!function(){function t(t){document.documentElement.setAttribute("data-theme",t)}var e=function(){var t=null;try{t=new URLSearchParams(window.location.search).get("docusaurus-theme")}catch(t){}return t}()||function(){var t=null;try{t=localStorage.getItem("theme")}catch(t){}return t}();t(null!==e?e:"light")}(),document.documentElement.setAttribute("data-announcement-bar-initially-dismissed",function(){try{return"true"===localStorage.getItem("docusaurus.announcement.dismiss")}catch(t){}return!1}())

[Skip to main content](#__docusaurus_skipToContent_fallback)

LangChain v0.2 is coming soon! Preview the new docs [here](/v0.2/docs/introduction/).

[

![🦜️🔗 Langchain](/v0.1/img/brand/wordmark.png)![🦜️🔗 Langchain](/v0.1/img/brand/wordmark-dark.png)

](/v0.1/)[Docs](/v0.1/docs/get_started/introduction/)[Use cases](/v0.1/docs/use_cases/)[Integrations](/v0.1/docs/integrations/platforms/)[API Reference](https://api.js.langchain.com)

[More](#)

*   [People](/v0.1/docs/people/)
*   [Community](/v0.1/docs/community/)
*   [Tutorials](/v0.1/docs/additional_resources/tutorials/)
*   [Contributing](/v0.1/docs/contributing/)

[v0.1](#)

*   [v0.2](https://js.langchain.com/v0.2/docs/introduction)
*   [v0.1](/v0.1/docs/get_started/introduction/)

[🦜🔗](#)

*   [LangSmith](https://smith.langchain.com)
*   [LangSmith Docs](https://docs.smith.langchain.com)
*   [LangChain Hub](https://smith.langchain.com/hub)
*   [LangServe](https://github.com/langchain-ai/langserve)
*   [Python Docs](https://python.langchain.com/)

[Chat](https://chatjs.langchain.com)[](https://github.com/langchain-ai/langchainjs)

Search

*   [Providers](/v0.1/docs/integrations/platforms/)
    
    *   [Providers](/v0.1/docs/integrations/platforms/)
    *   [Anthropic](/v0.1/docs/integrations/platforms/anthropic/)
    *   [AWS](/v0.1/docs/integrations/platforms/aws/)
    *   [Google](/v0.1/docs/integrations/platforms/google/)
    *   [Microsoft](/v0.1/docs/integrations/platforms/microsoft/)
    *   [OpenAI](/v0.1/docs/integrations/platforms/openai/)
*   [Components](/v0.1/docs/integrations/components/)
    
    *   [LLMs](/v0.1/docs/integrations/llms/)
        
        *   [LLMs](/v0.1/docs/integrations/llms/)
        *   [AI21](/v0.1/docs/integrations/llms/ai21/)
        *   [AlephAlpha](/v0.1/docs/integrations/llms/aleph_alpha/)
        *   [AWS SageMakerEndpoint](/v0.1/docs/integrations/llms/aws_sagemaker/)
        *   [Azure OpenAI](/v0.1/docs/integrations/llms/azure/)
        *   [Bedrock](/v0.1/docs/integrations/llms/bedrock/)
        *   [Cloudflare Workers AI](/v0.1/docs/integrations/llms/cloudflare_workersai/)
        *   [Cohere](/v0.1/docs/integrations/llms/cohere/)
        *   [Fake LLM](/v0.1/docs/integrations/llms/fake/)
        *   [Fireworks](/v0.1/docs/integrations/llms/fireworks/)
        *   [Friendli](/v0.1/docs/integrations/llms/friendli/)
        *   [(Legacy) Google PaLM/VertexAI](/v0.1/docs/integrations/llms/google_palm/)
        *   [Google Vertex AI](/v0.1/docs/integrations/llms/google_vertex_ai/)
        *   [Gradient AI](/v0.1/docs/integrations/llms/gradient_ai/)
        *   [HuggingFaceInference](/v0.1/docs/integrations/llms/huggingface_inference/)
        *   [Llama CPP](/v0.1/docs/integrations/llms/llama_cpp/)
        *   [NIBittensor](/v0.1/docs/integrations/llms/ni_bittensor/)
        *   [Ollama](/v0.1/docs/integrations/llms/ollama/)
        *   [OpenAI](/v0.1/docs/integrations/llms/openai/)
        *   [PromptLayer OpenAI](/v0.1/docs/integrations/llms/prompt_layer_openai/)
        *   [RaycastAI](/v0.1/docs/integrations/llms/raycast/)
        *   [Replicate](/v0.1/docs/integrations/llms/replicate/)
        *   [Together AI](/v0.1/docs/integrations/llms/togetherai/)
        *   [WatsonX AI](/v0.1/docs/integrations/llms/watsonx_ai/)
        *   [Writer](/v0.1/docs/integrations/llms/writer/)
        *   [YandexGPT](/v0.1/docs/integrations/llms/yandex/)
    *   [Chat models](/v0.1/docs/integrations/chat/)
        
