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LearningCircuit
local-deep-research
Python2025–2025
LearningCircuit / local-deep-research

local-deep-research

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

AI & Machine LearningSecurity & PrivacySelf-hostedLearning & ReferenceProductivityRobotics & IoTDevOps & CloudAutomation & Workflows
Explore on GitHubRead Hacker News discussions
4Distinct people
7Mentions
1Explicit recommenders
9.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted.

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

I love this one: https://github.com/LearningCircuit/local-deep-research I tied it together with qwen3 30b thinking. Very easy to get it up and running, but lots of the numbers…

incomingpain used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:03:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month7 dated mentions

Observed mentions: 2025-02-20 15:10:50 UTC to 2025-11-03 14:15:02 UTC.

012342025-02-01 00:00:00 UTC to 2025-03-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsFeb 20252025-03-01 00:00:00 UTC to 2025-04-01 00:00:00 UTC (exclusive): 4 mentions; 1 submissions, 3 comments2025-04-01 00:00:00 UTC to 2025-05-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-05-01 00:00:00 UTC to 2025-06-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-06-01 00:00:00 UTC to 2025-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsJun 20252025-07-01 00:00:00 UTC to 2025-08-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-08-01 00:00:00 UTC to 2025-09-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-09-01 00:00:00 UTC to 2025-10-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-10-01 00:00:00 UTC to 2025-11-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-11-01 00:00:00 UTC to 2025-12-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsNov 2025

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Feb 20252025-02-01 00:00:00 UTC2025-03-01 00:00:00 UTC101
Mar 20252025-03-01 00:00:00 UTC2025-04-01 00:00:00 UTC413
Apr 20252025-04-01 00:00:00 UTC2025-05-01 00:00:00 UTC101
May 20252025-05-01 00:00:00 UTC2025-06-01 00:00:00 UTC000
Jun 20252025-06-01 00:00:00 UTC2025-07-01 00:00:00 UTC000
Jul 20252025-07-01 00:00:00 UTC2025-08-01 00:00:00 UTC000
Aug 20252025-08-01 00:00:00 UTC2025-09-01 00:00:00 UTC000
Sep 20252025-09-01 00:00:00 UTC2025-10-01 00:00:00 UTC000
Oct 20252025-10-01 00:00:00 UTC2025-11-01 00:00:00 UTC000
Nov 20252025-11-01 00:00:00 UTC2025-12-01 00:00:00 UTC101

What Hacker News says

7 sources
incomingpain·Nov 3, 2025Recommendation phrase
I love this one: https://github.com/LearningCircuit/local-deep-research I tied it together with qwen3 30b thinking. Very easy to get it up and running, but lots of the numbers are shockingly low. You need to boost iterations and context. Especially easy if you already run searxng locally. I havent finished tuning the actual settings, but for the detailed report it'll take ~20 minutes and so far has given pretty good results. Similar to openai's deep research. Mine often has ~100 sources. But something I have noticed. It didnt seem to me the model was important. The magic was moreso in the project. Getting deep with higher iterations and more results.
In Tongyi DeepResearch – open-source 30B MoE Model that rivals OpenAI DeepResearch
learningcircuit·Apr 17, 2025
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
Read the rest
. 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
In Local Deep Research 0.2.0: AI-powered research assistant SearXNG, ArXiv, PubMed
learningcircuit·Mar 23, 2025
I wanted to share some updates on Local Deep Research since posting here 12 days ago. Thanks to everyone who gave feedback and suggestions - the project has improved significantly with your input. Recent improvements: - Better inline citation: Sources from PubMed, arXiv, Wikipedia, etc. are now properly cited directly in the text - Improved report structure: Reports now have better organization with logical sections and clearer source attribution - Added support for multiple research domains: Works well across scientific, historical, economic, and technical topics - Enhanced search iterations: Now performs multiple rounds of research with follow-up questions for deeper analysis - More flexible LLM integration: Works with pretty much any model (local via Ollama or cloud-based) - Expanded search engine options: Easy to add new sources for s
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pecialized research For those who mentioned concerns about report quality and organization - we've made significant improvements in this area. The citation tracking now provides much better provenance information throughout the research pipeline. I'd also like to thank HashedViking who joined as a contributor and has been improving the UI/UX side of things. We're committed to keeping this as a truly local, privacy-focused tool that doesn't rely on expensive APIs. For anyone interested in contributing, we're looking for help with: 1. Further improving report organization 2. More local search engines and sources 3. Documentation and examples 4. UI/UX enhancements 5. Testing with different models and research domains The project is at: https://github.com/LearningCircuit/local-deep-research/ What features would be most useful to you in a research tool like this? We're particularly interested in ideas for better knowledge organization and making the research outputs more valuable.
In Local Deep Research – ArXiv, wiki and other searches included
mentalgear·Mar 11, 2025
I applaud the effort for the local (lo-fi) space ! Yet, reading over the example linked in the docs (which does not seem cheery-picked, kudos for that!), my impression is that the document is a rather messy outcome [1]. I think what's missing is one (or more) step in-between, possible a graph database (eg[2]), which the LLM can place all it's information in, see relevant interconnections, query to question itself, and then generate the final report. (maybe the final report could be an interactive HTML file that the user can ask questions, or edit themselves). There's also a similar open-deep research tool called onyx [2], with I think has better UI/UX albeit not local. Maybe the author could consider porting this to local instead of rolling and maintaining another deep-research tool themselves ? I'm saying this, not because I think it's n
Read the rest
ot a good project, but because there are a ton of open deep-research projects which I'm afraid will just fizzle out, and would be better if people would join forces working on those aspects they care most about (e.g. local aspect, or RAG strategies, etc) . [1] https://github.com/LearningCircuit/local-deep-research/blob/... [2] "In-Browser Graph RAG with Kuzu-WASM and WebLLM" https://news.ycombinator.com/item?id=43321523 [3] https://github.com/onyx-dot-app/onyx
In Local Deep Research – ArXiv, wiki and other searches included

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