Local Deep Research 0.2.0: AI-powered research assistant SearXNG, ArXiv, PubMed

Local Deep Research is an open-source tool that enables deep, iterative research using AI without sending your queries to external services. It combines language models with specialized search capabilities to conduct multi-stage research that builds on initial findings. Key capabilities include:

Discovering connections between sources that simple search misses Producing structured reports with proper citations, not just collected snippets Integrating your personal document library as a research source Cross-referencing your documents with web sources in a single research flow Easily switching between search engines including PubMed, arXiv, and SearXNG Working entirely offline with local LLMs (via Ollama)

SearXNG integration is particularly powerful as it provides a high-quality, privacy-focused search experience without requiring API keys. The 0.2.0 release adds a redesigned UI, unified settings database, parallel search for faster results, and improved error recovery. With over 600 commits and 5 core contributors, the project is actively growing, and we're looking for more contributors to join the effort. Getting involved is straightforward even for those new to the codebase. More details: https://www.reddit.com/r/LocalDeepResearch/ GitHub: https://github.com/LearningCircuit/local-deep-research