Local Deep Research is what Gemini Deep Research wishes it could be on your laptop
LearningCircuit/local-deep-research released v1.6.9 on May 2, 2026 and is currently trending hard on GitHub at 564 stars/day. It's an OSS deep-research agent that runs entirely on your machine if you want it to — Ollama, LM Studio, llama.cpp loading Llama 3 / Mistral / Gemma / DeepSeek / Qwen, with an optional 100+ cloud model fallback via OpenRouter.
The search backend is the part nobody else has stitched together this cleanly: arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, Wayback Machine, GitHub, Elasticsearch, The Guardian, plus premium options for Tavily and Brave. Twenty-plus research strategies — quick summaries, detailed analysis, full-report generation. It builds a searchable knowledge base from everything it downloads, so subsequent queries don't restart from zero.
The number to be slightly skeptical about: ~95% on SimpleQA. The maintainer notes this is preliminary on limited samples with GPT-4.1-mini and SearXNG using the focused-iteration strategy. So 95% is a cherry-picked configuration, not a frontier-model claim. Still — Gemini Deep Research is a $20/month Google product, OpenAI Deep Research is gated behind ChatGPT Pro, and Perplexity Deep Research charges by query. All three live on managed infrastructure you don't control.
The structural read: Deep Research as a category is six months old and already has a credible OSS competitor. The privacy angle (PubMed queries against local Llama, no logs sent to a Mountain View data center) is the wedge that will turn this into the default for academic and corporate-research use cases. If your research questions touch patient data, M&A targets, or legal discovery, Local Deep Research isn't a hobby project — it's the only acceptable answer.
Repo: https://github.com/LearningCircuit/local-deep-research
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The search backend is the part nobody else has stitched together this cleanly: arXiv, PubMed, Semantic Scholar, Wikipedia, SearXNG, Wayback Machine, GitHub, Elasticsearch, The Guardian, plus premium options for Tavily and Brave. Twenty-plus research strategies — quick summaries, detailed analysis, full-report generation. It builds a searchable knowledge base from everything it downloads, so subsequent queries don't restart from zero.
The number to be slightly skeptical about: ~95% on SimpleQA. The maintainer notes this is preliminary on limited samples with GPT-4.1-mini and SearXNG using the focused-iteration strategy. So 95% is a cherry-picked configuration, not a frontier-model claim. Still — Gemini Deep Research is a $20/month Google product, OpenAI Deep Research is gated behind ChatGPT Pro, and Perplexity Deep Research charges by query. All three live on managed infrastructure you don't control.
The structural read: Deep Research as a category is six months old and already has a credible OSS competitor. The privacy angle (PubMed queries against local Llama, no logs sent to a Mountain View data center) is the wedge that will turn this into the default for academic and corporate-research use cases. If your research questions touch patient data, M&A targets, or legal discovery, Local Deep Research isn't a hobby project — it's the only acceptable answer.
Repo: https://github.com/LearningCircuit/local-deep-research
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