Where do I put my Monetzly API key in a LangChain app?

In an environment variable (e.g. MONETZLY_API_KEY) read at runtime — never hard-coded in source or shipped to the client.

Do I open a new connection per request in LangChain?

Open a connection when a request begins and close it when the response stream finishes. The SDK manages reconnection internally.

How-to guide

How to Set Up the Monetzly SDK in LangChain

This guide shows how to set up the Monetzly SDK in a LangChain app, the goal being to get the SDK configured and connected before injecting any ads. It builds on the Monetzly server SDK, so the approach is specific to how LangChain produces and streams responses.

Overview

Before you can monetize a LangChain app, the Monetzly client needs configuring and connecting. This is a one-time setup: an API key, the ad-server address, and SSL settings, then a connection you open when a request starts and close when it ends.

Keep the API key in an environment variable, never in source. Use a non-SSL localhost address in development and your real ad-server host in production.

LangChain apps are usually built for RAG chatbots, tool-using agents, multi-step reasoning pipelines, and document QA, so set up the Monetzly SDK typically comes up while a user is mid-conversation, the moment where monetization has to be additive rather than disruptive.

How this works on LangChain

Grounded in this repo (src/app/api/agents/chat/route.ts) and the SDK's own LangChain example. A LangChain.js chat model returns a stream of message chunks that already carry a `content` field, so it can be passed straight into sdk.inject() with no adapter.

Because Monetzly's inject() accepts any async token stream, the LangChain side of this task is just mapping your output to it, no rewrite of your LangChain model call.

Steps

Install the SDK (or, for a Python LangChain stack, generate gRPC stubs from the shipped tps_alter.proto).
Create a config with your API key, server address, and SSL flag.
Open a connection at the start of a request and close it when the response finishes.

Integration snippet

import { MonetzlySDK, MonetzlyConfig } from "@monetzly/server-sdk";
import { ChatGoogleGenerativeAI } from "@langchain/google-genai";

const config: MonetzlyConfig = {
  apiKey: process.env.MONETZLY_API_KEY!,
  serverAddress: process.env.MONETZLY_SERVER_ADDRESS || "localhost:8080",
  useSSL: !process.env.MONETZLY_SERVER_ADDRESS?.startsWith("localhost"),
};
const sdk = new MonetzlySDK(config);
const llm = new ChatGoogleGenerativeAI({ model: "gemini-2.0-flash" });

await sdk.connect();
const llmStream = await llm.stream(messages); // chunks already have .content

for await (const token of sdk.inject(llmStream, {
  sessionId,
  prompt: messages[messages.length - 1]?.content ?? "",
})) {
  send(token.content ?? ""); // forward to client (SSE / WebSocket)
}
await sdk.disconnect();