emergentintegrations

Node.js client for Emergent's LLM proxy and platform integrations. Chat with OpenAI, Anthropic, and Google models through one Emergent key, no manual proxy wiring, plus image generation, video generation, text-to-speech, speech-to-text, realtime audio, and Stripe payments.

// Same code, any provider — just change the model. Requires an Emergent API key.
chat.withModel("openai", "gpt-4o")
chat.withModel("anthropic", "claude-sonnet-4-6")
chat.withModel("gemini", "gemini-1.5-pro")

Key requirements are not the same for every feature. LlmChat requires an Emergent API key (sk-emergent-*) and always routes through Emergent's proxy — it does not accept direct OpenAI, Anthropic, or Google keys. OpenAITextToSpeech, OpenAISpeechToText, and OpenAIImageGeneration accept either an Emergent key or a direct OpenAI key. GeminiImageGeneration always talks directly to Google and requires a real Google API key. GeminiVideoGeneration always routes through the Emergent proxy. OpenAIChatRealtime always talks directly to OpenAI and requires a real OpenAI key, there is no proxy path for realtime audio. This isn't a Node-specific limitation, it mirrors the official Python package's actual per-module design exactly.

Framework agnostic — works with Express, Fastify, Hono, plain Node, serverless, anywhere
Dual ESM + CommonJS — native import and require, no workarounds
AI-agent ready — ships rule files (CLAUDE.md, AGENTS.md, .cursorrules) that stop coding agents from bypassing the package or assuming key requirements that don't hold
Verified against the official Python package — audited file by file, not assumed

Install

npm install emergentintegrations

Or install directly from GitHub:

npm install github:Hill-TreX/emergentintegrations

If you use Stripe payments, also install:

npm install stripe

Works natively in both CommonJS and ESM projects:

// CommonJS
const { LlmChat, UserMessage } = require("emergentintegrations");

// ESM — no createRequire workaround needed
import { LlmChat, UserMessage } from "emergentintegrations";

Set up AI agent rules (optional but recommended)

If you use an AI coding agent (Cursor, Windsurf, Emergent, Copilot), run this once after installing to copy the rule files to your project root:

npx emergentintegrations init

This copies CLAUDE.md, AGENTS.md, and .cursorrules into your project — nothing is changed automatically. Your agent will then follow the correct usage rules and never reach for the raw OpenAI SDK. Commit these files so your whole team benefits.

File	Covers
`CLAUDE.md`	Claude Code, claude.ai
`AGENTS.md`	Codex, general agents
`.cursorrules`	Cursor, Windsurf, Zed

Quick start

const { LlmChat, UserMessage } = require("emergentintegrations");

const chat = new LlmChat("sk-emergent-...", "session-123", "You are a helpful assistant.")
  .withModel("openai", "gpt-4o-mini")
  .withParams({ temperature: 0.3 });

const reply = await chat.sendMessage(new UserMessage({ text: "Hello!" }));
console.log(reply);

How routing works

LlmChat requires an Emergent API key. Every call routes through https://integrations.emergentagent.com/llm, which handles OpenAI, Anthropic, and Google on the backend. A key that doesn't start with sk-emergent- throws ChatError immediately at construction, not partway through a request.

INTEGRATION_PROXY_URL env var overrides the default proxy endpoint
APP_URL or REACT_APP_BACKEND_URL is forwarded as X-App-ID on every request

OpenAITextToSpeech, OpenAISpeechToText, and OpenAIImageGeneration are more permissive: they accept either an Emergent key or a real OpenAI key, and route accordingly. See each class's section below for its actual key requirement, they are not all the same.

LlmChat

const { LlmChat, UserMessage, ImageContent, FileContentWithMimeType, ChatError } = require("emergentintegrations");

Constructor

new LlmChat(apiKey, sessionId, systemMessage, initialMessages?, customHeaders?)

