Can I add Monetzly to an existing LangChain app without rewriting it?

Yes. You keep your LangChain model calls as they are and pass the streaming response into Monetzly's SDK, so ad injection is additive rather than a rewrite.

Will ads hurt the LangChain chat experience?

Ads are contextual and clearly labelled, placed inside the response stream rather than as competing banners, so the conversation stays the focus.

Do free users of my LangChain app still earn revenue?

That is the point of the model: every session can earn through in-conversation ads, so you are not forced to paywall casual users to make the app viable.

How long does the LangChain integration take?

Because you are wrapping an existing stream with a documented SDK call, most teams wire it in during a single session. The SDK handles connection, injection, and fallback.

Monetize by framework

How to Monetize a LangChain AI App

LangChain is a framework for composing LLM calls, tools, memory, and agents into chains and graphs, in Python and JS/TS. Teams reach for it to build RAG chatbots, tool-using agents, multi-step reasoning pipelines, and document QA. Those are exactly the always-on, conversational experiences where a subscription wall or a banner ad tends to feel out of place, which is where a contextual, in-conversation ad model fits better.

What building with LangChain usually looks like

A typical LangChain project centers on RAG chatbots, tool-using agents, multi-step reasoning pipelines, and document QA. The interface is a conversation, and value is delivered turn by turn rather than behind a checkout.

That shape is great for users and hard for revenue: the moment you gate it behind a paywall, casual usage, the majority of traffic for most LangChain apps, drops off before it ever converts.

Why subscriptions and display ads are awkward for this stack

Subscriptions force a paying decision before the user has felt the value, and they leave every non-paying session earning nothing. Display networks (banners, sidebars) were built for static pages, not for a streaming LangChain response, they compete with the conversation for attention instead of belonging to it.

Monetzly takes the other path: relevant, clearly-marked ads are placed inside the assistant's response stream, so free users can stay free while the app still earns on every session.

How Monetzly integrates with 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.

Monetzly's server SDK consumes any async stream of tokens, so you keep your existing LangChain model call and pass its streaming output into sdk.inject(), contextual ads are injected into the token stream keyed on the session and live prompt, with no UI rework.

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();