Local Deep Research：本地深度研究助手

~95% on SimpleQA (e.g. Qwen3.6-27B on a 3090). Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - …

Local Deep Research AI-powered research assistant for deep, agentic research Performs deep, agentic research using multiple LLMs and search engines with proper citations 🧪 First open-source project — fully-local on a single RTX 3090 (Qwen3.6-27B) — to report ~95% SimpleQA (n=500) and 77% xbench-DeepSearch (n=100) on local hardware. See the r/LocalLLaMA announcement and the benchmark dataset. ▶️ Watch Review by The Art Of The Terminal 🚀 What is Local Deep Research? AI research assistant you control. Run locally for privacy, use any LLM and build your own searchable knowledge base. You own your data and see exactly how it works. ⚡ Quick Start Option 1: Docker Run (Linux) # Step 1: Pull and run Ollama docker run -d -p 11434:11434 --name ollama ollama/ollama docker exec ollama ollama pull gpt-oss:20b # Step 2: Pull and run SearXNG for optimal search results docker run -d -p 8080:8080 --name searxng searxng/searxng # Step 3: Pull and run Local Deep Research # (the URL line pins SearXNG's address AND marks it operator-approved — # private/localhost engine URLs are otherwise blocked by default since # v1.10.3. The URL becomes read-only in the web UI; docs/SearXNG-Setup.md # lists the alternatives, e.g. an origin allowlist.) docker run -d --network host \ --name local-deep-research \ --volume "deep-research:/data" \ -e LDR_DATA_DIR=/data \ -e LDR_SEARCH_ENGINE_WEB_SEARXNG_DEFAULT_PARAMS_INSTANCE_URL=http://localhost:8080 \ localdeepresearch/local-deep-research Mac / Windows / WSL2 users: --network host only works on native Linux. On Docker Desktop it silently fails to publish port 5000 and leaves localhost pointing at the LDR container itself (so it can't reach Ollama/SearXNG). Use Option 2 below, or see the Windows/WSL2 FAQ entry for a working docker run recipe. Option 2: Docker Compose CPU-only (all platforms): curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d With NVIDIA GPU (Linux): curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && \ curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.gpu.override.yml && \ docker compose -f docker-compose.yml -f docker-compose.gpu.override.yml up -d Open http://localhost:5000 after ~30 seconds. For GPU setup, environment variables, and more, see the Docker Compose Guide. Option 3: pip install pip install local-deep-research python -m local_deep_research.web.app # starts the web UI on http://localhost:5000 You'll also need Ollama (or any OpenAI-compatible LLM endpoint) and SearXNG running — see the pip install guide for the full recipe. Works on Windows, macOS, and Linux. SQLCipher encryption is included via pre-built wheels — no compilation needed. PDF export on Windows requires Pango (setup guide). Development fallback: LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true permits plaintext SQLite only when SQLCipher is unavailable or nonfunctional; working SQLCipher still takes priority. The fallback does not verify account passwords and is unsuitable for shared deployments. See fallback limitations. Detailed install guides: Docker · Docker Compose · pip · Unraid · full install reference Older CPU (x86-64)? LDR needs an AVX-capable CPU — Intel Sandy Bridge / AMD Bulldozer (2011) or newer. Several scientific Python dependencies (pandas, scikit-learn) ship wheels that crash with Illegal instruction on older CPUs. ARM64 (aarch64) is fully supported. Every release is smoke-tested against this floor, including AVX-without-AVX2 CPUs (#4480). 🏗️ How It Works Research You ask a complex question. Local Deep Research (LDR): Does the research for you automatically Searches across web, academic papers, and your own documents Synthesizes everything into a report with proper citations Choose the research strategy that fits: quick pipeline modes for fast facts, or fully agentic deep research for complex analysis and academic work. LangGraph Agent Strategy — An autonomous agentic research mode where the LLM decides what to search, which specialized engines to use (arXiv, PubMed, Semantic Scholar, etc.), and when to synthesize. It adaptively switches between search engines based on what it finds and collects significantly more sources than pipeline-based strategies — this is the strategy behind the ~95% SimpleQA result above. Select langgraph-agent in Settings. Build Your Knowledge Base flowchart LR R[Research] --> D[Download Sources] D --> L[(Library)] L --> I[Index & Embed] I --> S[Search Your Docs] S -.-> R Loading Every research session finds valuable sources. Download them directly into your encrypted library — academic papers from ArXiv, PubMed articles, web pages. LDR extracts text, indexes everything, and makes it searchable. Next time you research, ask questions across your own documents and the live web together. Your knowledge compounds over time. 🛡️ Security flowchart LR U1[User A] --> D1[(Encrypted DB)] U2[User B] --> D2[(Encrypted DB)] Loading Your data stays yours. Each user gets their own isolated SQLCipher database encrypted with AES-256, with the key derived from your password. Your password is never stored — login works by attempting to decrypt your database, so the database files on their own are unusable to anyone who obtains them. Per-user LLM API keys live encrypted inside the same personal database rather than in a shared server-level store. The Docker setup ships with cap_drop: ALL, no-new-privileges, and a non-root runtime, with the bundled Ollama and SearXNG images pinned by digest. With a model running in the bundled Ollama, inference runs on your own hardware. LDR judges locality mainly by provider and endpoint address, not by where the model runs, so a local endpoint that relays to a hosted model (an Ollama cloud model, or a local LiteLLM or other OpenAI-compatible proxy in front of a hosted API) can still send your prompts off the machine without being detected. A self-hosted SearXNG s