# DeepWideResearch > Agentic RAG for any scenario. Customize sources, depth, and width - **Type:** MCP server - **Install:** `agentstack add mcp-puppyone-ai-deepwideresearch` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [puppyone-ai](https://agentstack.voostack.com/s/puppyone-ai) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [puppyone-ai](https://github.com/puppyone-ai) - **Source:** https://github.com/puppyone-ai/DeepWideResearch - **Website:** https://www.deepwideresearch.com ## Install ```sh agentstack add mcp-puppyone-ai-deepwideresearch ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About Open Deep Wide Research Agentic RAG for any scenarioCustomize sources, depth, and width ## 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:** User Story Settings Example Query Time Cost Customer Service Bot Deep: ███░░░░░░░░░ 25%Wide: ███░░░░░░░░░ 25% "What glasses do you provide?" ~10s ~$0.01 Market Research Deep: ███░░░░░░░░░ 25%Wide: ████████████ 100% "100 Notion and Airtable alternatives" ~2-3min ~$0.10 Enterprise Analytics Deep: ████████████ 100%Wide: ████████████ 100% "What was the ROI of our latest marketing campaign?" ~5min ~$1.00 > 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 Feature OpenDeep WideResearch OpenAIDeep Research GeminiDeep Research ManusWide Research GenSparkDeep Research JinaDeepSearch LangChainOpen Deep Research Depth × width controls D x W × × W × D × Open source ✅ × × × × ✅ ✅ MCP support ✅ ✅ × × × ✅ × SDK / API ✅ ✅ ✅ × × ✅ ✅ Local knowledge ✅ × × × × ✅ ✅ Model flexibility ✅ × × × × × ✅ Search engine flexibility ✅ × × × × × × Performance 5 5 4 3 4 4 3 Names are trademarks of their owners; descriptions are generalized and may change. --- ### Deep Wide Research Archietecture --- ## 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. ## Source & license This open-source MCP server is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [puppyone-ai](https://github.com/puppyone-ai) - **Source:** [puppyone-ai/DeepWideResearch](https://github.com/puppyone-ai/DeepWideResearch) - **License:** Apache-2.0 - **Homepage:** https://www.deepwideresearch.com Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** yes - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/mcp-puppyone-ai-deepwideresearch - Seller: https://agentstack.voostack.com/s/puppyone-ai - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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