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Fake LLM

LangChain provides a fake LLM chat model for testing purposes. This allows you to mock out calls to the LLM and and simulate what would happen if the LLM responded in a certain way.


import { FakeListChatModel } from "@langchain/core/utils/testing";
import { HumanMessage } from "@langchain/core/messages";
import { StringOutputParser } from "@langchain/core/output_parsers";

* The FakeListChatModel can be used to simulate ordered predefined responses.

const chat = new FakeListChatModel({
responses: ["I'll callback later.", "You 'console' them!"],

const firstMessage = new HumanMessage("You want to hear a JavasSript joke?");
const secondMessage = new HumanMessage(
"How do you cheer up a JavaScript developer?"
const firstResponse = await chat.invoke([firstMessage]);
const secondResponse = await chat.invoke([secondMessage]);

console.log({ firstResponse });
console.log({ secondResponse });

* The FakeListChatModel can also be used to simulate streamed responses.

const stream = await chat
.pipe(new StringOutputParser())
.stream(`You want to hear a JavasSript joke?`);
const chunks = [];
for await (const chunk of stream) {


* The FakeListChatModel can also be used to simulate delays in either either synchronous or streamed responses.

const slowChat = new FakeListChatModel({
responses: ["Because Oct 31 equals Dec 25", "You 'console' them!"],
sleep: 1000,

const thirdMessage = new HumanMessage(
"Why do programmers always mix up Halloween and Christmas?"
const slowResponse = await slowChat.invoke([thirdMessage]);
console.log({ slowResponse });

const slowStream = await slowChat
.pipe(new StringOutputParser())
.stream("How do you cheer up a JavaScript developer?");
const slowChunks = [];
for await (const chunk of slowStream) {


API Reference:

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