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3b:Td58,# Chatbot Chatbot (formerly AI Chatbot) is a free, open-source template built with Next.js and the AI SDK that helps you quickly build powerful chatbot applications.

Read Docs · Features · Model Providers · Deploy Your Own · Running locally

## Features - [Next.js](https://nextjs.org) App Router - Advanced routing for seamless navigation and performance - React Server Components (RSCs) and Server Actions for server-side rendering and increased performance - [AI SDK](https://ai-sdk.dev/docs/introduction) - Unified API for generating text, structured objects, and tool calls with LLMs - Hooks for building dynamic chat and generative user interfaces - Supports OpenAI, Anthropic, Google, xAI, and other model providers via AI Gateway - [shadcn/ui](https://ui.shadcn.com) - Styling with [Tailwind CSS](https://tailwindcss.com) - Component primitives from [Radix UI](https://radix-ui.com) for accessibility and flexibility - Data Persistence - [Neon Serverless Postgres](https://vercel.com/marketplace/neon) for saving chat history and user data - [Vercel Blob](https://vercel.com/storage/blob) for efficient file storage - [Auth.js](https://authjs.dev) - Simple and secure authentication ## Model Providers This template uses the [Vercel AI Gateway](https://vercel.com/docs/ai-gateway) to access multiple AI models through a unified interface. Models are configured in `lib/ai/models.ts` with per-model provider routing. Included models: Mistral, Moonshot, DeepSeek, OpenAI, and xAI. ### AI Gateway Authentication **For Vercel deployments**: Authentication is handled automatically via OIDC tokens. **For non-Vercel deployments**: You need to provide an AI Gateway API key by setting the `AI_GATEWAY_API_KEY` environment variable in your `.env.local` file. With the [AI SDK](https://ai-sdk.dev/docs/introduction), you can also switch to direct LLM providers like [OpenAI](https://openai.com), [Anthropic](https://anthropic.com), [Cohere](https://cohere.com/), and [many more](https://ai-sdk.dev/providers/ai-sdk-providers) with just a few lines of code. ## Deploy Your Own You can deploy your own version of Chatbot to Vercel with one click: [![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/templates/next.js/chatbot) ## Running locally You will need to use the environment variables [defined in `.env.example`](.env.example) to run Chatbot. It's recommended you use [Vercel Environment Variables](https://vercel.com/docs/projects/environment-variables) for this, but a `.env` file is all that is necessary. > Note: You should not commit your `.env` file or it will expose secrets that will allow others to control access to your various AI and authentication provider accounts. 1. Install Vercel CLI: `npm i -g vercel` 2. Link local instance with Vercel and GitHub accounts (creates `.vercel` directory): `vercel link` 3. Download your environment variables: `vercel env pull` ```bash pnpm install pnpm db:migrate # Setup database or apply latest database changes pnpm dev ``` Your app template should now be running on [localhost:3000](http://localhost:3000). 3c:T866,An open-source vibe coding platform built with Next.js, AI SDK, GPT-5, and the Vercel AI Cloud. The platform combines real-time AI interactions with safe sandboxed execution environments and framework-defined infrastructure. ## Getting Started First, run the development server: npm run dev # or yarn dev # or pnpm dev # or bun dev Open http://localhost:3000 in your browser to see the result. You can start editing the page by modifying app/page.tsx. The page auto-updates as you edit the file. This project uses next/font to automatically optimize and load Geist, a new font family for Vercel. ## How it Works ### Frontend The frontend is built with Next.js and AI SDK. User messages are submitted through the sendMessage API, which forwards the prompt and selected model (default GPT-5) to the backend. Once deployed, Vercel automatically provisions infrastructure to: Serve the frontend quickly through the Vercel CDN. Handle API calls with Vercel Functions. ### Backend The backend runs as a function on Fluid compute, optimized for prompt-based workloads. Since LLMs often idle while reasoning, Fluid compute reallocates unused cycles to serve other requests or reduce cost. const result = streamText({ model: "openai/gpt-5", system: prompt, messages, stopWhen: stepCountIs(20), tools }); The API call is powered by the AI SDK. It handles the conversation history, verifies the user with Vercel BotID, and routes through AI Gateway automatically. ### Sandboxed Execution AI responses stream into an ephemeral sandbox. Each sandbox is stateless, isolated, and expires after a short timeout. It has no access to your projects or data, making it safe to run arbitrary code. 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