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.
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.
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.
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:
typescript
1interfaceSSEConfig{2// Research topic3 query:string;4// AI provider, Possible values include: google, openai, anthropic, deepseek, xai, mistral, azure, openrouter, openaicompatible, pollinations, ollama5 provider:string;6// Thinking model id7 thinkingModel:string;8// Task model id9 taskModel:string;10// Search provider, Possible values include: model, tavily, firecrawl, exa, bocha, searxng11 searchProvider:string;12// Response Language, also affects the search language. (optional)13 language?:string;14// Maximum number of search results. Default, `5` (optional)15 maxResult?:number;16// Whether to include content-related images in the final report. Default, `true`. (optional)17 enableCitationImage?:boolean;18// Whether to include citation links in search results and final reports. Default, `true`. (optional)19 enableReferences?:boolean;20}
Headers:
typescript
1interfaceHeaders{2"Content-Type":"application/json";3// If you set an access password4// Authorization: "Bearer YOUR_ACCESS_PASSWORD";5}
1// The parameters are the same as POST parameters2interfaceQueryParamsextendsSSEConfig{3// If you set the `ACCESS_PASSWORD` environment variable, this parameter is required4 password?:string;5}
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
Enabling MCP service requires setting global environment variables:
bash
1# MCP Server AI provider2# Possible values include: google, openai, anthropic, deepseek, xai, mistral, azure, openrouter, openaicompatible, pollinations, ollama3MCP_AI_PROVIDER=google
4# MCP Server search provider. Default, `model`5# Possible values include: model, tavily, firecrawl, exa, bocha, searxng6MCP_SEARCH_PROVIDER=tavily
7# MCP Server thinking model id, the core model used in deep research.8MCP_THINKING_MODEL=gemini-2.0-flash-thinking-exp
9# MCP Server task model id, used for secondary tasks, high output models are recommended.10MCP_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)
mermaid
1flowchart TB
2 A[Research Topic]:::start
34subgraph Propose[Propose your ideas]5 B1[System asks questions]:::process
6 B2[System outputs the research plan]:::process
7 B3[System generates SERP queries]:::process
8 B1 --> B2
9 B2 --> B3
10end1112subgraph Collect[Information collection]13 C1[Initial research]:::collection
14 C1a[Retrieve local research resources based on SERP queries]:::collection
15 C1b[Collect information from the Internet based on SERP queries]:::collection
16 C2[In-depth research]:::recursive
17 Refine{More in-depth research needed?}:::decision
1819 C1 --> C1a
20 C1 --> C1b
21 C1a --> C2
22 C1b --> C2
23 C2 --> Refine
24 Refine -->|Yes| C2
25end2627 Report[Generate Final Report]:::output
2829 A --> Propose
30 B3 --> C1
3132%% Connect the exit from the loop/subgraph to the final report33 Refine -->|No| Report
3435%% Styling36classDef start fill:#7bed9f,stroke:#2ed573,color:black37classDef process fill:#70a1ff,stroke:#1e90ff,color:black38classDef recursive fill:#ffa502,stroke:#ff7f50,color:black39classDef output fill:#ff4757,stroke:#ff6b81,color:black40classDef collection fill:#a8e6cf,stroke:#3b7a57,color:black41classDef decision fill:#c8d6e5,stroke:#8395a7,color:black4243class A start
44class B1,B2,B3 process
45class C1,C1a,C1b collection
46class C2 recursive
47class Refine decision
48class 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.
Deep Research is released under the MIT License. This license allows for free use, modification, and distribution for both commercial and non-commercial purposes.
