NextChat is a lightweight, self-hostable web and desktop chat UI for interacting with multiple LLM providers (OpenAI, Claude, Gemini, DeepSeek, and many others) through a single interface. It offers one-click deployment, local-first data storage, prompt templates, plugins, and cross-platform clients.
A polished, multi-provider chat client (web, PWA, and desktop via Tauri) that gives end users a ChatGPT-like interface in front of OpenAI, Anthropic, Google, DeepSeek, and numerous regional Chinese model APIs. The core idea is a deploy-it-yourself frontend where your conversation data lives locally rather than on a vendor's servers.
When to use NextChat
Good when you want a private, branded, or low-cost ChatGPT alternative deployable to Vercel/Zeabur in minutes, when you need a single UI bridging many model providers, or when you want a compact cross-platform desktop client with markdown/LaTeX/mermaid rendering, prompt templates (masks), and plugin/MCP support.
When not to
Not the right tool if you need to build agentic workflows or embed LLM logic into your own application code — it's an end-user app, not an SDK or library. For team RBAC, audit logging, and shared knowledge bases you must pay for the closed-source Enterprise Edition. For RAG-heavy pipelines or programmatic orchestration, dedicated frameworks fit better.
Strengths
Extremely broad provider coverage out of the box, including many Chinese vendors (Baidu, ByteDance, Alibaba, iFlytek, ChatGLM)
One-click deploy to Vercel and tiny (~5MB) cross-platform desktop client
Local-first data storage and access-code gating for cheap private hosting
Rich rendering (LaTeX, mermaid, code highlight), PWA, dark mode, and extensive i18n
Plugins, MCP, artifacts, and realtime chat features keep it feature-competitive
Trade-offs
Key enterprise features (permissions, audit, knowledge base) are gated behind a paid closed-source edition
Self-deploy on Vercel has awkward update mechanics requiring manual fork/sync setup
Configuration is sprawling — dozens of provider env vars and a cryptic CUSTOM_MODELS DSL
It's a consumer-facing UI, not extensible as a developer library
Bring-your-own-API-key model means cost/usage control depends on the operator
Maturity
Very mature and widely adopted with ~88k GitHub stars, active versioned releases, iOS app, and an enterprise commercial offering. It is one of the most popular open-source LLM chat frontends, though much of the advanced functionality lives in the paid tier.
Meeting Your Company's Privatization and Customization Deployment Requirements:
Brand Customization: Tailored VI/UI to seamlessly align with your corporate brand image.
Resource Integration: Unified configuration and management of dozens of AI resources by company administrators, ready for use by team members.
Permission Control: Clearly defined member permissions, resource permissions, and knowledge base permissions, all controlled via a corporate-grade Admin Panel.
Knowledge Integration: Combining your internal knowledge base with AI capabilities, making it more relevant to your company's specific business needs compared to general AI.
Security Auditing: Automatically intercept sensitive inquiries and trace all historical conversation records, ensuring AI adherence to corporate information security standards.
Private Deployment: Enterprise-level private deployment supporting various mainstream private cloud solutions, ensuring data security and privacy protection.
Continuous Updates: Ongoing updates and upgrades in cutting-edge capabilities like multimodal AI, ensuring consistent innovation and advancement.
Self-host Model: Fully compatible with RWKV-Runner, as well as server deployment of LocalAI: llama/gpt4all/rwkv/vicuna/koala/gpt4all-j/cerebras/falcon/dolly etc.
Artifacts: Easily preview, copy and share generated content/webpages through a separate window #5092
Plugins: support network search, calculator, any other apis etc. #165#5353
network search, calculator, any other apis etc. #165#5353
If you have deployed your own project with just one click following the steps above, you may encounter the issue of "Updates Available" constantly showing up. This is because Vercel will create a new project for you by default instead of forking this project, resulting in the inability to detect updates correctly.
We recommend that you follow the steps below to re-deploy:
Delete the original repository;
Use the fork button in the upper right corner of the page to fork this project;
If you encounter a failure of Upstream Sync execution, please manually update code.
