**Lightning-Fast Deep Research Report** Deep Research uses a variety of powerful AI models to generate in-depth research reports in just a few minutes. It leverages advanced "Thinking" and "Task" models, combined with an internet connection, to provide fast and insightful analysis on a variety of topics. **Your privacy is paramount - all data is processed and stored locally.** ## ✨ Features - **Rapid Deep Research:** Generates comprehensive research reports in about 2 minutes, significantly accelerating your research process. - **Multi-platform Support:** Supports rapid deployment to Vercel, Cloudflare and other platforms. - **Powered by AI:** Utilizes the advanced AI models for accurate and insightful analysis. - **Privacy-Focused:** Your data remains private and secure, as all data is stored locally on your browser. - **Support for Multi-LLM:** Supports a variety of mainstream large language models, including Gemini, OpenAI, Anthropic, Deepseek, Grok, Mistral, Azure OpenAI, any OpenAI Compatible LLMs, OpenRouter, Ollama, etc. - **Support Web Search:** Supports search engines such as Searxng, Tavily, Firecrawl, Exa, Bocha, Brave, etc., allowing LLMs that do not support search to use the web search function more conveniently. - **Thinking & Task Models:** Employs sophisticated "Thinking" and "Task" models to balance depth and speed, ensuring high-quality results quickly. Support switching research models. - **Support Further Research:** You can refine or adjust the research content at any stage of the project and support re-research from that stage. - **Local Knowledge Base:** Supports uploading and processing text, Office, PDF and other resource files to generate local knowledge base. - **Artifact:** Supports editing of research content, with two editing modes: WYSIWYM and Markdown. It is possible to adjust the reading level, article length and full text translation. - **Knowledge Graph:** It supports one-click generation of knowledge graph, allowing you to have a systematic understanding of the report content. - **Research History:** Support preservation of research history, you can review previous research results at any time and conduct in-depth research again. - **Local & Server API Support:** Offers flexibility with both local and server-side API calling options to suit your needs. - **Support for SaaS and MCP:** You can use this project as a deep research service (SaaS) through the SSE API, or use it in other AI services through MCP service. - **Support PWA:** With Progressive Web App (PWA) technology, you can use the project like a software. - **Support Multi-Key payload:** Support Multi-Key payload to improve API response efficiency. - **Multi-language Support**: English, 简体中文, Español. - **Built with Modern Technologies:** Developed using Next.js 15 and Shadcn UI, ensuring a modern, performant, and visually appealing user experience. - **MIT Licensed:** Open-source and freely available for personal and commercial use under the MIT License. ## 🎯 Roadmap - [x] Support preservation of research history - [x] Support editing final report and search results - [x] Support for other LLM models - [x] Support file upload and local knowledge base - [x] Support SSE API and MCP server ## 🚀 Getting Started ### Use Free Gemini (recommend) 1. Get [Gemini API Key](https://aistudio.google.com/app/apikey) 2. One-click deployment of the project, you can choose to deploy to Vercel or Cloudflare [](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fu14app%2Fdeep-research&project-name=deep-research&repository-name=deep-research) Currently the project supports deployment to Cloudflare, but you need to follow [How to deploy to Cloudflare Pages](./docs/How-to-deploy-to-Cloudflare-Pages.md) to do it. 3. Start using ### Use Other LLM 1. Deploy the project to Vercel or Cloudflare 2. Set the LLM API key 3. Set the LLM API base URL (optional) 4. Start using ## ⌨️ Development Follow these steps to get Deep Research up and running on your local browser. ### Prerequisites - [Node.js](https://nodejs.org/) (version 18.18.0 or later recommended) - [pnpm](https://pnpm.io/) or [npm](https://www.npmjs.com/) or [yarn](https://yarnpkg.com/) ### Installation 1. **Clone the repository:** ```bash git clone https://github.com/u14app/deep-research.git cd deep-research ``` 2. **Install dependencies:** ```bash pnpm install # or npm install or yarn install ``` 3. **Set up Environment Variables:** You need to modify the file `env.tpl` to `.env`, or create a `.env` file and write the variables to this file. ```bash # For Development cp env.tpl .env.local # For Production cp env.tpl .env ``` 4. **Run the development server:** ```bash pnpm dev # or npm run dev or yarn dev ``` Open your browser and visit [http://localhost:3000](http://localhost:3000) to access Deep Research. ### Custom Model List The project allow custom model list, but **only works in proxy mode**. Please add an environment variable named `NEXT_PUBLIC_MODEL_LIST` in the `.env` file or environment variables page. Custom model lists use `,` to separate multiple models. If you want to disable a model, use the `-` symbol followed by the model name, i.e. `-existing-model-name`. To only allow the specified model to be available, use `-all,+new-model-name`. ## 🚢 Deployment ### Vercel [](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Fu14app%2Fdeep-research&project-name=deep-research&repository-name=deep-research) ### Cloudflare Currently the project supports deployment to Cloudflare, but you need to follow [How to deploy to Cloudflare Pages](./docs/How-to-deploy-to-Cloudflare-Pages.md) to do it. ### Docker > The Docker version needs to be 20 or above, otherwise it will prompt that the image cannot be found. > ⚠️ Note: Most of the time, the docker version will lag behind the latest version by 1 to 2 days, so the "update exists" prompt will continue to appear after deployment, which is normal. ```bash docker pull xiangfa/deep-research:latest docker run -d --name deep-research -p 3333:3000 xiangfa/deep-research ``` You can also specify additional environment variables: ```bash docker run -d --name deep-research \ -p 3333:3000 \ -e ACCESS_PASSWORD=your-password \ -e GOOGLE_GENERATIVE_AI_API_KEY=AIzaSy... \ xiangfa/deep-research ``` or build your own docker image: ```bash docker build -t deep-research . docker run -d --name deep-research -p 3333:3000 deep-research ``` If you need to specify other environment variables, please add `-e key=value` to the above command to specify it. Deploy using `docker-compose.yml`: ```bash version: '3.9' services: deep-research: image: xiangfa/deep-research container_name: deep-research environment: - ACCESS_PASSWORD=your-password - GOOGLE_GENERATIVE_AI_API_KEY=AIzaSy... ports: - 3333:3000 ``` or build your own docker compose: ```bash docker compose -f docker-compose.yml build ``` ### Static Deployment You can also build a static page version directly, and then upload all files in the `out` directory to any website service that supports static pages, such as Github Page, Cloudflare, Vercel, etc.. ```bash pnpm build:export ``` ## ⚙️ Configuration As mentioned in the "Getting Started" section, Deep Research utilizes the following environment variables for server-side API configurations: Please refer to the file [env.tpl](./env.tpl) for all available environment variables. **Important Notes on Environment Variables:** - **Privacy Reminder:** These environment variables are primarily used for **server-side API calls**. When using the **local API mode**, no API keys or server-side configurations are needed, further enhancing your privacy. - **Multi-key Support:** Supports multiple keys, each key is separated by `,`, i.e. `key1,key2,key3`. - **Security Setting:** By setting `ACCESS_PASSWORD`, you can better protect the security of the server API. - **Make variables effective:** After adding or modifying this environment variable, please redeploy the project for the changes to take effect. ## 📄 API documentation Currently the project supports two form ---