The core competence of Local Deep Research, an open source AI research assistant, lies in its fully localized operation and deep research capabilities. The tool accomplishes complex research tasks without relying on cloud services by integrating local large language models (e.g., gemma3:12b hosted by Ollama) with comprehensive search capabilities. It offers significant advantages in terms of data privacy protection over cloud-based AI tools and is particularly suitable for handling sensitive research materials.
The technical architecture supports a variety of key functions: automatic retrieval of academic databases (arXiv, PubMed), Wikipedia and web content; generation of structured reports with regular citations; and retrieval-enhanced generation (RAG) analysis of local documents. The installation process is standardized through the Python environment, and the SearXNG search service can be rapidly deployed with Docker to enhance web search.
In practice, users can choose the quick summary mode to get instant answers (seconds response), or start multiple rounds of iterative research (default 2 rounds) to generate a full Markdown report with table of contents, chapters. Hardware configuration is recommended to use GPU acceleration, the quality of the report increases linearly with the number of iterations and the number of results.
This answer comes from the articleLocal Deep Research: a locally run tool for generating in-depth research reportsThe
