DIY deep research that actually runs on your GPU
An open-source research agent that claims ~95% SimpleQA accuracy using local models—no API keys, no data exfiltration.

What it does
Local Deep Research is a self-hosted research assistant that takes a complex question, searches across web and academic sources (arXiv, PubMed, Semantic Scholar, etc.), and synthesizes a cited report. It supports both local LLMs via Ollama/llama.cpp and cloud providers, with 20+ research strategies ranging from quick facts to deep agentic exploration.
The interesting bit
The project explicitly targets the “can I actually run this at home?” crowd. It claims to be the first open-source tool hitting ~95% on SimpleQA (n=500) with a single RTX 3090 running Qwen3.6-27B. The langgraph-agent strategy is the engine behind that number: an autonomous agent that decides which search engines to query, when to keep digging, and when to synthesize—collecting more sources than fixed pipelines.
Key highlights
- Compounding knowledge base: Download sources from any research session into an encrypted personal library; future queries search your accumulated documents plus the live web
- Per-user AES-256 encryption via SQLCipher with no password recovery—admins can’t read your data
- Extensive security posture: Cosign-signed Docker images, plus automated scanning via CodeQL, Semgrep, OWASP ZAP, OSV-Scanner, and others
- Broad LLM support: Ollama, llama.cpp, OpenAI-compatible APIs, Google, Anthropic, etc.
- 10+ search engines including SearXNG, Brave, arXiv, PubMed; also indexes your private documents
Caveats
- Docker
--network hostfails silently on macOS/Windows Docker Desktop, breaking port publishing and localhost resolution; the README warns you need workarounds - The ~95% SimpleQA claim is specifically for the
langgraph-agentstrategy with Qwen3.6-27B; your mileage will vary with other models and strategies - In-memory credentials remain plaintext during active sessions, which the documentation candidly notes is an industry-wide limitation, not a solved problem
Verdict
Worth a look if you want research automation without shipping your queries to OpenAI or Perplexity. Skip it if you need turnkey SaaS reliability or don’t want to babysit Ollama and SearXNG.
Frequently asked
- What is LearningCircuit/local-deep-research?
- An open-source research agent that claims ~95% SimpleQA accuracy using local models—no API keys, no data exfiltration.
- Is local-deep-research open source?
- Yes — LearningCircuit/local-deep-research is open source, released under the MIT license.
- What language is local-deep-research written in?
- LearningCircuit/local-deep-research is primarily written in Python.
- How popular is local-deep-research?
- LearningCircuit/local-deep-research has 8.8k stars on GitHub and is currently accelerating.
- Where can I find local-deep-research?
- LearningCircuit/local-deep-research is on GitHub at https://github.com/LearningCircuit/local-deep-research.