    *   [Document loaders](/v0.1/docs/integrations/document_loaders/)
        
    *   [Document transformers](/v0.1/docs/integrations/document_transformers/)
        
    *   [Document compressors](/v0.1/docs/integrations/document_compressors/)
        
    *   [Text embedding models](/v0.1/docs/integrations/text_embedding/)
        
    *   [Vector stores](/v0.1/docs/integrations/vectorstores/)
        
    *   [Retrievers](/v0.1/docs/integrations/retrievers/)
        
    *   [Tools](/v0.1/docs/integrations/tools/)
        
    *   [Agents and toolkits](/v0.1/docs/integrations/toolkits/)
        
    *   [Chat Memory](/v0.1/docs/integrations/chat_memory/)
        
    *   [Stores](/v0.1/docs/integrations/stores/)
        

*   [](/v0.1/)
*   [Components](/v0.1/docs/integrations/components/)
*   [LLMs](/v0.1/docs/integrations/llms/)
*   Fake LLM

On this page

Fake LLM
========

LangChain provides a fake LLM 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.

Usage[​](#usage "Direct link to Usage")
---------------------------------------

    import { FakeListLLM } from "langchain/llms/fake";/** * The FakeListLLM can be used to simulate ordered predefined responses. */const llm = new FakeListLLM({  responses: ["I'll callback later.", "You 'console' them!"],});const firstResponse = await llm.invoke("You want to hear a JavasSript joke?");const secondResponse = await llm.invoke(  "How do you cheer up a JavaScript developer?");console.log({ firstResponse });console.log({ secondResponse });/** * The FakeListLLM can also be used to simulate streamed responses. */const stream = await llm.stream("You want to hear a JavasSript joke?");const chunks = [];for await (const chunk of stream) {  chunks.push(chunk);}console.log(chunks.join(""));/** * The FakeListLLM can also be used to simulate delays in either either synchronous or streamed responses. */const slowLLM = new FakeListLLM({  responses: ["Because Oct 31 equals Dec 25", "You 'console' them!"],  sleep: 1000,});const slowResponse = await slowLLM.invoke(  "Why do programmers always mix up Halloween and Christmas?");console.log({ slowResponse });const slowStream = await slowLLM.stream(  "How do you cheer up a JavaScript developer?");const slowChunks = [];for await (const chunk of slowStream) {  slowChunks.push(chunk);}console.log(slowChunks.join(""));

#### API Reference:

*   [FakeListLLM](https://api.js.langchain.com/classes/langchain_llms_fake.FakeListLLM.html) from `langchain/llms/fake`

* * *

#### Help us out by providing feedback on this documentation page:

[

Previous

Cohere

](/v0.1/docs/integrations/llms/cohere/)[

Next

Fireworks

](/v0.1/docs/integrations/llms/fireworks/)

*   [Usage](#usage)

Community

*   [Discord](https://discord.gg/cU2adEyC7w)
*   [Twitter](https://twitter.com/LangChainAI)

GitHub

*   [Python](https://github.com/langchain-ai/langchain)
*   [JS/TS](https://github.com/langchain-ai/langchainjs)

More

*   [Homepage](https://langchain.com)
*   [Blog](https://blog.langchain.dev)

Copyright © 2024 LangChain, Inc.