asyncLLM

Fetch LLM responses as an async iterable.

Features

🚀 Lightweight (<2KB) and dependency-free
🔄 Works with multiple LLM providers (OpenAI, Anthropic, Gemini, and more)
🌐 Browser and Node.js compatible
📦 Easy to use with ES modules

Installation

npm install asyncllm

Usage

Call asyncLLM() just like you would use fetch with any LLM provider with streaming responses.

OpenAI Streaming. Many providers like Azure, Groq, OpenRouter, etc. follow the OpenAI API.
Anthropic Streaming
Gemini Streaming

The result is an async generator that yields objects with content, tool, and args properties.

For example, to update the DOM with the LLM's response:

<!DOCTYPE html>
<html lang="en">
  <body>
    <div id="output"></div>
  </body>

  <script type="module">
    import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@1";

    const apiKey = "YOUR_API_KEY";

    // Example usage with OpenAI
    for await (const { content } of asyncLLM("https://api.openai.com/v1/chat/completions", {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        Authorization: `Bearer ${apiKey}`,
      },
      body: JSON.stringify({
        model: "gpt-4",
        stream: true,
        messages: [{ role: "user", content: "Hello, world!" }],
      }),
    })) {
      // Update the output in real time.
      document.getElementById("output").textContent = content;
    }
  </script>
</html>

Node.js or bundled projects

import { asyncLLM } from "asyncllm";

// Usage is the same as in the browser example

API

`asyncLLM(request: string | Request, options?: RequestInit): AsyncGenerator<LLMEvent, void, unknown>`

Fetches streaming responses from LLM providers and yields events.

request: The URL or Request object for the LLM API endpoint
options: Optional fetch options

Returns an async generator that yields LLMEvent objects.

LLMEvent

content: The text content of the response
tool: The name of the tool being called (for function calling)
args: The arguments for the tool call (for function calling) as a JSON-encoded string, e.g. {"order_id":"123456"}
message: The raw message object from the LLM provider

Examples

OpenAI

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@1";

const body = {
  model: "gpt-4o-mini",
  stream: true,
  messages: [
    { role: "system", content: "You are a helpful assistant." },
    { role: "user", content: "What is 2+2?" },
  ],
  temperature: 0.7,
  max_tokens: 10,
  tools: [
    {
      type: "function",
      function: {
        name: "get_weather",
        description: "Get the weather for a location",
        parameters: { type: "object", properties: { location: { type: "string" } }, required: ["location"] },
      },
    },
  ],
};

for await (const { content } of asyncLLM("https://api.openai.com/v1/chat/completions", {
  method: "POST",
  headers: { "Content-Type": "application/json", Authorization: `Bearer ${apiKey}` },
  body: JSON.stringify(body),
})) {
  console.log(content);
}

Anthropic

The package includes an Anthropic adapter that converts OpenAI-style requests to Anthropic's format, allowing you to use the same code structure across providers.

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@1";
import { anthropic } from "https://cdn.jsdelivr.net/npm/asyncllm@1/dist/anthropic.js";

// You can use the anthropic() adapter to convert OpenAI-style requests to Anthropic's format.
const body = anthropic({
  // Same as OpenAI example above
});

// Or you can use the asyncLLM() function directly with the Anthropic API endpoint.
const body = {
  model: "claude-3-haiku-20240307",
  stream: true,
  max_tokens: 10,
  messages: [{ role: "user", content: "What is 2 + 2" }],
};

for await (const { content } of asyncLLM("https://api.anthropic.com/v1/messages", {
  headers: { "Content-Type": "application/json", "x-api-key": apiKey },
  body: JSON.stringify(body),
})) {
  console.log(content);
}

The Anthropic adapter supports:

System messages
Multi-modal content (text and images only, no audio support)
Model parameters (temperature, max_tokens, top_p, stop, metadata.user_id, but not n, presence_penalty, frequency_penalty, logprobs, top_logprobs)
User metadata
Function/tool calling with parallel execution control
Stop sequences

Gemini

The package includes a Gemini adapter that converts OpenAI-style requests to Gemini's format, allowing you to use the same code structure across providers.

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@1";
import { gemini } from "https://cdn.jsdelivr.net/npm/asyncllm@1/dist/gemini.js";

// You can use the gemini() adapter to convert OpenAI-style requests to Gemini's format.
const body = gemini({
  // Same as OpenAI example above
});

// Or you can use the asyncLLM() function directly with the Anthropic API endpoint.
const body = {
  contents: [{ role: "user", parts: [{ text: "What is 2+2?" }] }],
};

for await (const { content } of asyncLLM(
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-8b:streamGenerateContent?alt=sse",
  {
    method: "POST",
    headers: { "Content-Type": "application/json", Authorization: `Bearer ${apiKey}` },
    body: JSON.stringify(body),
  }
)) {
  console.log(content);
}

The Gemini adapter supports:

System messages
Multi-modal content (text, images, audio via URL or data URI)
Model parameters (temperature, max_tokens, top_p, stop, n, presence_penalty, frequency_penalty, logprobs, top_logprobs, but not metadata)
Function calling (no parallel execution support)
JSON mode and schema validation
Stop sequences
Multiple candidates

Function Calling

asyncLLM supports function calling (aka tools). Here's an example with OpenAI:

for await (const { content, tool, args, error } of asyncLLM("https://api.openai.com/v1/chat/completions", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Authorization: `Bearer ${apiKey}`,
  },
  body: JSON.stringify({
    model: "gpt-4",
    stream: true,
    messages: [
      { role: "system", content: "Call get_delivery_date with the order ID." },
      { role: "user", content: "123456" },
    ],
    tools: [
      {
        type: "function",
        function: {
          name: "get_delivery_date",
          description: "Get the delivery date for a customer order.",
          parameters: {
            type: "object",
            properties: { order_id: { type: "string", description: "The customer order ID." } },
            required: ["order_id"],
          },
        },
      },
    ],
  }),
})) {
  console.log(error ?? content, tool, args);
}

The tool and args properties are incrementally streamed until the LLM calls a tool.

Error handling

If an error occurs, it will be yielded in the error property. For example:

for await (const { content, error } of asyncLLM("https://api.openai.com/v1/chat/completions", {
  method: "POST",
  // ...
})) {
  if (error) console.error(error);
  else console.log(content);
}

Changelog

1.1.2: Corrected Gemini docs to use gemini() instead of anthropic()
1.1.1: Added Anthropic adapter
1.1.0: Added Gemini adapter
1.0.0: Initial release with asyncLLM and LLMEvent

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.