Package Exports

asyncllm
asyncllm/anthropic
asyncllm/gemini

Readme

asyncLLM

Fetch LLM responses across multiple providers as an async iterable.

🚀 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

To use locally, install via npm:

npm install asyncllm

... and add this to your script:

import { asyncLLM } from "./node_modules/asyncllm/dist/asyncllm.js";

To use via CDN, add this to your script:

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

Usage

Streaming

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

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

The result is an async generator that yields objects with content, error, tools, and message properties.

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

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";

const body = {
  model: "gpt-4.1-nano",
  // You MUST enable streaming, else the API will return an {error}
  stream: true,
  messages: [{ role: "user", content: "Hello, world!" }],
};

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

This will log something like this on the console:

{ content: "", message: { "id": "chatcmpl-...", ...} }
{ content: "Hello", message: { "id": "chatcmpl-...", ...} }
{ content: "Hello!", message: { "id": "chatcmpl-...", ...} }
{ content: "Hello! How", message: { "id": "chatcmpl-...", ...} }
...
{ content: "Hello! How can I assist you today?", message: { "id": "chatcmpl-...", ...} }

Anthropic and Gemini Adapters

Adapters convert OpenAI chat completions request bodies to the Anthropic or Gemini formats. For example:

import { anthropic } from "https://cdn.jsdelivr.net/npm/asyncllm@2/dist/anthropic.js";
import { gemini } from "https://cdn.jsdelivr.net/npm/asyncllm@2/dist/gemini.js";

// Create an OpenAI chat completions request
const body = {
  messages: [{ role: "user", content: "Hello, world!" }],
  temperature: 0.5,
};

// Fetch request with the Anthropic API
const anthropicResponse = await fetch("https://api.anthropic.com/v1/messages", {
  method: "POST",
  headers: { "Content-Type": "application/json", "x-api-key": "YOUR_API_KEY" },
  // anthropic() converts the OpenAI chat completions request to Anthropic's format
  body: JSON.stringify(anthropic({ ...body, model: "claude-3-haiku-20240307" })),
}).then((r) => r.json());

// Fetch request with the Gemini API
const geminiResponse = await fetch(
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-1.5-flash-8b:generateContent",
  {
    method: "POST",
    headers: { "Content-Type": "application/json", Authorization: `Bearer YOUR_API_KEY` },
    // gemini() converts the OpenAI chat completions request to Gemini's format
    body: JSON.stringify(gemini(body)),
  },
).then((r) => r.json());

Here are the parameters supported by each provider.

OpenAI Parameter	Anthropic	Gemini
messages	Y	Y
system message	Y	Y
temperature	Y	Y
max_tokens	Y	Y
top_p	Y	Y
stop sequences	Y	Y
stream	Y	Y
presence_penalty		Y
frequency_penalty		Y
logprobs		Y
top_logprobs		Y
n (multiple candidates)		Y
metadata.user_id	Y
tools/functions	Y	Y
tool_choice	Y	Y
parallel_tool_calls	Y
response_format.type: "json_object"		Y
response_format.type: "json_schema"		Y

Content types:

OpenAI	Anthropic	Gemini
Text	Y	Y
Images	Y	Y
Audio		Y

Image Sources

OpenAI Parameter	Anthropic	Gemini
Data URI	Y	Y
External URLs		Y

OpenAI Responses API streaming

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";

const body = {
  model: "gpt-4.1-mini",
  // You MUST enable streaming, else the API will return an {error}
  stream: true,
  input: "Hello, world!",
};

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

This will log something like this on the console:

{ content: "Hello", message: { "item_id": "msg_...", ...} }
{ content: "Hello!", message: { "item_id": "msg_...", ...} }
{ content: "Hello! How", message: { "item_id": "msg_...", ...} }
...
{ content: "Hello! How can I assist you today?", message: { "item_id": "msg_...", ...} }

Anthropic streaming

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

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";
import { anthropic } from "https://cdn.jsdelivr.net/npm/asyncllm@2/dist/anthropic.js";

// You can use the anthropic() adapter to convert OpenAI chat completions 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",
  // You MUST enable streaming, else the API will return an {error}
  stream: true,
  max_tokens: 10,
  messages: [{ role: "user", content: "What is 2 + 2" }],
};

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

Gemini streaming

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

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";
import { gemini } from "https://cdn.jsdelivr.net/npm/asyncllm@2/dist/gemini.js";

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

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

for await (const data of asyncLLM(
  // You MUST use a streaming endpoint, else the API will return an {error}
  "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(data);
}

Function Calling

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

for await (const { tools } of asyncLLM("https://api.openai.com/v1/chat/completions", {
  method: "POST",
  headers: {
    "Content-Type": "application/json",
    Authorization: `Bearer ${apiKey}`,
  },
  body: JSON.stringify({
    model: "gpt-4.1-nano",
    stream: true,
    messages: [
      { role: "system", content: "Get delivery date for order" },
      { role: "user", content: "Order ID: 123456" },
    ],
    tool_choice: "required",
    tools: [
      {
        type: "function",
        function: {
          name: "get_delivery_date",
          parameters: { type: "object", properties: { order_id: { type: "string" } }, required: ["order_id"] },
        },
      },
    ],
  }),
})) {
  console.log(JSON.stringify(tools));
}

tools is an array of objects with name, id (for Anthropic and OpenAI, not Gemini), and args properties. It streams like this:

[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":""}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\""}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order"}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order_id"}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order_id\":\""}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order_id\":\"123"}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order_id\":\"123456"}]
[{"name":"get_delivery_date","id":"call_F8YHCjnzrrTjfE4YSSpVW2Bc","args":"{\"order_id\":\"123456\"}"}]

Use a library like partial-json to parse the args incrementally.

