深度研究
STDIOAI驱动的深度研究平台快速生成报告
AI驱动的深度研究平台快速生成报告
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.
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
Follow these steps to get Deep Research up and running on your local browser.
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.
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.
Currently the project supports deployment to Cloudflare, but you need to follow How to deploy to Cloudflare Pages to do it.
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
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
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.
Currently the project supports two forms of API: Server-Sent Events (SSE) and Model Context Protocol (MCP).
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.
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.
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; }
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.
Research topic
Propose your ideas
Information collection
Generate Final Report
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
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.
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.
dzhng/deep-research for inspiration.We welcome contributions to Deep Research! If you have ideas for improvements, bug fixes, or new features, please feel free to:
For major changes, please open an issue first to discuss your proposed changes.
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.