Deep Research

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<h1>Deep Research</h1>

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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, Atlas Cloud, Grok, Mistral, Azure OpenAI, any OpenAI Compatible LLMs, OpenRouter, Ollama, etc.
- Support Web Search: Supports search engines such as Searxng, Tavily, Firecrawl, fastCRW, 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
2. One-click deployment of the project, you can choose to deploy to Vercel or Cloudflare

Currently the project supports deployment to Cloudflare, but you need to follow How to deploy to Cloudflare Pages 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 (version 18.18.0 or later recommended)
- pnpm or npm or yarn

Installation

1. Clone the repository:

   git clone https://github.com/u14app/deep-research.git
   cd deep-research
   

2. Install dependencies:

   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.

   # For Development
   cp env.tpl .env.local
   # For Production
   cp env.tpl .env
   

4. Run the development server:

   pnpm dev  # or npm run dev or yarn dev
   

Open your browser and visit 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

Cloudflare

Currently the project supports deployment to Cloudflare, but you need to follow How to deploy to Cloudflare Pages 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.

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:

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