Param	Type	Description
`apiKey`	`string`	Must be an Emergent API key, starts with `sk-emergent-`. Throws `ChatError` immediately if not.
`sessionId`	`string`	Session identifier
`systemMessage`	`string`	System prompt
`initialMessages`	`array?`	Seed conversation history (replaces system message)
`customHeaders`	`object?`	Extra HTTP headers sent on every request

Builder methods (chainable)

chat.withModel("openai", "gpt-4o-mini")
chat.withModel("anthropic", "claude-sonnet-4-6")
chat.withModel("gemini", "gemini-1.5-pro")
chat.withParams({ temperature: 0.3, max_tokens: 512 })
chat.withTools(tools, toolChoice?)            // attach function tools

Sending messages

// Simple text reply
const text = await chat.sendMessage(new UserMessage({ text: "Hello" }));

// With image
const text = await chat.sendMessage(new UserMessage({
  text: "What's in this image?",
  file_contents: [new ImageContent(base64String)],
}));

// Multimodal response (text + generated images)
const [text, images] = await chat.sendMessageMultimodalResponse(userMessage);
// images: [{ mime_type: "image/png", data: "<base64>" }]

// Tool-aware — returns ChatResponse with tool_calls
const response = await chat.sendMessageWithTools(userMessage);
// response.content, response.tool_calls, response.finish_reason, response.usage

// Stream — yields typed events
for await (const event of chat.streamMessage(userMessage)) {
  if (event.type === "text_delta") process.stdout.write(event.content);
  if (event.type === "tool_call_start") console.log("Tool called:", event.name);
  if (event.type === "tool_call_ready") console.log("Args:", event.tool_call.arguments);
  if (event.type === "stream_done") console.log("Done:", event.content);
}

// Get history
const messages = await chat.getMessages();
// chat.messages also works directly

Tool use loop

chat.withTools([{
  type: "function",
  function: { name: "get_weather", description: "...", parameters: { ... } }
}]);

let response = await chat.sendMessageWithTools(new UserMessage({ text: "What's the weather?" }));

while (response.tool_calls) {
  for (const tc of response.tool_calls) {
    const result = await myGetWeather(tc.arguments);
    chat.addToolResult(tc.id, JSON.stringify(result));
  }
  response = await chat.sendMessageWithTools();
}
console.log(response.content);

Message types

new UserMessage({ text: "Hello" })
new UserMessage({ text: "What's this?", file_contents: [new ImageContent(base64)] })
new UserMessage()  // empty, for tool-result continuation turns

new ImageContent(base64String)           // auto-detects PNG/JPEG/GIF/WEBP
ImageContent.getMimeType(base64String)   // static helper

new FileContentWithMimeType("application/pdf", "./doc.pdf")  // reads from disk

OpenAI extras

const { OpenAITextToSpeech, OpenAISpeechToText, OpenAIImageGeneration,
        OpenAIVideoGeneration, OpenAIChatRealtime } = require("emergentintegrations");

// Text-to-Speech
const tts = new OpenAITextToSpeech(apiKey);
const audioBytes = await tts.generateSpeech("Hello world", "tts-1", "alloy");
const base64Audio = await tts.generateSpeechBase64("Hello world");

// Speech-to-Text
const stt = new OpenAISpeechToText(apiKey);
const result = await stt.transcribe("./audio.mp3");

// Image Generation
const imgGen = new OpenAIImageGeneration(apiKey);
const imageBuffers = await imgGen.generateImages("A sunset", "gpt-image-1", 1, "low");

// Video Generation (Sora)
const vidGen = new OpenAIVideoGeneration(apiKey);
const videoBytes = await vidGen.textToVideo("A cat playing piano", "sora-2", "1280x720", 4);

// Realtime WebRTC
const realtime = new OpenAIChatRealtime(apiKey);
const session = await realtime.createEphemeralSessionForAudioChat();
const sdpAnswer = await realtime.negotiateConnection(sdpOffer);
// Express route handlers
const handlers = OpenAIChatRealtime.createRouteHandlers(realtime);
router.post("/realtime/session", handlers.createSession);
router.post("/realtime/negotiate", handlers.negotiate);

Gemini extras

const { GeminiImageGeneration, GeminiVideoGeneration } = require("emergentintegrations");

