OpenAI's Deep Research is great, but it has two problems: it's expensive, and you have to send your research topics and data to someone else's servers.
local-deep-research answers: no need to send anything anywhere—a single 3090 is enough.
7,545 stars, up 2,046 this week. Running Qwen3.6-27B on a single RTX 3090, SimpleQA score hit approximately 95%.
What 95% Actually Means
SimpleQA is a benchmark from OpenAI specifically designed to test a model's "simple but requires factual retrieval" ability. Not reasoning questions—"do you know this fact" questions.
What's the context for 95%? OpenAI's own o3 scored 93.6% on this benchmark (per OpenAI's official system card).
Of course, direct comparison requires caution. local-deep-research isn't using a raw model—it wraps search augmentation, multi-engine aggregation, and answer verification in a full pipeline. But even so, reaching this level with consumer-grade hardware is itself a signal.
Workflow Breakdown
The core of this project isn't the model—it's the engineering of the research process. It does these things:
Multi-search engine aggregation. Supports 10+ search engines, including arXiv, PubMed, and your own private documents. Not just calling a few APIs—it does result deduplication, relevance ranking, and cross-verification.
Iterative research. Doesn't give an answer after one search. Like a human doing research: search first, find key information, then dig deeper specifically, and finally synthesize the output.
Local encryption. All data stored locally, search through encrypted channels. For industries sensitive to data privacy—healthcare, legal, finance—this is a must-have.
Model-agnostic. Supports llama.cpp, Ollama, Google, OpenAI—almost all local and cloud LLMs. You can switch freely based on hardware conditions.
My Use Cases
I tested it with two scenarios:
First, technical research—"comparing vector vs. non-vector retrieval in RAG systems." It searched arXiv for related papers, aggregated multiple sources, and output a structured comparison report. Quality didn't lose to what I'd write myself in two hours.
Second, market research—"the AI coding tool landscape in Q2 2026." This was slightly weaker, because real-time data coverage isn't as good as professional paid tools. But for initial reconnaissance, it's perfectly adequate.
Shortcomings
Don't get carried away by the 95% number.
- Speed. A full research cycle on a 3090 takes minutes to over ten minutes, depending on query complexity. Cloud Deep Research is also slow, but it uses more powerful models.
- No multimodal. Currently text-only. Charts, images in PDFs, video content—it can't handle these.
- Configuration门槛. While the README is well-written, getting the full pipeline running still requires some knowledge of Ollama/llama.cpp configuration. It's not a "one-click install" experience.
- Knowledge cutoff. Local models have a training data cutoff date. While search augmentation helps, it's not as responsive to "things that happened today" as cloud services.
When to Use It
Use it when:
- Research involves sensitive data that can't be sent to the cloud
- You need to run the same type of research repeatedly, and cloud costs add up
- Data sovereignty is a requirement (academic institutions, government projects)
Don't use it when:
- You need the latest real-time information (e.g., "what did company X release today")
- You need multimodal analysis
- You don't have 3090-class hardware—running 27B models on CPU or low-memory GPUs will be a bad experience
My Take
local-deep-research represents a mature direction for local AI workflows: no longer "can it run locally," but "can the local results compete with cloud."
It's not a complete replacement for Deep Research. But for specific scenarios, it's already good enough.
And the trend is clear: as 27B-class open-source models get stronger, the quality of local deep research will only continue to rise.
Primary Sources:
- GitHub - LearningCircuit/local-deep-research — 7,545 stars
- OpenAI o3 System Card — SimpleQA benchmark data
- GitHub Trending Weekly — Python trending