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https://github.com/langchain-ai/langchain-nextjs-template

LangChain + Next.js starter template
https://github.com/langchain-ai/langchain-nextjs-template

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LangChain + Next.js starter template

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# 🦜️🔗 LangChain + Next.js Starter Template



[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain-nextjs-template)

[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flangchain-ai%2Flangchain-nextjs-template)



This template scaffolds a LangChain.js + Next.js starter app. It showcases how to use and combine LangChain modules for several

use cases. Specifically:



- [Simple chat](/app/api/chat/route.ts)

- [Returning structured output from an LLM call](/app/api/chat/structured_output/route.ts)

- [Answering complex, multi-step questions with agents](/app/api/chat/agents/route.ts)

- [Retrieval augmented generation (RAG) with a chain and a vector store](/app/api/chat/retrieval/route.ts)

- [Retrieval augmented generation (RAG) with an agent and a vector store](/app/api/chat/retrieval_agents/route.ts)



Most of them use Vercel's [AI SDK](https://github.com/vercel-labs/ai) to stream tokens to the client and display the incoming messages.



The agents use [LangGraph.js](https://langchain-ai.github.io/langgraphjs/), LangChain's framework for building agentic workflows. They use preconfigured helper functions to minimize boilerplate, but you can replace them with custom graphs as desired.



![Demo GIF](/public/images/agent-convo.gif)



It's free-tier friendly too! Check out the [bundle size stats below](#-bundle-size).



You can check out a hosted version of this repo here: https://langchain-nextjs-template.vercel.app/



## 🚀 Getting Started



First, clone this repo and download it locally.



Next, you'll need to set up environment variables in your repo's `.env.local` file. Copy the `.env.example` file to `.env.local`.

To start with the basic examples, you'll just need to add your OpenAI API key.



Next, install the required packages using your preferred package manager (e.g. `yarn`).



Now you're ready to run the development server:



```bash

yarn dev

```



Open [http://localhost:3000](http://localhost:3000) with your browser to see the result! Ask the bot something and you'll see a streamed response:



![A streaming conversation between the user and the AI](/public/images/chat-conversation.png)



You can start editing the page by modifying `app/page.tsx`. The page auto-updates as you edit the file.



Backend logic lives in `app/api/chat/route.ts`. From here, you can change the prompt and model, or add other modules and logic.



## 🧱 Structured Output



The second example shows how to have a model return output according to a specific schema using OpenAI Functions.

Click the `Structured Output` link in the navbar to try it out:



![A streaming conversation between the user and an AI agent](/public/images/structured-output-conversation.png)



The chain in this example uses a [popular library called Zod](https://zod.dev) to construct a schema, then formats it in the way OpenAI expects.

It then passes that schema as a function into OpenAI and passes a `function_call` parameter to force OpenAI to return arguments in the specified format.



For more details, [check out this documentation page](https://js.langchain.com/v0.2/docs/how_to/structured_output).



## 🦜 Agents



To try out the agent example, you'll need to give the agent access to the internet by populating the `SERPAPI_API_KEY` in `.env.local`.

Head over to [the SERP API website](https://serpapi.com/) and get an API key if you don't already have one.



You can then click the `Agent` example and try asking it more complex questions:



![A streaming conversation between the user and an AI agent](/public/images/agent-conversation.png)



This example uses a [prebuilt LangGraph agent](https://langchain-ai.github.io/langgraphjs/tutorials/quickstart/), but you can customize your own as well.



## 🐶 Retrieval



The retrieval examples both use Supabase as a vector store. However, you can swap in

[another supported vector store](https://js.langchain.com/v0.2/docs/integrations/vectorstores) if preferred by changing

the code under `app/api/retrieval/ingest/route.ts`, `app/api/chat/retrieval/route.ts`, and `app/api/chat/retrieval_agents/route.ts`.



For Supabase, follow [these instructions](https://js.langchain.com/v0.2/docs/integrations/vectorstores/supabase) to set up your

database, then get your database URL and private key and paste them into `.env.local`.



You can then switch to the `Retrieval` and `Retrieval Agent` examples. The default document text is pulled from the LangChain.js retrieval

use case docs, but you can change them to whatever text you'd like.



For a given text, you'll only need to press `Upload` once. Pressing it again will re-ingest the docs, resulting in duplicates.

You can clear your Supabase vector store by navigating to the console and running `DELETE FROM documents;`.



After splitting, embedding, and uploading some text, you're ready to ask questions!



![A streaming conversation between the user and an AI retrieval chain](/public/images/retrieval-chain-conversation.png)



![A streaming conversation between the user and an AI retrieval agent](/public/images/retrieval-agent-conversation.png)



For more info on retrieval chains, [see this page](https://js.langchain.com/v0.2/docs/tutorials/rag).

The specific variant of the conversational retrieval chain used here is composed using LangChain Expression Language, which you can

[read more about here](https://js.langchain.com/v0.2/docs/how_to/qa_sources/). This chain example will also return cited sources

via header in addition to the streaming response.



For more info on retrieval agents, [see this page](https://langchain-ai.github.io/langgraphjs/tutorials/rag/langgraph_agentic_rag/).



## 📦 Bundle size



The bundle size for LangChain itself is quite small. After compression and chunk splitting, for the RAG use case LangChain uses 37.32 KB of code space (as of [@langchain/core 0.1.15](https://npmjs.com/package/@langchain/core)), which is less than 4% of the total Vercel free tier edge function alottment of 1 MB:



![](/public/images/bundle-size.png)



This package has [@next/bundle-analyzer](https://www.npmjs.com/package/@next/bundle-analyzer) set up by default - you can explore the bundle size interactively by running:



```bash

$ ANALYZE=true yarn build

```



## 📚 Learn More



The example chains in the `app/api/chat/route.ts` and `app/api/chat/retrieval/route.ts` files use

[LangChain Expression Language](https://js.langchain.com/v0.2/docs/concepts#langchain-expression-language) to

compose different LangChain.js modules together. You can integrate other retrievers, agents, preconfigured chains, and more too, though keep in mind

`HttpResponseOutputParser` is meant to be used directly with model output.



To learn more about what you can do with LangChain.js, check out the docs here:



- https://js.langchain.com/docs/



## ▲ Deploy on Vercel



When ready, you can deploy your app on the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme).



Check out the [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.



## Thank You!



Thanks for reading! If you have any questions or comments, reach out to us on Twitter

[@LangChainAI](https://twitter.com/langchainai), or [click here to join our Discord server](https://discord.gg/langchain).