Deepwideresearch

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:

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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:

cp deep_wide_research/env.example deep_wide_research/.env

1. Edit the copied .env and set your keys:

# 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 and Exa.

1. Set up the environment:

cd deep_wide_research
python -m venv deep-wide-research
source deep-wide-research/bin/activate
pip install -r requirements.txt

1. Start the backend server:

python main.py

Frontend

1. Copy the env template:

cp chat_interface/env.example chat_interface/.env.local

1. Install dependencies and start the dev server:

cd chat_interface
npm install
npm run dev

1. Open the app:

Open http://localhost:3000 – Start researching in seconds.

Docker (Production)

docker-compose up -d

How We Compare

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Deep Wide Research Archietecture

License

This project is licensed under the Apache License, Version 2.0. See the LICENSE file for details.

Copyright (c) 2025 PuppyAgent and contributors.