After forking the project, due to the limitations imposed by GitHub, you need to manually enable Workflows and Upstream Sync Action on the Actions page of the forked project. Once enabled, automatic updates will be scheduled every hour:
Manually Updating Code
If you want to update instantly, you can check out the GitHub documentation to learn how to synchronize a forked project with upstream code.
You can star or watch this project or follow author to get release notifications in time.
Access Password
This project provides limited access control. Please add an environment variable named CODE on the vercel environment variables page. The value should be passwords separated by comma like this:
code1,code2,code3
After adding or modifying this environment variable, please redeploy the project for the changes to take effect.
Environment Variables
CODE (optional)
Access password, separated by comma.
OPENAI_API_KEY (required)
Your openai api key, join multiple api keys with comma.
If you do not want users to input their own API key, set this value to 1.
DISABLE_GPT4 (optional)
Default: Empty
If you do not want users to use GPT-4, set this value to 1.
ENABLE_BALANCE_QUERY (optional)
Default: Empty
If you do want users to query balance, set this value to 1.
DISABLE_FAST_LINK (optional)
Default: Empty
If you want to disable parse settings from url, set this to 1.
CUSTOM_MODELS (optional)
Default: Empty
Example: +llama,+claude-2,-gpt-3.5-turbo,gpt-4-1106-preview=gpt-4-turbo means add llama, claude-2 to model list, and remove gpt-3.5-turbo from list, and display gpt-4-1106-preview as gpt-4-turbo.
To control custom models, use + to add a custom model, use - to hide a model, use name=displayName to customize model name, separated by comma.
User -all to disable all default models, +all to enable all default models.
For Azure: use modelName@Azure=deploymentName to customize model name and deployment name.
Example: +gpt-3.5-turbo@Azure=gpt35 will show option gpt35(Azure) in model list.
If you only can use Azure model, -all,+gpt-3.5-turbo@Azure=gpt35 will gpt35(Azure) the only option in model list.
For ByteDance: use modelName@bytedance=deploymentName to customize model name and deployment name.
Example: +Doubao-lite-4k@bytedance=ep-xxxxx-xxx will show option Doubao-lite-4k(ByteDance) in model list.
DEFAULT_MODEL （optional）
Change default model
VISION_MODELS (optional)
Default: Empty
Example: gpt-4-vision,claude-3-opus,my-custom-model means add vision capabilities to these models in addition to the default pattern matches (which detect models containing keywords like "vision", "claude-3", "gemini-1.5", etc).
Add additional models to have vision capabilities, beyond the default pattern matching. Multiple models should be separated by commas.
WHITE_WEBDAV_ENDPOINTS (optional)
You can use this option if you want to increase the number of webdav service addresses you are allowed to access, as required by the format：
Each address must be a complete endpoint
https://xxxx/yyy
Multiple addresses are connected by ', '
DEFAULT_INPUT_TEMPLATE (optional)
Customize the default template used to initialize the User Input Preprocessing configuration item in Settings.
STABILITY_API_KEY (optional)
Stability API key.
STABILITY_URL (optional)
Customize Stability API url.
ENABLE_MCP (optional)
Enable MCP（Model Context Protocol）Feature
SILICONFLOW_API_KEY (optional)
SiliconFlow API Key.
SILICONFLOW_URL (optional)
SiliconFlow API URL.
AI302_API_KEY (optional)
302.AI API Key.
AI302_URL (optional)
302.AI API URL.
Requirements
NodeJS >= 18, Docker >= 20
Development
Before starting development, you must create a new .env.local file at project root, and place your api key into it:
OPENAI_API_KEY=<your api key here>
# if you are not able to access openai service, use this BASE_URL
BASE_URL=https://chatgpt1.nextweb.fun/api/proxy
Local Development
# 1. install nodejs and yarn first
# 2. config local env vars in `.env.local`
# 3. run
yarn install
yarn dev
