Deepwideresearch
Agentic RAG for any scenario. Customize sources, depth, and width
What is Deepwideresearch?
Deepwideresearch is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to agentic rag for any scenario. customize sources, depth, and width
Agentic RAG for any scenario. Customize sources, depth, and width
This server falls under the Search & Data Extraction and Knowledge & Memory categories on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Agentic RAG for any scenario. Customize sources, depth, and
Use Cases
Maintainer
Works with
Installation
Manual Installation
npx deepwideresearchConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
How to Set Up and Use Deepwideresearch
DeepWideResearch is an agentic RAG (Retrieval-Augmented Generation) MCP server that lets you control both the depth of reasoning and the breadth of information sources for any research task. It integrates with pluggable search engines (Tavily, Exa) and supports hot-swappable LLM backends (OpenAI, Claude, private models via OpenRouter), enabling everything from quick 10-second customer service lookups to comprehensive 5-minute enterprise research reports. Developers and analysts can self-host the full stack or consume it via MCP to build intelligent research workflows with fine-grained cost and quality tradeoffs.
Prerequisites
- Python 3.9+ for the backend research engine
- Node.js 18+ for the optional chat frontend
- An OpenRouter API key (OPENROUTER_API_KEY) for LLM access
- At least one search API key: Tavily (TAVILY_API_KEY) or Exa (EXA_API_KEY)
- Docker and Docker Compose (optional, for containerized deployment)
Clone the repository
Download the DeepWideResearch source code from GitHub to your local machine.
git clone https://github.com/puppyone-ai/DeepWideResearch
cd DeepWideResearchConfigure backend environment variables
Copy the example environment file for the backend and fill in your API keys. At minimum you need an OpenRouter key and at least one search engine key.
cp deep_wide_research/env.example deep_wide_research/.env
# Edit .env and set:
# OPENROUTER_API_KEY=your_openrouter_api_key
# TAVILY_API_KEY=your_tavily_api_key
# EXA_API_KEY=your_exa_api_keyInstall backend dependencies and start the server
Create a Python virtual environment, install the required packages, and start the MCP backend server.
cd deep_wide_research
python -m venv deep-wide-research
source deep-wide-research/bin/activate # Windows: deep-wide-research\Scripts\activate
pip install -r requirements.txt
python main.pyLaunch the frontend chat interface (optional)
For a browser-based chat UI, configure and run the Next.js frontend separately.
cp chat_interface/env.example chat_interface/.env.local
# Edit .env.local with your settings
cd chat_interface
npm install
npm run dev
# Visit http://localhost:3000Deploy with Docker (alternative)
For a fully containerized deployment of both backend and frontend in one step, use Docker Compose.
docker-compose up -dDeepwideresearch Examples
Client configuration
Register DeepWideResearch as an MCP server. Adjust the path to match where you cloned the repository.
{
"mcpServers": {
"deepwideresearch": {
"command": "python",
"args": ["/path/to/DeepWideResearch/deep_wide_research/main.py"],
"env": {
"OPENROUTER_API_KEY": "your_openrouter_api_key",
"TAVILY_API_KEY": "your_tavily_api_key",
"EXA_API_KEY": "your_exa_api_key"
}
}
}
}Prompts to try
Use these prompts to leverage the depth and breadth controls of DeepWideResearch.
- "Do a quick search to find the current pricing for Supabase Pro plans"
- "Research the latest advances in transformer architecture efficiency — go deep and cover at least 10 sources"
- "Give me a comprehensive market analysis of the enterprise AI platform space in 2025"
- "Find and summarize the top 5 academic papers on causal inference from the last two years"Troubleshooting Deepwideresearch
Research returns no results or fails immediately
Verify that at least one search engine API key (TAVILY_API_KEY or EXA_API_KEY) is set in your .env file and that the key is active. Test your Tavily key with `curl -H 'x-api-key: YOUR_KEY' https://api.tavily.com/search -d '{"query":"test"}'`.
OpenRouter API calls fail with authentication errors
Ensure OPENROUTER_API_KEY is set correctly in deep_wide_research/.env. Obtain a key from https://openrouter.ai/keys and confirm your account has credits. The key should start with `sk-or-`.
Docker Compose fails to start containers
Ensure Docker Desktop is running and both .env files are populated before running `docker-compose up`. Check logs with `docker-compose logs -f` to identify which service is failing and which environment variable is missing.
Frequently Asked Questions about Deepwideresearch
What is Deepwideresearch?
Deepwideresearch is a Model Context Protocol (MCP) server that agentic rag for any scenario. customize sources, depth, and width It connects AI assistants to external tools and data sources through a standardized interface.
How do I install Deepwideresearch?
Follow the installation instructions on the Deepwideresearch GitHub repository. Clone the repo, install dependencies, and add the server config to your AI client.
Which AI clients work with Deepwideresearch?
Deepwideresearch works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is Deepwideresearch free to use?
Yes, Deepwideresearch is open source and available under the Apache-2.0 license. You can use it freely in both personal and commercial projects.
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