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*   How to stream chat model responses

On this page

How to stream chat model responses
==================================

All [chat models](https://v02.api.js.langchain.com/classes/langchain_core_language_models_chat_models.BaseChatModel.html) implement the [Runnable interface](https://v02.api.js.langchain.com/classes/langchain_core_runnables.Runnable.html), which comes with a **default** implementations of standard runnable methods (i.e. `invoke`, `batch`, `stream`, `streamEvents`).

The **default** streaming implementation provides an `AsyncGenerator` that yields a single value: the final output from the underlying chat model provider.

tip

The **default** implementation does **not** provide support for token-by-token streaming, but it ensures that the the model can be swapped in for any other model as it supports the same standard interface.

The ability to stream the output token-by-token depends on whether the provider has implemented proper streaming support.

See which [integrations support token-by-token streaming here](/v0.2/docs/integrations/chat/).

Streaming[​](#streaming "Direct link to Streaming")
---------------------------------------------------

Below, we use a `---` to help visualize the delimiter between tokens.

### Pick your chat model:

*   OpenAI
*   Anthropic
*   FireworksAI
*   MistralAI
*   Groq
*   VertexAI

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/openai 

    yarn add @langchain/openai 

    pnpm add @langchain/openai 

#### Add environment variables

    OPENAI_API_KEY=your-api-key

#### Instantiate the model

    import { ChatOpenAI } from "@langchain/openai";const model = new ChatOpenAI({  model: "gpt-3.5-turbo",  temperature: 0});

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/anthropic 

    yarn add @langchain/anthropic 

    pnpm add @langchain/anthropic 

#### Add environment variables

    ANTHROPIC_API_KEY=your-api-key

#### Instantiate the model

    import { ChatAnthropic } from "@langchain/anthropic";const model = new ChatAnthropic({  model: "claude-3-sonnet-20240229",  temperature: 0});

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/community 

    yarn add @langchain/community 

    pnpm add @langchain/community 

#### Add environment variables

    FIREWORKS_API_KEY=your-api-key

#### Instantiate the model

    import { ChatFireworks } from "@langchain/community/chat_models/fireworks";const model = new ChatFireworks({  model: "accounts/fireworks/models/firefunction-v1",  temperature: 0});

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/mistralai 

    yarn add @langchain/mistralai 

    pnpm add @langchain/mistralai 

#### Add environment variables

    MISTRAL_API_KEY=your-api-key

#### Instantiate the model

    import { ChatMistralAI } from "@langchain/mistralai";const model = new ChatMistralAI({  model: "mistral-large-latest",  temperature: 0});

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/groq 

    yarn add @langchain/groq 

    pnpm add @langchain/groq 

#### Add environment variables

    GROQ_API_KEY=your-api-key

#### Instantiate the model

    import { ChatGroq } from "@langchain/groq";const model = new ChatGroq({  model: "mixtral-8x7b-32768",  temperature: 0});

#### Install dependencies

tip

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

*   npm
*   yarn
*   pnpm

    npm i @langchain/google-vertexai 

    yarn add @langchain/google-vertexai 

    pnpm add @langchain/google-vertexai 

#### Add environment variables

    GOOGLE_APPLICATION_CREDENTIALS=credentials.json

#### Instantiate the model

    import { ChatVertexAI } from "@langchain/google-vertexai";const model = new ChatVertexAI({  model: "gemini-1.5-pro",  temperature: 0});

    for await (const chunk of await model.stream(  "Write me a 1 verse song about goldfish on the moon")) {  console.log(`${chunk.content}---`);}

    ---Here--- is--- a------1------verse--- song--- about--- gol---dfish--- on--- the--- moon---:---Gol---dfish--- on--- the--- moon---,--- swimming--- through--- the--- sk---ies---,---Floating--- in--- the--- darkness---,--- beneath--- the--- lunar--- eyes---.---Weight---less--- as--- they--- drift---,--- through--- the--- endless--- voi---d,---D---rif---ting---,--- swimming---,--- exploring---,--- this--- new--- worl---d unexp---lo---ye---d.---------

Stream events[​](#stream-events "Direct link to Stream events")
---------------------------------------------------------------

Chat models also support the standard [streamEvents()](https://v02.api.js.langchain.com/classes/langchain_core_runnables.Runnable.html#streamEvents) method.

This method is useful if you’re streaming output from a larger LLM application that contains multiple steps (e.g., a chain composed of a prompt, chat model and parser).

    let idx = 0;for await (const event of model.streamEvents(  "Write me a 1 verse song about goldfish on the moon",  {    version: "v1",  })) {  idx += 1;  if (idx >= 5) {    console.log("...Truncated");    break;  }  console.log(event);}

    {  run_id: "a84e1294-d281-4757-8f3f-dc4440612949",  event: "on_llm_start",  name: "ChatAnthropic",  tags: [],  metadata: {},  data: { input: "Write me a 1 verse song about goldfish on the moon" }}{  event: "on_llm_stream",  run_id: "a84e1294-d281-4757-8f3f-dc4440612949",  tags: [],  metadata: {},  name: "ChatAnthropic",  data: {    chunk: AIMessageChunk {      lc_serializable: true,      lc_kwargs: {        content: "",        additional_kwargs: {          id: "msg_01DqDQ9in33ZhmrCzdZaRNMZ",          type: "message",          role: "assistant",          model: "claude-3-haiku-20240307"        },        tool_calls: [],        invalid_tool_calls: [],        tool_call_chunks: [],        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "",      name: undefined,      additional_kwargs: {        id: "msg_01DqDQ9in33ZhmrCzdZaRNMZ",        type: "message",        role: "assistant",        model: "claude-3-haiku-20240307"      },      response_metadata: {},      tool_calls: [],      invalid_tool_calls: [],      tool_call_chunks: []    }  }}{  event: "on_llm_stream",  run_id: "a84e1294-d281-4757-8f3f-dc4440612949",  tags: [],  metadata: {},  name: "ChatAnthropic",  data: {    chunk: AIMessageChunk {      lc_serializable: true,      lc_kwargs: {        content: "Here",        additional_kwargs: {},        tool_calls: [],        invalid_tool_calls: [],        tool_call_chunks: [],        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: "Here",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: [],      tool_call_chunks: []    }  }}{  event: "on_llm_stream",  run_id: "a84e1294-d281-4757-8f3f-dc4440612949",  tags: [],  metadata: {},  name: "ChatAnthropic",  data: {    chunk: AIMessageChunk {      lc_serializable: true,      lc_kwargs: {        content: " is",        additional_kwargs: {},        tool_calls: [],        invalid_tool_calls: [],        tool_call_chunks: [],        response_metadata: {}      },      lc_namespace: [ "langchain_core", "messages" ],      content: " is",      name: undefined,      additional_kwargs: {},      response_metadata: {},      tool_calls: [],      invalid_tool_calls: [],      tool_call_chunks: []    }  }}...Truncated

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You’ve now seen a few ways you can stream chat model responses.

Next, check out this guide for more on [streaming with other LangChain modules](/v0.2/docs/how_to/streaming).

* * *

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*   [Streaming](#streaming)
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