base_url. Env: ANTHROPIC_API_URL
anthropic_api_key SecretStr No API key. Alias: api_key. Env: ANTHROPIC_API_KEY

Outputs

Name Type Description
result str Generated text from the Anthropic model (for _call and _acall)
chunk GenerationChunk Individual token chunks (for _stream and _astream)

Key Mechanisms

Prompt Formatting

The _format_messages method converts text prompts into the Anthropic Messages API format:

  • Legacy prompts: Prompts containing Human:/Assistant: markers are split and converted to alternating user/assistant messages
  • Plain text: Wrapped in a single user message, with Human:/Assistant: markers stripped if present

Environment Validation

The validate_environment model validator initializes both synchronous (anthropic.Anthropic) and asynchronous (anthropic.AsyncAnthropic) clients using the configured API key, base URL, timeout, and retry settings.

Deprecation Warning

A model_validator on AnthropicLLM emits a deprecation warning on every instantiation, directing users to ChatAnthropic.

Usage Examples

Basic Usage

# Deprecated - use ChatAnthropic instead
from langchain_anthropic import AnthropicLLM

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

# Synchronous call
response = model.invoke("What is the meaning of life?")
print(response)

Streaming Usage

from langchain_anthropic import AnthropicLLM

model = AnthropicLLM(model="claude-sonnet-4-5", streaming=True)

for chunk in model.stream("Write a haiku about Python."):
    print(chunk, end="")

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