LangChain Reference home pageLangChain ReferenceLangChain Reference
  • GitHub
  • Main Docs
Deep Agents
LangChain
LangGraph
Integrations
LangSmith
  • Overview
  • MCP Adapters
    Standard Tests
    Text Splitters
    • Overview
    • Agents
    • Callbacks
    • Chains
    • Chat models
    • Embeddings
    • Evaluation
    • Globals
    • Hub
    • Memory
    • Output parsers
    • Retrievers
    • Runnables
    • LangSmith
    • Storage
    ⌘I

    LangChain Assistant

    Ask a question to get started

    Enter to send•Shift+Enter new line

    Menu

    MCP Adapters
    Standard Tests
    Text Splitters
    OverviewAgentsCallbacksChainsChat modelsEmbeddingsEvaluationGlobalsHubMemoryOutput parsersRetrieversRunnablesLangSmithStorage
    Language
    Theme
    Pythonlangchain-classicchainsllm
    Moduleā—Since v1.0

    llm

    Chain that just formats a prompt and calls an LLM.

    Classes

    View source on GitHub
    class
    Chain
    deprecatedclass
    LLMChain

    Abstract base class for creating structured sequences of calls to components.

    Chains should be used to encode a sequence of calls to components like models, document retrievers, other chains, etc., and provide a simple interface to this sequence.

    Chain to run queries against LLMs.

    This class is deprecated. See below for an example implementation using LangChain runnables:

    from langchain_core.output_parsers import StrOutputParser
    from langchain_core.prompts import PromptTemplate
    from langchain_openai import OpenAI
    
    prompt_template = "Tell me a {adjective} joke"
    prompt = PromptTemplate(input_variables=["adjective"], template=prompt_template)
    model = OpenAI()
    chain = prompt | model | StrOutputParser()
    
    chain.invoke("your adjective here")