Deep Research is an AI-powered research assistant that performs iterative, deep research on any topic by combining search engines, web scraping, and large language models.
The tool addresses the challenge of conducting thorough research by automating the iterative refinement process. It works by generating targeted search queries based on research goals, processing results through web scraping and content extraction, and then recursively diving deeper based on findings. The system uses large language models to intelligently generate follow-up questions and determine research direction, while handling multiple searches and result processing concurrently for efficiency. Users control how wide and deep the research goes through configurable breadth and depth parameters, and the tool produces comprehensive markdown reports with findings and sources.
The project is designed for developers who want a straightforward, minimal implementation of a deep research agent that remains easy to understand and extend. It suits scenarios where you need automated research capabilities without complex infrastructure, though it requires API keys for Firecrawl and OpenAI. The tool supports flexibility in model choice, allowing use of OpenAI's o3-mini by default, DeepSeek R1 via Fireworks, or local LLM endpoints through custom configuration. The codebase is intentionally kept under five hundred lines of code to remain accessible for modification and building upon.
Development activity shows consistent engagement with the codebase through regular commits and active maintenance of core functionality. The project maintains responsiveness to user needs through environment variable configuration options that address practical constraints like rate limiting and concurrency management. Documentation includes clear setup instructions for both Node.js and Docker environments, along with usage guidance for different model providers and custom endpoints.
