Local Deep Research

🚀 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). If you encounter issues with encryption, set export LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true to use standard SQLite instead.

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