Deep Research is a lightweight AI research agent designed to autonomously investigate complex topics through iterative web exploration and reasoning. The project combines search engines, web scraping, and large language models to progressively refine its understanding of a user’s query and dive deeper over multiple cycles. Its core goal is to provide the simplest possible implementation of a deep research workflow so developers can study and extend agent behavior without dealing with large, opaque codebases. The system exposes parameters such as breadth and depth to control how widely and how deeply the agent explores information sources. It is intentionally kept compact, with a codebase under roughly 500 lines, making it highly approachable for experimentation and learning. The architecture demonstrates how modern agent pipelines can continuously gather evidence, extract learnings, and adjust research direction over time.
Features
- Iterative autonomous research workflow
- Integrated search and web scraping pipeline
- Adjustable breadth and depth parameters
- Compact educational codebase
- LLM-driven topic refinement
- Structured learnings and direction outputs