// Programmatically interact with the Hub

await createRepo({
  repo: { type: "model", name: "my-user/nlp-model" },
  accessToken: HF_TOKEN
});

await uploadFile({
  repo: "my-user/nlp-model",
  accessToken: HF_TOKEN,
  // Can work with native File in browsers
  file: {
    path: "pytorch_model.bin",
    content: new Blob(...)
  }
});

// Use all supported Inference Providers!

await inference.chatCompletion({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  provider: "sambanova", // or together, fal-ai, replicate, cohere …
  messages: [
    {
      role: "user",
      content: "Hello, nice to meet you!",
    },
  ],
  max_tokens: 512,
  temperature: 0.5,
});

await inference.textToImage({
  model: "black-forest-labs/FLUX.1-dev",
  provider: "replicate",
  inputs: "a picture of a green bird",
});

// and much more…

Hugging Face JS libraries

This is a collection of JS libraries to interact with the Hugging Face API, with TS types included.

• @huggingface/inference: Use all supported (serverless) Inference Providers or switch to Inference Endpoints (dedicated) to make calls to 100,000+ Machine Learning models
• @huggingface/hub: Interact with huggingface.co to create or delete repos and commit / download files
• @huggingface/mcp-client: A Model Context Protocol (MCP) client, and a tiny Agent library, built on top of InferenceClient.
• @huggingface/gguf: A GGUF parser that works on remotely hosted files.
• @huggingface/dduf: Similar package for DDUF (DDUF Diffusers Unified Format)
• @huggingface/tasks: The definition files and source-of-truth for the Hub's main primitives like pipeline tasks, model libraries, etc.
• @huggingface/jinja: A minimalistic JS implementation of the Jinja templating engine, to be used for ML chat templates.
• @huggingface/space-header: Use the Space mini_header outside Hugging Face
• @huggingface/ollama-utils: Various utilities for maintaining Ollama compatibility with models on the Hugging Face Hub.

We use modern features to avoid polyfills and dependencies, so the libraries will only work on modern browsers / Node.js >= 18 / Bun / Deno.

The libraries are still very young, please help us by opening issues!

Installation

From NPM

To install via NPM, you can download the libraries as needed:

npm install @huggingface/inference
npm install @huggingface/hub
npm install @huggingface/mcp-client

Then import the libraries in your code:

import { InferenceClient } from "@huggingface/inference";
import { createRepo, commit, deleteRepo, listFiles } from "@huggingface/hub";
import { McpClient } from "@huggingface/mcp-client";
import type { RepoId } from "@huggingface/hub";

From CDN or Static hosting

You can run our packages with vanilla JS, without any bundler, by using a CDN or static hosting. Using ES modules, i.e. <script type="module">, you can import the libraries in your code:

<script type="module">
    import { InferenceClient } from 'https://cdn.jsdelivr.net/npm/@huggingface/inference@4.0.3/+esm';
    import { createRepo, commit, deleteRepo, listFiles } from "https://cdn.jsdelivr.net/npm/@huggingface/hub@2.2.0/+esm";
</script>

Deno

// esm.sh
import { InferenceClient } from "https://esm.sh/@huggingface/inference"

import { createRepo, commit, deleteRepo, listFiles } from "https://esm.sh/@huggingface/hub"
// or npm:
import { InferenceClient } from "npm:@huggingface/inference"

import { createRepo, commit, deleteRepo, listFiles } from "npm:@huggingface/hub"

Usage examples

Get your HF access token in your account settings.

@huggingface/inference examples

import { InferenceClient } from "@huggingface/inference";

const HF_TOKEN = "hf_...";

const client = new InferenceClient(HF_TOKEN);

// Chat completion API
const out = await client.chatCompletion({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  messages: [{ role: "user", content: "Hello, nice to meet you!" }],
  max_tokens: 512
});
console.log(out.choices[0].message);

// Streaming chat completion API
for await (const chunk of client.chatCompletionStream({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  messages: [{ role: "user", content: "Hello, nice to meet you!" }],
  max_tokens: 512
})) {
  console.log(chunk.choices[0].delta.content);
}

/// Using a third-party provider:
await client.chatCompletion({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  messages: [{ role: "user", content: "Hello, nice to meet you!" }],
  max_tokens: 512,
  provider: "sambanova", // or together, fal-ai, replicate, cohere …
})

await client.textToImage({
  model: "black-forest-labs/FLUX.1-dev",
  inputs: "a picture of a green bird",
  provider: "fal-ai",
})



// You can also omit "model" to use the recommended model for the task
await client.translation({
  inputs: "My name is Wolfgang and I live in Amsterdam",
  parameters: {
    src_lang: "en",
    tgt_lang: "fr",
  },
});

// pass multimodal files or URLs as inputs
await client.imageToText({
  model: 'nlpconnect/vit-gpt2-image-captioning',
  data: await (await fetch('https://picsum.photos/300/300')).blob(),
})

// Using your own dedicated inference endpoint: https://hf.co/docs/inference-endpoints/
const gpt2Client = client.endpoint('https://xyz.eu-west-1.aws.endpoints.huggingface.cloud/gpt2');
const { generated_text } = await gpt2Client.textGeneration({ inputs: 'The answer to the universe is' });

// Chat Completion
const llamaEndpoint = client.endpoint(
  "https://router.huggingface.co/hf-inference/models/meta-llama/Llama-3.1-8B-Instruct"
);
const out = await llamaEndpoint.chatCompletion({
  model: "meta-llama/Llama-3.1-8B-Instruct",
  messages: [{ role: "user", content: "Hello, nice to meet you!" }],
  max_tokens: 512,
});
console.log(out.choices[0].message);

@huggingface/hub examples

import { createRepo, uploadFile, deleteFiles } from "@huggingface/hub";

const HF_TOKEN = "hf_...";

await createRepo({
  repo: "my-user/nlp-model", // or { type: "model", name: "my-user/nlp-test" },
  accessToken: HF_TOKEN
});

await uploadFile({
  repo: "my-user/nlp-model",
  accessToken: HF_TOKEN,
  // Can work with native File in browsers
  file: {
    path: "pytorch_model.bin",
    content: new Blob(...)
  }
});

await deleteFiles({
  repo: { type: "space", name: "my-user/my-space" }, // or "spaces/my-user/my-space"
  accessToken: HF_TOKEN,
  paths: ["README.md", ".gitattributes"]
});

@huggingface/mcp-client example

import { Agent } from '@huggingface/mcp-client';

const HF_TOKEN = "hf_...";

const agent = new Agent({
  provider: "auto",
  model: "Qwen/Qwen2.5-72B-Instruct",
  apiKey: HF_TOKEN,
  servers: [
    {
      // Playwright MCP
      command: "npx",
      args: ["@playwright/mcp@latest"],
    },
  ],
});

await agent.loadTools();

for await (const chunk of agent.run("What are the top 5 trending models on Hugging Face?")) {
    if ("choices" in chunk) {
        const delta = chunk.choices[0]?.delta;
        if (delta.content) {
            console.log(delta.content);
        }
    }
}

There are more features of course, check each library's README!

Formatting & testing

sudo corepack enable
pnpm install

pnpm -r format:check
pnpm -r lint:check
pnpm -r test

Building

pnpm -r build

This will generate ESM and CJS javascript files in packages/*/dist, eg packages/inference/dist/index.mjs.