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*   Conversation buffer memory

Conversation buffer memory
==========================

This notebook shows how to use `BufferMemory`. This memory allows for storing of messages, then later formats the messages into a prompt input variable.

We can first extract it as a string.

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { OpenAI } from "@langchain/openai";import { BufferMemory } from "langchain/memory";import { ConversationChain } from "langchain/chains";const model = new OpenAI({});const memory = new BufferMemory();const chain = new ConversationChain({ llm: model, memory: memory });const res1 = await chain.call({ input: "Hi! I'm Jim." });console.log({ res1 });

    {response: " Hi Jim! It's nice to meet you. My name is AI. What would you like to talk about?"}

    const res2 = await chain.call({ input: "What's my name?" });console.log({ res2 });

    {response: ' You said your name is Jim. Is there anything else you would like to talk about?'}

You can also load messages into a `BufferMemory` instance by creating and passing in a `ChatHistory` object. This lets you easily pick up state from past conversations:

    import { BufferMemory, ChatMessageHistory } from "langchain/memory";import { HumanMessage, AIMessage } from "langchain/schema";const pastMessages = [  new HumanMessage("My name's Jonas"),  new AIMessage("Nice to meet you, Jonas!"),];const memory = new BufferMemory({  chatHistory: new ChatMessageHistory(pastMessages),});

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