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*   [v0.1](https://js.langchain.com/v0.1/docs/get_started/introduction)

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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   Installation

On this page

Installation
============

Supported Environments[​](#supported-environments "Direct link to Supported Environments")
------------------------------------------------------------------------------------------

LangChain is written in TypeScript and can be used in:

*   Node.js (ESM and CommonJS) - 18.x, 19.x, 20.x
*   Cloudflare Workers
*   Vercel / Next.js (Browser, Serverless and Edge functions)
*   Supabase Edge Functions
*   Browser
*   Deno
*   Bun

However, note that individual integrations may not be supported in all environments.

Installation[​](#installation-1 "Direct link to Installation")
--------------------------------------------------------------

To install LangChain, run the following command:

*   npm
*   Yarn
*   pnpm

    npm install langchain

    yarn add langchain

    pnpm add langchain

A lot of the value of LangChain comes when integrating it with various model providers, datastores, etc. By default, the dependencies needed to do that are NOT installed. You will need to install the dependencies for specific integrations separately.

### @langchain/community[​](#langchaincommunity "Direct link to @langchain/community")

The [@langchain/community](https://www.npmjs.com/package/@langchain/community) package contains a range of third-party integrations. Install with:

*   npm
*   Yarn
*   pnpm

    npm install @langchain/community

    yarn add @langchain/community

    pnpm add @langchain/community

There are also more granular packages containing LangChain integrations for individual providers.

### @langchain/core[​](#langchaincore "Direct link to @langchain/core")

The [@langchain/core](https://www.npmjs.com/package/@langchain/core) package contains base abstractions that the rest of the LangChain ecosystem uses, along with the LangChain Expression Language. It is automatically installed along with `langchain`, but can also be used separately. Install with:

*   npm
*   Yarn
*   pnpm

    npm install @langchain/core

    yarn add @langchain/core

    pnpm add @langchain/core

### LangGraph[​](#langgraph "Direct link to LangGraph")

`langgraph` is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. Install with:

*   npm
*   Yarn
*   pnpm

    npm install @langchain/langgraph

    yarn add @langchain/langgraph

    pnpm add @langchain/langgraph

### LangSmith SDK[​](#langsmith-sdk "Direct link to LangSmith SDK")

The LangSmith SDK is automatically installed by LangChain. If you're not using it with LangChain, install with:

*   npm
*   Yarn
*   pnpm

    npm install langsmith

    yarn add langsmith

    pnpm add langsmith

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

Installing integration packages[​](#installing-integration-packages "Direct link to Installing integration packages")
---------------------------------------------------------------------------------------------------------------------

LangChain supports packages that contain module integrations with individual third-party providers. They can be as specific as [`@langchain/anthropic`](/v0.2/docs/integrations/platforms/anthropic/), which contains integrations just for Anthropic models, or as broad as [`@langchain/community`](https://www.npmjs.com/package/@langchain/community), which contains broader variety of community contributed integrations.

These packages, as well as the main LangChain package, all depend on [`@langchain/core`](https://www.npmjs.com/package/@langchain/core), which contains the base abstractions that these integration packages extend.

To ensure that all integrations and their types interact with each other properly, it is important that they all use the same version of `@langchain/core`. The best way to guarantee this is to add a `"resolutions"` or `"overrides"` field like the following in your project's `package.json`. The name will depend on your package manager:

tip

The `resolutions` or `pnpm.overrides` fields for `yarn` or `pnpm` must be set in the root `package.json` file.

If you are using `yarn`:

yarn package.json

    {  "name": "your-project",  "version": "0.0.0",  "private": true,  "engines": {    "node": ">=18"  },  "dependencies": {    "@langchain/anthropic": "^0.0.2",    "langchain": "0.0.207"  },  "resolutions": {    "@langchain/core": "0.1.5"  }}

Or for `npm`:

npm package.json

    {  "name": "your-project",  "version": "0.0.0",  "private": true,  "engines": {    "node": ">=18"  },  "dependencies": {    "@langchain/anthropic": "^0.0.2",    "langchain": "0.0.207"  },  "overrides": {    "@langchain/core": "0.1.5"  }}

Or for `pnpm`:

pnpm package.json

    {  "name": "your-project",  "version": "0.0.0",  "private": true,  "engines": {    "node": ">=18"  },  "dependencies": {    "@langchain/anthropic": "^0.0.2",    "langchain": "0.0.207"  },  "pnpm": {    "overrides": {      "@langchain/core": "0.1.5"    }  }}

Loading the library[​](#loading-the-library "Direct link to Loading the library")
---------------------------------------------------------------------------------

### TypeScript[​](#typescript "Direct link to TypeScript")

LangChain is written in TypeScript and provides type definitions for all of its public APIs.