// Image generation (requires: npm install @google/genai)
const imgGen = new GeminiImageGeneration(apiKey);
const imageBuffers = await imgGen.generateImages("A mountain lake", "imagen-3.0-generate-002", 4);

// Video generation (Veo via proxy)
const vidGen = new GeminiVideoGeneration(apiKey);
const videoBytes = await vidGen.textToVideo("A robot dancing");

Stripe payments

const { StripeCheckout, CheckoutError } = require("emergentintegrations");

const stripe = new StripeCheckout(apiKey, webhookSecret?, webhookUrl?);

// Create checkout session
const { url, session_id } = await stripe.createCheckoutSession({
  amount: 29.99,
  currency: "usd",
  success_url: "https://yoursite.com/success?session_id={CHECKOUT_SESSION_ID}",
  cancel_url: "https://yoursite.com/cancel",
  metadata: { userId: "123" },
});

// Check status
const status = await stripe.getCheckoutStatus(session_id);

// Handle webhook
const event = await stripe.handleWebhook(rawBody, stripeSignatureHeader);

Keys containing sk_test_emergent are automatically routed through the Emergent Stripe proxy.

Clean code — the right way to use this package

Never create a raw OpenAI client or set baseURL manually. The package handles all routing internally.

// ❌ WRONG — never do this
const OpenAI = require("openai");
const client = new OpenAI({
  apiKey: process.env.EMERGENT_LLM_KEY,
  baseURL: "https://integrations.emergentagent.com/llm",
});

// ✅ CORRECT — always use this
const { LlmChat, UserMessage } = require("emergentintegrations");
const chat = new LlmChat(process.env.EMERGENT_LLM_KEY, sessionId, systemMessage)
  .withModel("anthropic", "claude-sonnet-4-6");
const reply = await chat.sendMessage(new UserMessage({ text: input }));

`createChat()` — factory helper for clean setup

const { createChat, validateApiKey, UserMessage } = require("emergentintegrations");

// Validate your key at startup — throws a clear error if missing or malformed
const apiKey = validateApiKey(process.env.EMERGENT_LLM_KEY);

// Create a chat client cleanly
const chat = createChat({
  apiKey,
  sessionId: "user-123",
  systemMessage: "You are a helpful assistant.",
  provider: "anthropic",         // "openai" | "anthropic" | "gemini"
  model: "claude-sonnet-4-6",
  params: { temperature: 0.3 },  // optional
});

const reply = await chat.sendMessage(new UserMessage({ text: "Hello!" }));

`getProxyInfo()` — debug routing

const { getProxyInfo } = require("emergentintegrations");

const info = getProxyInfo(process.env.EMERGENT_LLM_KEY);
console.log(info);
// { isEmergent: true, proxyUrl: "https://integrations.emergentagent.com/llm" }

For AI agents

This repo includes AGENTS.md and .cursorrules with strict rules for AI coding agents. If you are an AI agent: read AGENTS.md before writing any LLM integration code.

Listing available models

const { listModels } = require("emergentintegrations");

// List OpenAI models (default)
const models = await listModels({ apiKey: process.env.OPENAI_API_KEY });
models.forEach(m => console.log(m.id, m.provider));

// List Anthropic models
const models = await listModels({
  apiKey: process.env.ANTHROPIC_API_KEY,
  provider: "anthropic",
});

// List Gemini models
const models = await listModels({
  apiKey: process.env.GOOGLE_API_KEY,
  provider: "gemini",
});

// List ALL providers at once via Emergent proxy
const models = await listModels({
  apiKey: process.env.EMERGENT_LLM_KEY,
  provider: "all",
});
models.forEach(m => console.log(m.provider, m.id));

Each model entry returns:

{
  id: "gpt-4o",           // model string to pass to withModel()
  provider: "openai",     // "openai" | "anthropic" | "gemini"
  created: 1715367049,    // unix timestamp (when available)
  owned_by: "openai",     // owner string (when available)
}