Streaming Config

asyncLLM accepts a config object with the following properties:

fetch: Custom fetch implementation (defaults to global fetch).
onResponse: Async callback function that receives the Response object before streaming begins. If the callback returns a promise, it will be awaited before continuing the stream.

Here's how you can use a custom fetch implementation:

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";

const body = {
  // Same as OpenAI example above
};

// Optional configuration. You can ignore it for most use cases.
const config = {
  onResponse: async (response) => {
    console.log(response.status, response.headers);
  },
  // You can use a custom fetch implementation if needed
  fetch: fetch,
};

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),
  },
  config,
)) {
  console.log(content);
}

Streaming from text

You can parse streamed SSE events from a text string (e.g. from a cached response) using the provided fetchText helper:

import { asyncLLM } from "https://cdn.jsdelivr.net/npm/asyncllm@2";
import { fetchText } from "https://cdn.jsdelivr.net/npm/asyncsse@1/dist/fetchtext.js";

const text = `
data: {"candidates": [{"content": {"parts": [{"text": "2"}],"role": "model"}}]}

data: {"candidates": [{"content": {"parts": [{"text": " + 2 = 4\\n"}],"role": "model"}}]}

data: {"candidates": [{"content": {"parts": [{"text": ""}],"role": "model"}}]}
`;

// Stream events from text
for await (const event of asyncLLM(text, {}, { fetch: fetchText })) {
  console.log(event);
}

This outputs:

{ data: "Hello" }
{ data: "World" }

This is particularly useful for testing SSE parsing without making actual HTTP requests.

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);
}

The error property is set if:

The underlying API (e.g. OpenAI, Anthropic, Gemini) returns an error in the response (e.g. error.message or message.error or error)
The fetch request fails (e.g. network error)
The response body cannot be parsed as JSON

API

`asyncLLM(request: string | Request, options?: RequestInit, config?: SSEConfig): 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
config: Optional configuration object for SSE handling
- fetch: Custom fetch implementation (defaults to global fetch)
- onResponse: Async callback function that receives the Response object before streaming begins. If the callback returns a promise, it will be awaited before continuing the stream.

Returns an async generator that yields LLMEvent objects.

LLMEvent

content: The text content of the response
tools: Array of tool call objects with:
- name: The name of the tool being called
- args: The arguments for the tool call as a JSON-encoded string, e.g. {"order_id":"123456"}
- id: Optional unique identifier for the tool call (e.g. OpenAI's call_F8YHCjnzrrTjfE4YSSpVW2Bc or Anthropic's toolu_01T1x1fJ34qAmk2tNTrN7Up6. Gemini does not return an id.)
message: The raw message object from the LLM provider (may include id, model, usage stats, etc.)
error: Error message if the request fails

Node.js usage

import { asyncLLM } from "asyncllm";

// Rest of the usage is the same as in the browser examples

Development

git clone https://github.com/sanand0/asyncllm.git
cd asyncllm

npm install
npm run lint && npm run build && npm test

npm publish
git commit . -m"$COMMIT_MSG"; git tag $VERSION; git push --follow-tags

Release notes

2.3.0: 30 Jul 2025: Standardized package.json & README.md, renamed index.js to asyncllm.js
2.2.0: 23 Apr 2025. Added OpenAI Responses API
2.1.2: 25 Dec 2024. Update repo links
2.1.1: 9 Nov 2024. Document standalone adapter usage
2.1.0: 7 Nov 2024. Added id to tools to support unique tool call identifiers from providers
2.0.1: 5 Nov 2024. Multiple tools support. Breaking change: tool and args are not part of the response. Instead, it has tools, an array of { name, args }. Gemini adapter returns toolConfig instead of toolsConfig
1.2.2: 3 Nov 2024. Added streaming from text documentation via config.fetch. Upgrade to asyncSSE 1.3.1 (bug fix).
1.2.1: 3 Nov 2024. Added config.fetch for custom fetch implementation
1.2.0: 2 Nov 2024. Added config.onResponse(response) that receives the Response object before streaming begins
1.1.3: 2 Nov 2024. Ensure max_tokens for Anthropic. Improve error handling
1.1.1: 30 Oct 2024. Added Anthropic adapter
1.1.0: 30 Oct 2024. Added Gemini adapter
1.0.0: 15 Oct 2024. Initial release with asyncLLM and LLMEvent

License

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