Introducing Agents.js: Give tools to your LLMs using JavaScript
nsarrazin
We have recently been working on Agents.js at [huggingface.js](https://github.com/huggingface/huggingface.js/blob/main/packages/agents/README.md). It's a new library for giving tool access to LLMs from JavaScript in either the browser or the server. It ships with a few multi-modal tools out of the box and can easily be extended with your own tools and language models.

## Installation

Getting started is very easy, you can grab the library from npm with the following:

```
npm install @huggingface/agents
```

## Usage

The library exposes the `HfAgent` object which is the entry point to the library. You can instantiate it like this:

```ts
import { HfAgent } from "@huggingface/agents";

const HF_ACCESS_TOKEN = "hf_..."; // get your token at https://huggingface.co/settings/tokens

const agent = new HfAgent(HF_ACCESS_TOKEN);
```

Afterward, using the agent is easy. You give it a plain-text command and it will return some messages.

```ts
const code = await agent.generateCode(
  "Draw a picture of a rubber duck with a top hat, then caption this picture."
);
```

which in this case generated the following code

```js
// code generated by the LLM
async function generate() {
  const output = await textToImage("rubber duck with a top hat");
  message("We generate the duck picture", output);
  const caption = await imageToText(output);
  message("Now we caption the image", caption);
  return output;
}
```

Then the code can be evaluated as such:

```ts
const messages = await agent.evaluateCode(code);
```

The messages returned by the agent are objects with the following shape:

```ts
export interface Update {
	message: string;
	data: undefined | string | Blob;
```

where `message` is an info text and `data` can contain either a string or a blob. The blob can be used to display images or audio.

If you trust your environment (see [warning](#usage-warning)), you can also run the code directly from the prompt with `run` :

```ts
const messages = await agent.run(
  "Draw a picture of a rubber duck with a top hat, then caption this picture."
);
```

### Usage warning

Currently using this library will mean evaluating arbitrary code in the browser (or in Node). This is a security risk and should not be done in an untrusted environment. We recommend that you use `generateCode` and `evaluateCode` instead of `run` in order to check what code you are running.

## Custom LLMs 💬

By default `HfAgent` will use [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5) hosted Inference API as the LLM. This can be customized however.

When instancing your `HfAgent` you can pass a custom LLM. A LLM in this context is any async function that takes a string input and returns a promise for a string. For example if you have an OpenAI API key you could make use of it like this:

```ts
import { Configuration, OpenAIApi } from "openai";

const HF_ACCESS_TOKEN = "hf_...";
const api = new OpenAIApi(new Configuration({ apiKey: "sk-..." }));

const llmOpenAI = async (prompt: string): Promise<string> => {
  return (
    (
      await api.createCompletion({
        model: "text-davinci-003",
        prompt: prompt,
        max_tokens: 1000,
      })
    ).data.choices[0].text ?? ""
  );
};

const agent = new HfAgent(HF_ACCESS_TOKEN, llmOpenAI);
```

## Custom Tools 🛠️

Agents.js was designed to be easily expanded with custom tools & examples. For example if you wanted to add a tool that would translate text from English to German you could do it like this:

```ts
import type { Tool } from "@huggingface/agents/src/types";

const englishToGermanTool: Tool = {
  name: "englishToGerman",
  description:
    "Takes an input string in english and returns a german translation. ",
  examples: [
    {
      prompt: "translate the string 'hello world' to german",
      code: `const output = englishToGerman("hello world")`,
      tools: ["englishToGerman"],
    },
    {
      prompt:
        "translate the string 'The quick brown fox jumps over the lazy dog` into german",
      code: `const output = englishToGerman("The quick brown fox jumps over the lazy dog")`,
      tools: ["englishToGerman"],
    },
  ],
  call: async (input, inference) => {
    const data = await input;
    if (typeof data !== "string") {
      throw new Error("Input must be a string");
    }
    const result = await inference.translation({
      model: "t5-base",
      inputs: input,
    });
    return result.translation_text;
  },
};
```

Now this tool can be added to the list of tools when initiating your agent.

```ts
import { HfAgent, LLMFromHub, defaultTools } from "@huggingface/agents";

const HF_ACCESS_TOKEN = "hf_...";

const agent = new HfAgent(HF_ACCESS_TOKEN, LLMFromHub("hf_..."), [
  englishToGermanTool,
  ...defaultTools,
]);
```

## Passing input files to the agent 🖼️

The agent can also take input files to pass along to the tools. You can pass an optional [`FileList`](https://developer.mozilla.org/en-US/docs/Web/API/FileList) to `generateCode` and `evaluateCode` as such:

If you have the following html:

```html
<input id="fileItem" type="file" />
```

Then you can do:

```ts
const agent = new HfAgent(HF_ACCESS_TOKEN);
const files = document.getElementById("fileItem").files; // FileList type
const code = agent.generateCode(
  "Caption the image and then read the text out loud.",
  files
);
```

Which generated the following code when passing an image:

```ts
// code generated by the LLM
async function generate(image) {
  const caption = await imageToText(image);
  message("First we caption the image", caption);
  const output = await textToSpeech(caption);
  message("Then we read the caption out loud", output);
  return output;
}
```

## Demo 🎉

We've been working on a demo for Agents.js that you can try out [here](https://nsarrazin-agents-js-oasst.hf.space/). It's powered by the same Open Assistant 30B model that we use on HuggingChat and uses tools called from the hub. 🚀