How-to guide
How to Inject Ads Into a Streaming Response in LangChain
This guide shows how to inject ads into a streaming response in a LangChain app, the goal being to wrap the LLM token stream so contextual ads appear inside the assistant's reply. It builds on the Monetzly server SDK, so the approach is specific to how LangChain produces and streams responses.
Overview
This is the core of monetizing a LangChain app: instead of returning the raw model stream, you pass it through Monetzly, which injects contextual, labelled ads into the token stream and hands you back the enhanced stream to forward to the client.
You keep your existing LangChain model call untouched. Injection is additive — a wrapper around the stream you already produce, keyed on the session and the live prompt so the ad matches what the user is asking about.
LangChain apps are usually built for RAG chatbots, tool-using agents, multi-step reasoning pipelines, and document QA, so inject ads into a streaming response 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
- Produce your LangChain response as a token stream as you already do.
- Pass that stream into the Monetzly injection call with the session id and prompt.
- Forward the enhanced tokens to the client (SSE, WebSocket, or your existing transport).
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();Frequently asked questions
Start monetizing in about 5 minutes
Wrap your existing LLM response stream with the Monetzly SDK and earn on every session, no paywall required.