LangChain LLM

LangChain LLM is the core wrapper class that lets developers call any large language model (LLM) through a unified interface in the LangChain framework. With a single import—from langchain.llms import OpenAI, Anthropic, HuggingFaceHub—you can swap GPT-4, Claude, or an open-source model without changing the surrounding code. The wrapper standardizes methods such as generate(), stream(), and get_num_tokens(), handles async batching, and plugs into LangChain’s callbacks for real-time tracing and cost tracking. Built-in retry, exponential back-off, and token-limiting guards improve reliability, while environment variables keep keys secure. Because each LLM subclass inherits the same schema, you can drop an LLMChain, Retrieval-Augmented Generation (RAG) component, or autonomous agent into production and later pin a cheaper or faster model with one line of Python. Fine-tune endpoints, temperature, and system prompts are passed via a typed config, giving teams granular control over creativity, latency, and compliance across clouds