DeepWideResearch

<p align="center"> <img src="chat_interface/public/title3.svg" alt="Open Deep Wide Research" /> </p> <h1 align="center">Open Deep Wide Research</h1> <p align="center"> <a href="https://go.deepwideresearch.com/4o5mSMy" target="_blank"> <img src="https://img.shields.io/badge/Web-deepwideresearch.com-39BC66?style=flat&logo=google-chrome&logoColor=white" alt="Homepage" height="22" /> </a> <a href="https://x.com/deepwiderag" target="_blank"> <img src="https://img.shields.io/badge/X-@deepwiderag-000000?style=flat&logo=x&logoColor=white" alt="X (Twitter)" height="22" /> </a> <a href="https://discord.gg/puppychat" target="_blank"> <img src="https://img.shields.io/badge/Discord-Join-5865F2?style=flat&logo=discord&logoColor=white" alt="Discord" height="22" /> </a> <a href="mailto:guantum@puppyagent.com"> <img src="https://img.shields.io/badge/Support-guantum@puppyagent.com-F44336?style=flat&logo=gmail&logoColor=white" alt="Support" height="22" /> </a> </p> <p align="center"> Agentic RAG for any scenario<br>Customize sources, depth, and width </p> <p align="center"> <img src="chat_interface/public/chat_interface.png" alt="Deep & Wide Research Chat Interface" /> </p> ## Why Do You Need Open Deep Wide Research? In 2025, we observed 2 critical trends reshaping the Retrieval-Augmented Generation (RAG) tech stacks: 1. Traditional, Rigid, pipeline-driven RAG is giving way to more dynamic agentic RAG systems. 2. The emergence of MCP is dramatically lowering the complexity of developing enterprise level Agentic RAG. However, a core pain point remains: 1. **Developers still struggle to balance response quality, speed, and cost, as most agentic solutions offer a rigid, one-size-fits-all approach.** Based on these trends and the core pain point, the market needs a single, open-source RAG agent that is MCP-compatible and offers granular control over performance, scope, and cost. We built **Open Deep Wide Research** to be that solution, providing one agent for all RAG scenarios. It gives you granular control over the core dimensions of agentic research: * **Sources**: Connect custom data sources, from internal knowledge bases to specialized APIs. * **Deep**: Controls response time and reasoning depth. * **Wide**: Controls information breadth across your selected sources. The "Deep × Wide" coordinate system also transparently predicts the cost of each response, giving you full budget control. **Example Scenarios:** <table> <thead> <tr> <th align="left">_{User Story}</th> <th align="left">_Settings</th> <th align="left">_{Example Query}</th> <th align="center">_Time</th> <th align="center">_Cost</th> </tr> </thead> <tbody> <tr> <td align="left">_{<b>Customer Service Bot</b>}</td> <td align="left">_{Deep: <code>███░░░░░░░░░</code> 25%<br/>Wide: <code>███░░░░░░░░░</code> 25%}</td> <td align="left">_{"What glasses do you provide?"}</td> <td align="center">_~10s</td> <td align="center">_~$0.01</td> </tr> <tr> <td align="left">_{<b>Market Research</b>}</td> <td align="left">_{Deep: <code>███░░░░░░░░░</code> 25%<br/>Wide: <code>████████████</code> 100%}</td> <td align="left">_{"100 Notion and Airtable alternatives"}</td> <td align="center">_~2-3min</td> <td align="center">_~$0.10</td> </tr> <tr> <td align="left">_{<b>Enterprise Analytics</b>}</td> <td align="left">_{Deep: <code>████████████</code> 100%<br/>Wide: <code>████████████</code> 100%}</td> <td align="left">_{"What was the ROI of our latest marketing campaign?"}</td> <td align="center">_~5min</td> <td align="center">_~$1.00</td> </tr> </tbody> </table> > If this mission resonates with you, please give us a star ⭐ and fork it! 🤞 ## Features - **Deep × Wide Control** – Tune the depth of reasoning and breadth of information sources to perfectly match any RAG scenario, from quick chats to in-depth analysis. - **Predictable Cost Management** – No more surprise bills. Cost is a transparent function of your Deep × Wide settings, giving you full control over your budget. - **MCP Protocol Native Support** – Built on the Model Context Protocol for seamless integration with any compliant data source or tool, creating a truly extensible and future-proof agent. - **Self-Hosted for Maximum Privacy** – Deploy on your own infrastructure to maintain absolute control over your data and meet the strictest security requirements. - **Hot‑Swappable Models** – Plug in OpenAI, Claude, or your private LLM instantly. - **Customizable