Migrating from Python

Python	Node.js
`LlmChat(api_key=k, session_id=s, system_message=m)`	`new LlmChat(k, s, m)`
`.with_model("openai", "gpt-4o")`	`.withModel("openai", "gpt-4o")`
`.with_params(temperature=0.3)`	`.withParams({ temperature: 0.3 })`
`.with_tools(tools)`	`.withTools(tools)`
`.add_tool_result(id, content)`	`.addToolResult(id, content)`
`await chat.send_message(UserMessage(text="hi"))`	`await chat.sendMessage(new UserMessage({ text: "hi" }))`
`await chat.send_message_with_tools(msg)`	`await chat.sendMessageWithTools(msg)`
`async for event in chat.stream_message(msg)`	`for await (const event of chat.streamMessage(msg))`
`isinstance(event, TextDelta)`	`event.type === "text_delta"`
`isinstance(event, StreamDone)`	`event.type === "stream_done"`
`await chat.send_message_multimodal_response(msg)`	`await chat.sendMessageMultimodalResponse(msg)`
`await chat.get_messages()`	`await chat.getMessages()`
`chat.messages`	`chat.messages`
`UserMessage(text="hi", file_contents=[...])`	`new UserMessage({ text: "hi", file_contents: [...] })`
`ImageContent(base64)`	`new ImageContent(base64)`
`ImageContent.get_mime_type(b64)`	`ImageContent.getMimeType(b64)`
`FileContentWithMimeType(mime, path)`	`new FileContentWithMimeType(mime, path)`

Import paths

All of the following resolve to the same classes, and every path works in both CommonJS and ESM:

// CommonJS
const { LlmChat, UserMessage } = require("emergentintegrations");
const { LlmChat } = require("emergentintegrations/llm/chat");
const { getIntegrationProxyUrl } = require("emergentintegrations/llm/utils");
const { OpenAITextToSpeech } = require("emergentintegrations/llm/openai");
const { GeminiImageGeneration } = require("emergentintegrations/llm/gemini");
const { StripeCheckout } = require("emergentintegrations/payments/stripe");

// ESM — identical paths, native import, no createRequire needed
import { LlmChat, UserMessage } from "emergentintegrations";
import { LlmChat } from "emergentintegrations/llm/chat";
import { getIntegrationProxyUrl } from "emergentintegrations/llm/utils";
import { OpenAITextToSpeech } from "emergentintegrations/llm/openai";
import { GeminiImageGeneration } from "emergentintegrations/llm/gemini";
import { StripeCheckout } from "emergentintegrations/payments/stripe";

Development

git clone https://github.com/Hill-TreX/emergentintegrations
cd emergentintegrations
npm install       # also runs tsc via prepare script
npm run build     # rebuild dist/

License

MIT

Name		Name	Last commit message	Last commit date
Latest commit History 18 Commits
bin		bin
docs		docs
scripts		scripts
src		src
.cursorrules		.cursorrules
.gitignore		.gitignore
AGENTS.md		AGENTS.md
CLAUDE.md		CLAUDE.md
README.md		README.md
package-lock.json		package-lock.json
package.json		package.json
tsconfig.esm.json		tsconfig.esm.json
tsconfig.json		tsconfig.json

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Repository files navigation

emergentintegrations

Install

Set up AI agent rules (optional but recommended)

Quick start

How routing works

LlmChat

Constructor

Builder methods (chainable)

Sending messages

Tool use loop

Message types

OpenAI extras

Gemini extras

Stripe payments

Clean code — the right way to use this package

`createChat()` — factory helper for clean setup

`getProxyInfo()` — debug routing

For AI agents

Listing available models

Migrating from Python

Import paths

Development

License

About

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Repository files navigation

emergentintegrations

Install

Set up AI agent rules (optional but recommended)

Quick start

How routing works

LlmChat

Constructor

Builder methods (chainable)

Sending messages

Tool use loop

Message types

OpenAI extras

Gemini extras

Stripe payments

Clean code — the right way to use this package

createChat() — factory helper for clean setup

getProxyInfo() — debug routing

For AI agents

Listing available models

Migrating from Python

Import paths

Development

License

About

Resources

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`createChat()` — factory helper for clean setup

`getProxyInfo()` — debug routing