### ESM[​](#esm "Direct link to ESM")

LangChain provides an ESM build targeting Node.js environments. You can import it using the following syntax:

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { OpenAI } from "@langchain/openai";

If you are using TypeScript in an ESM project we suggest updating your `tsconfig.json` to include the following:

tsconfig.json

    {  "compilerOptions": {    ...    "target": "ES2020", // or higher    "module": "nodenext",  }}

### CommonJS[​](#commonjs "Direct link to CommonJS")

LangChain provides a CommonJS build targeting Node.js environments. You can import it using the following syntax:

    const { OpenAI } = require("@langchain/openai");

### Cloudflare Workers[​](#cloudflare-workers "Direct link to Cloudflare Workers")

LangChain can be used in Cloudflare Workers. You can import it using the following syntax:

    import { OpenAI } from "@langchain/openai";

### Vercel / Next.js[​](#vercel--nextjs "Direct link to Vercel / Next.js")

LangChain can be used in Vercel / Next.js. We support using LangChain in frontend components, in Serverless functions and in Edge functions. You can import it using the following syntax:

    import { OpenAI } from "@langchain/openai";

### Deno / Supabase Edge Functions[​](#deno--supabase-edge-functions "Direct link to Deno / Supabase Edge Functions")

LangChain can be used in Deno / Supabase Edge Functions. You can import it using the following syntax:

    import { OpenAI } from "https://esm.sh/@langchain/openai";

or

    import { OpenAI } from "npm:@langchain/openai";

We recommend looking at our [Supabase Template](https://github.com/langchain-ai/langchain-template-supabase) for an example of how to use LangChain in Supabase Edge Functions.

### Browser[​](#browser "Direct link to Browser")

LangChain can be used in the browser. In our CI we test bundling LangChain with Webpack and Vite, but other bundlers should work too. You can import it using the following syntax:

    import { OpenAI } from "@langchain/openai";

Unsupported: Node.js 16[​](#unsupported-nodejs-16 "Direct link to Unsupported: Node.js 16")
-------------------------------------------------------------------------------------------

We do not support Node.js 16, but if you still want to run LangChain on Node.js 16, you will need to follow the instructions in this section. We do not guarantee that these instructions will continue to work in the future.

You will have to make `fetch` available globally, either:

*   run your application with `NODE_OPTIONS='--experimental-fetch' node ...`, or
*   install `node-fetch` and follow the instructions [here](https://github.com/node-fetch/node-fetch#providing-global-access)

You'll also need to [polyfill `ReadableStream`](https://www.npmjs.com/package/web-streams-polyfill) by installing:

*   npm
*   Yarn
*   pnpm

    npm i web-streams-polyfill

    yarn add web-streams-polyfill

    pnpm add web-streams-polyfill

And then adding it to the global namespace in your main entrypoint:

    import "web-streams-polyfill/es6";

Additionally you'll have to polyfill `structuredClone`, eg. by installing `core-js` and following the instructions [here](https://github.com/zloirock/core-js).

If you are running Node.js 18+, you do not need to do anything.

* * *

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#### You can leave detailed feedback [on GitHub](https://github.com/langchain-ai/langchainjs/issues/new?assignees=&labels=03+-+Documentation&projects=&template=documentation.yml&title=DOC%3A+%3CPlease+write+a+comprehensive+title+after+the+%27DOC%3A+%27+prefix%3E).

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How to stream responses from an LLM

](/v0.2/docs/how_to/streaming_llm)

*   [Supported Environments](#supported-environments)
*   [Installation](#installation-1)
    *   [@langchain/community](#langchaincommunity)
    *   [@langchain/core](#langchaincore)
    *   [LangGraph](#langgraph)
    *   [LangSmith SDK](#langsmith-sdk)
*   [Installing integration packages](#installing-integration-packages)
*   [Loading the library](#loading-the-library)
    *   [TypeScript](#typescript)
    *   [ESM](#esm)
    *   [CommonJS](#commonjs)
    *   [Cloudflare Workers](#cloudflare-workers)
    *   [Vercel / Next.js](#vercel--nextjs)
    *   [Deno / Supabase Edge Functions](#deno--supabase-edge-functions)
    *   [Browser](#browser)
*   [Unsupported: Node.js 16](#unsupported-nodejs-16)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

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