Dewey.py: Rebuilding Deep Research with Open Models

Deep Research is an AI-powered solution that automates multi-step research processes, mimicking the work of a seasoned research analyst. In this repo, we demonstrate how to build Dewey, your own deep research agent, using only open source. This way, you can see how it works under the hood, customize the process based on your own needs, and deploy it on your own infrastructure.

To give you a sense of the style and quality of Dewey, here are a few sample reports it has generated:

• Technology: Differences between Microsoft’s and Google’s recent quantum breakthroughs.
• Healthcare: Advances in gene therapy for Alzheimer’s disease.
• Travel: A two-day itinerary for exploring New York City.

Each research takes about 5 to 10 minutes to run and costs less than 10 cents.

Installation

pip install -r requirements.txt

Create a .env file and add:

UBICLOUD_API_KEY=your_api_key_here

You can obtain an API key by signing up on Ubicloud and generating one from the "AI Inference" page.

Usage

Run research:

python dewey.py "Artificial Intelligence"

Set research depth (default is 3):

python dewey.py "Quantum Computing" --depth=2

Resume from saved state:

python dewey.py "Artificial Intelligence" --resume="saved_state.json"

By default, we use DuckDuckGo as the search engine, but it may encounter rate limits. To avoid this, you can switch to Tavily search. To do so, register at tavily.com to obtain an API key, then add TAVILY_API_KEY=your_api_key_here to the .env file, and run

python dewey.py "Artificial Intelligence" --search_engine="tavily"

Output

• PDF Report: Structured report with summary and references.
• JSON State: Save progress for resuming research.

License

MIT License.

🚀 Happy researching!