deep-research

Use any LLMs (Large Language Models) for Deep Research. Support SSE API and MCP server.

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Deep Research is a lightning-fast tool that uses powerful AI models to generate comprehensive 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 various topics. The tool ensures privacy by processing and storing all data locally. It supports multi-platform deployment, offers support for various large language models, web search functionality, knowledge graph generation, research history preservation, local and server API support, PWA technology, multi-key payload support, multi-language support, and is built with modern technologies like Next.js and Shadcn UI. Deep Research is open-source under the MIT License.

README:

Deep Research

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, 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)

Get Gemini API Key
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.
Start using

Use Other LLM

Deploy the project to Vercel or Cloudflare
Set the LLM API key
Set the LLM API base URL (optional)
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

Clone the repository:

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

Install dependencies:

pnpm install  # or npm install or yarn install

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
```
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:

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:

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:

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:

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..

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 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 forms of API: Server-Sent Events (SSE) and Model Context Protocol (MCP).

Server-Sent Events API

The Deep Research API provides a real-time interface for initiating and monitoring complex research tasks.

Recommended to use the API via @microsoft/fetch-event-source, to get the final report, you need to listen to the message event, the data will be returned in the form of a text stream.

POST method

Endpoint: /api/sse

Method: POST

Body:

interface SSEConfig {
  // Research topic
  query: string;
  // AI provider, Possible values include: google, openai, anthropic, deepseek, xai, mistral, azure, openrouter, openaicompatible, pollinations, ollama
  provider: string;
  // Thinking model id
  thinkingModel: string;
  // Task model id
  taskModel: string;
  // Search provider, Possible values include: model, tavily, firecrawl, exa, bocha, searxng
  searchProvider: string;
  // Response Language, also affects the search language. (optional)
  language?: string;
  // Maximum number of search results. Default, `5` (optional)
  maxResult?: number;
  // Whether to include content-related images in the final report. Default, `true`. (optional)
  enableCitationImage?: boolean;
  // Whether to include citation links in search results and final reports. Default, `true`. (optional)
  enableReferences?: boolean;
}

Headers:

interface Headers {
  "Content-Type": "application/json";
  // If you set an access password
  // Authorization: "Bearer YOUR_ACCESS_PASSWORD";
}

See the detailed API documentation.

GET method

This is an interesting implementation. You can watch the whole process of deep research directly through the URL just like watching a video.

You can access the deep research report via the following link:

http://localhost:3000/api/sse/live?query=AI+trends+for+this+year&provider=pollinations&thinkingModel=openai&taskModel=openai-fast&searchProvider=searxng

Query Params:

// The parameters are the same as POST parameters
interface QueryParams extends SSEConfig {
  // If you set the `ACCESS_PASSWORD` environment variable, this parameter is required
  password?: string;
}

Model Context Protocol (MCP) Server

Currently supports StreamableHTTP and SSE Server Transport.

StreamableHTTP server endpoint: /api/mcp, transport type: streamable-http

SSE server endpoint: /api/mcp/sse, transport type: sse

{
  "mcpServers": {
    "deep-research": {
      "url": "http://127.0.0.1:3000/api/mcp",
      "transportType": "streamable-http",
      "timeout": 600
    }
  }
}

Note: Since deep research take a long time to execute, you need to set a longer timeout to avoid interrupting the study.

If your server sets ACCESS_PASSWORD, the MCP service will be protected and you need to add additional headers parameters:

{
  "mcpServers": {
    "deep-research": {
      "url": "http://127.0.0.1:3000/api/mcp",
      "transportType": "streamable-http",
      "timeout": 600,
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_PASSWORD"
      }
    }
  }
}

Enabling MCP service requires setting global environment variables:

# MCP Server AI provider
# Possible values include: google, openai, anthropic, deepseek, xai, mistral, azure, openrouter, openaicompatible, pollinations, ollama
MCP_AI_PROVIDER=google
# MCP Server search provider. Default, `model`
# Possible values include: model, tavily, firecrawl, exa, bocha, searxng
MCP_SEARCH_PROVIDER=tavily
# MCP Server thinking model id, the core model used in deep research.
MCP_THINKING_MODEL=gemini-2.0-flash-thinking-exp
# MCP Server task model id, used for secondary tasks, high output models are recommended.
MCP_TASK_MODEL=gemini-2.0-flash-exp

Note: To ensure that the MCP service can be used normally, you need to set the environment variables of the corresponding model and search engine. For specific environment variable parameters, please refer to env.tpl.

🪄 How it works

Research topic
- Input research topic
- Use local research resources (optional)
- Start thinking (or rethinking)
Propose your ideas
- The system asks questions
  - Answer system questions (optional)
  - Write a research plan (or rewrite the research plan)
- The system outputs the research plan
  - Start in-depth research (or re-research)
  - The system generates SERP queries
Information collection
- Initial research
  - Retrieve local research resources based on SERP queries
  - Collect information from the Internet based on SERP queries
- In-depth research (this process can be repeated)
  - Propose research suggestions (optional)
  - Start a new round of information collection (the process is the same as the initial research)
Generate Final Report
- Make a writing request (optional)
- Summarize all research materials into a comprehensive Markdown report
- Regenerate research report (optional)

flowchart TB
    A[Research Topic]:::start

    subgraph Propose[Propose your ideas]
        B1[System asks questions]:::process
        B2[System outputs the research plan]:::process
        B3[System generates SERP queries]:::process
        B1 --> B2
        B2 --> B3
    end

    subgraph Collect[Information collection]
        C1[Initial research]:::collection
        C1a[Retrieve local research resources based on SERP queries]:::collection
        C1b[Collect information from the Internet based on SERP queries]:::collection
        C2[In-depth research]:::recursive
        Refine{More in-depth research needed?}:::decision

        C1 --> C1a
        C1 --> C1b
        C1a --> C2
        C1b --> C2
        C2 --> Refine
        Refine -->|Yes| C2
    end

    Report[Generate Final Report]:::output

    A --> Propose
    B3 --> C1

    %% Connect the exit from the loop/subgraph to the final report
    Refine -->|No| Report

    %% Styling
    classDef start fill:#7bed9f,stroke:#2ed573,color:black
    classDef process fill:#70a1ff,stroke:#1e90ff,color:black
    classDef recursive fill:#ffa502,stroke:#ff7f50,color:black
    classDef output fill:#ff4757,stroke:#ff6b81,color:black
    classDef collection fill:#a8e6cf,stroke:#3b7a57,color:black
    classDef decision fill:#c8d6e5,stroke:#8395a7,color:black

    class A start
    class B1,B2,B3 process
    class C1,C1a,C1b collection
    class C2 recursive
    class Refine decision
    class Report output

🙋 FAQs

Why does my Ollama or SearXNG not work properly and displays the error TypeError: Failed to fetch?

If your request generates CORS due to browser security restrictions, you need to configure parameters for Ollama or SearXNG to allow cross-domain requests. You can also consider using the server proxy mode, which is a backend server that makes requests, which can effectively avoid cross-domain issues.

🛡️ Privacy

Deep Research is designed with your privacy in mind. All research data and generated reports are stored locally on your machine. We do not collect or transmit any of your research data to external servers (unless you are explicitly using server-side API calls, in which case data is sent to API through your configured proxy if any). Your privacy is our priority.

🙏 Acknowledgements

Next.js - The React framework for building performant web applications.
Shadcn UI - Beautifully designed components that helped streamline the UI development.
AI SDKs - Powering the intelligent research capabilities of Deep Research.
Deep Research - Thanks to the project dzhng/deep-research for inspiration.

🤝 Contributing

We welcome contributions to Deep Research! If you have ideas for improvements, bug fixes, or new features, please feel free to:

Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and commit them.
Submit a pull request.

For major changes, please open an issue first to discuss your proposed changes.

✉️ Contact

If you have any questions, suggestions, or feedback, please create a new issue.

📝 License

Deep Research is released under the MIT License. This license allows for free use, modification, and distribution for both commercial and non-commercial purposes.

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