Search Engines** – Integrate any search provider. Tavily and Exa supported out-of-the-box. As long as it supports MCP. ## Get Started ### Prerequisites - Python 3.9+ and Node.js 18+ - API keys: Open Router (required), and Exa / Tavily (at least one) - Recommended model: open-o4mini ### Deployment Options - API-only (Backend): If you only need the Deep Research backend as an API to embed in your codebase, deploy the backend only. - Full stack (Frontend + Backend): If you want the full experience with the web UI, deploy both the backend and the frontend. ### Backend 1. Copy the env template: ```bash cp deep_wide_research/env.example deep_wide_research/.env ``` 2. Edit the copied .env and set your keys: ```bash # deep_wide_research/.env OPENROUTER_API_KEY=your_key # At least one of the following EXA_API_KEY=your_exa_key # or TAVILY_API_KEY=your_tavily_key ``` > You can obtain the Tavily and Exa API keys from their official sites: [Tavily](https://www.tavily.com/) and [Exa](https://exa.ai/). 3. Set up the environment: ```bash cd deep_wide_research python -m venv deep-wide-research source deep-wide-research/bin/activate pip install -r requirements.txt ``` 4. Start the backend server: ```bash python main.py ``` ### Frontend 1. Copy the env template: ```bash cp chat_interface/env.example chat_interface/.env.local ``` 2. Install dependencies and start the dev server: ```bash cd chat_interface npm install npm run dev ``` 3. Open the app: Open **http://localhost:3000** – Start researching in seconds. ### Docker (Production) ```bash docker-compose up -d ``` --- ## How We Compare <table> <thead> <tr> <th align="left">_Feature</th> <th align="center">_{<img src="asserts/DWResearch.png" alt="Open Deep Wide Research" width="40" /><br/>Open<br/>Deep Wide<br/>Research}</th> <th align="center">_{<img src="chat_interface/public/openai.jpg" alt="OpenAI" width="32" /><br/>OpenAI<br/>Deep Research}</th> <th align="center">_{<img src="chat_interface/public/genmini.jpg" alt="Gemini" width="32" /><br/>Gemini<br/>Deep Research}</th> <th align="center">_{<img src="chat_interface/public/manus.png" alt="Manus" width="28" /><br/>Manus<br/>Wide Research}</th> <th align="center">_{<img src="chat_interface/public/genspark.jpg" alt="GenSpark" width="40" /><br/>GenSpark<br/>Deep Research}</th> <th align="center">_{<img src="chat_interface/public/jina.jpg" alt="Jina" width="40" /><br/>Jina<br/>DeepSearch}</th> <th align="center">_{<img src="chat_interface/public/langchain.png" alt="LangChain" width="40" /><br/>LangChain<br/>Open Deep Research}</th> </tr> </thead> <tbody> <tr> <td align="left">_{<b>Depth × width controls</b>}</td> <td align="center">_{D x W}</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_W</td> <td align="center">_×</td> <td align="center">_D</td> <td align="center">_×</td> </tr> <tr> <td align="left">_{<b>Open source</b>}</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_✅</td> <td align="center">_✅</td> </tr> <tr> <td align="left">_{<b>MCP support</b>}</td> <td align="center">_✅</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_✅</td> <td align="center">_×</td> </tr> <tr> <td align="left">_{<b>SDK / API</b>}</td> <td align="center">_✅</td> <td align="center">_✅</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_✅</td> <td align="center">_✅</td> </tr> <tr> <td align="left">_{<b>Local knowledge</b>}</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_✅</td> <td align="center">_✅</td> </tr> <tr> <td align="left">_{<b>Model flexibility</b>}</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_✅</td> </tr> <tr> <td align="left">_{<b>Search engine flexibility</b>}</td> <td align="center">_✅</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> <td align="center">_×</td> </tr> <tr> <td align="left">_{<b>Performance</b>}</td> <td align="center">₅</td> <td align="center">₅</td> <td align="center">₄</td> <td align="center">₃</td> <td align="center">₄</td> <td align="center">₄</td> <td align="center">₃</td> </tr> </tbody> </table> _{<i>Names are trademarks of their owners; descriptions are generalized and may change.</i>} --- ### Deep Wide Research Archietecture <p align="center"> <img src="chat_interface/public/archietecture.svg" alt="Deep & Wide Research Architecture" width="960" /> </p> --- ## License This project is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for details. Copyright (c) 2025 PuppyAgent and contributors.