Workers AI allows you to run machine learning models, on the Cloudflare network, from your own code. This will help you getting started with Cloudflare Workers AI chat models. For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the API reference.

​
Overview

​
Integration details

ClassPackageLocalSerializablePY supportDownloadsVersion
ChatCloudflareWorkersAI@langchain/cloudflare❌✅❌NPM - DownloadsNPM - Version

​
Model features

See the links in the table headers below for guides on how to use specific features.
Tool callingStructured outputJSON modeImage inputAudio inputVideo inputToken-level streamingToken usageLogprobs
❌❌❌✅❌❌✅❌❌

​
Setup

To access Cloudflare Workers AI models you’ll need to create a Cloudflare account, get an API key, and install the @langchain/cloudflare integration package.

​
Credentials

Head to this page to sign up to Cloudflare and generate an API key. Once you’ve done this, note your CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN. Passing a binding within a Cloudflare Worker is not yet supported.

​
Installation

The LangChain ChatCloudflareWorkersAI integration lives in the @langchain/cloudflare package: 
import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/cloudflare @langchain/core
</Npm2Yarn>

​
Instantiation

Now we can instantiate our model object and generate chat completions: 
// @lc-docs-hide-cell

// @ts-expect-error Deno is not recognized
const CLOUDFLARE_ACCOUNT_ID = Deno.env.get("CLOUDFLARE_ACCOUNT_ID");
// @ts-expect-error Deno is not recognized
const CLOUDFLARE_API_TOKEN = Deno.env.get("CLOUDFLARE_API_TOKEN");

import { ChatCloudflareWorkersAI } from "@langchain/cloudflare";

const llm = new ChatCloudflareWorkersAI({
  model: "@cf/meta/llama-2-7b-chat-int8", // Default value
  cloudflareAccountId: CLOUDFLARE_ACCOUNT_ID,
  cloudflareApiToken: CLOUDFLARE_API_TOKEN,
  // Pass a custom base URL to use Cloudflare AI Gateway
  // baseUrl: `https://gateway.ai.cloudflare.com/v1/{YOUR_ACCOUNT_ID}/{GATEWAY_NAME}/workers-ai/`,
});

​
Invocation

const aiMsg = await llm.invoke([
  [
    "system",
    "You are a helpful assistant that translates English to French. Translate the user sentence.",
  ],
  ["human", "I love programming."],
])
aiMsg

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: 'I can help with that! The translation of "I love programming" in French is:\n' +
      "\n" +
      `"J'adore le programmati`... 4 more characters,
    tool_calls: [],
    invalid_tool_calls: [],
    additional_kwargs: {},
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: 'I can help with that! The translation of "I love programming" in French is:\n' +
    "\n" +
    `"J'adore le programmati`... 4 more characters,
  name: undefined,
  additional_kwargs: {},
  response_metadata: {},
  tool_calls: [],
  invalid_tool_calls: []
}

console.log(aiMsg.content)

I can help with that! The translation of "I love programming" in French is:

"J'adore le programmation."

​
Chaining

We can chain our model with a prompt template like so: 
import { ChatPromptTemplate } from "@langchain/core/prompts"

const prompt = ChatPromptTemplate.fromMessages(
  [
    [
      "system",
      "You are a helpful assistant that translates {input_language} to {output_language}.",
    ],
    ["human", "{input}"],
  ]
)

const chain = prompt.pipe(llm);
await chain.invoke(
  {
    input_language: "English",
    output_language: "German",
    input: "I love programming.",
  }
)

AIMessage {
  lc_serializable: true,
  lc_kwargs: {
    content: "Das Programmieren ist für mich sehr Valent sein!",
    tool_calls: [],
    invalid_tool_calls: [],
    additional_kwargs: {},
    response_metadata: {}
  },
  lc_namespace: [ "langchain_core", "messages" ],
  content: "Das Programmieren ist für mich sehr Valent sein!",
  name: undefined,
  additional_kwargs: {},
  response_metadata: {},
  tool_calls: [],
  invalid_tool_calls: []
}

​
API reference

For detailed documentation of all ChatCloudflareWorkersAI features and configurations head to the API reference.