Open-Source Deep Research Initiatives

An Open-Source Alternative to OpenAI's Deep Research: Open Deep Research

After OpenAI introduced Deep Research, many open source versions comes up. Actually, there was already some built agents similar to deep research.

Huggingface published a blog post about that and they also run some agents for it.

Other great open implementations of Deep Research emerged from the community, specifically from

• dzhng,
• assafelovic(This even exists before Deep Research),
• nickscamara,
• jina-ai and
• mshumer.

Each of these implementations use different libraries for indexing data, browsing the web and querying LLMs.

In this article, I want to mention Nicolas Silberstein Camara for his great action against Openai Deep Research Premium. Here is the details and tutorial.

Open-Source Deep Research: Overview

Open Deep Research is an open-source clone of OpenAI's Deep Research experiment. Unlike OpenAI's proprietary model, this project leverages Firecrawl’s extract + search technology combined with a reasoning model to conduct deep research across the web.

Open-Source Deep Research: Key Features

• Firecrawl Extract + Search

  • Feeds real-time data to the AI via search.
  • Extracts structured data from multiple websites.

• Next.js App Router

  • Uses React Server Components (RSCs) for efficient rendering.
  • Supports server-side rendering for performance optimization.

• AI SDK Integration

  • Supports multiple LLM providers:
  • OpenAI (default: gpt-4o)
  • Anthropic, Cohere, DeepSeek, and more.

• Advanced UI Components

  • Styled with Tailwind CSS.
  • Uses shadcn/ui with Radix UI for flexible component handling.

• Data Persistence

  • Uses Vercel Postgres (Neon) for chat history and user data.
  • Stores files efficiently with Vercel Blob.

• Authentication System

  • Implemented using NextAuth.js for secure user login.

Open Deep Research: Installation Guide (Run Locally)

1. Clone the Repository

Open your terminal and run:

git clone https://github.com/nickscamara/open-deep-research.git
cd open-deep-research

2. Install Dependencies

Install pnpm if not installed:

npm install -g pnpm

Then install all dependencies:

pnpm install

3. Set Up Environment Variables

You'll need to define environment variables in .env using the .env.example file as a reference.

To automatically configure environment variables:

vercel env pull

Or manually create a .env file and include:

OPENAI_API_KEY=your_openai_api_key
FIRECRAWL_API_KEY=your_firecrawl_api_key
AUTH_SECRET=your_auth_secret

⚠️ Do not commit the .env file to avoid exposing sensitive API keys.

4. Run Database Migrations

pnpm db:migrate

5. Start the Application

pnpm dev

Your app should now be running at:

🔗 http://localhost:3000

Alternative Deployment: One-Click Deploy to Vercel

If you prefer not to run it locally, you can deploy to Vercel in one click:

Model Providers

By default, the project uses OpenAI's GPT-4o.

However, it supports multiple LLM providers via Vercel's AI SDK, including:

• Anthropic (Claude)
• Cohere
• DeepSeek
• TogetherAI
• OpenRouter

Switching Models

Modify the .env file:

REASONING_MODEL=deepseek-reasoner
BYPASS_JSON_VALIDATION=true

Adding Model Dependencies

If you want to use a model other than GPT-4o, install the respective dependency.

DeepSeek AI Model

pnpm add @ai-sdk/deepseek

TogetherAI Model

pnpm add @ai-sdk/togetherai

🚨 Check TogetherAI rate limits:

🔗 Rate Limit Info

Reasoning Model Configuration

This project includes a reasoning model for structured outputs such as research analysis, data extraction, and document summarization.

Key Notes

• GPT-4o, o1, o3-mini → Natively support structured JSON outputs.
• DeepSeek & TogetherAI → Need BYPASS_JSON_VALIDATION=true.
• If no model is set, it defaults to o1-mini.
• If an invalid model is chosen, it falls back to o1-mini.

To use DeepSeek as the reasoning model, add this to .env:

REASONING_MODEL=deepseek-reasoner
BYPASS_JSON_VALIDATION=true

Open-Source Deep Research: Conclusion

Open Deep Research by Nicolas Silberstein Camara is a powerful, open-source alternative to OpenAI’s Deep Research.

It allows users to autonomously research the web, retrieve structured data, and leverage multiple AI models.

By following the steps above, you can:
✅ Run it locally

✅ Deploy it to Vercel

✅ Customize model providers

For the latest updates, visit the GitHub Repository.

Developer: Nicolas Silberstein Camara

GitHub Repository: Github

Demo: Live Demo