~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.
Local Deep Research is a self-hosted, privacy-focused AI research assistant that conducts multi-step agentic research by combining any supported LLM with more than ten search backends, including arXiv, PubMed, SearXNG, Brave, and local private document collections. Users interact through a web UI served on localhost, deployable via Docker, Docker Compose, or pip install. The system connects to Claude through Anthropic's API alongside other cloud providers and local inference runtimes such as Ollama and llama.cpp, treating all as interchangeable LLM backends. Research history and knowledge bases are stored in a SQLCipher-encrypted SQLite database, meaning all data stays on the user's hardware. A concrete benchmark result distinguishes the project: running Qwen3.6-27B on a single RTX 3090, it achieved roughly 95% accuracy on SimpleQA (n=500), claimed as the first fully local open-source system to reach that mark. Researchers, academics, and privacy-conscious developers who need cited, multi-source reports without sending queries to third-party services are the primary audience.
- ✓Open-source license (MIT)
- ✓Actively maintained (<30d)
- ✓Healthy fork ratio
- ✓Clear description
- ✓Topics declared
- ✓Mature repo (>1y old)
git clone https://github.com/LearningCircuit/local-deep-researchTools overview
# Local Deep Research
<div align="center">
[](https://github.com/LearningCircuit/local-deep-research/stargazers)
[](https://hub.docker.com/r/localdeepresearch/local-deep-research)
[](https://pypi.org/project/local-deep-research/)
[](https://trendshift.io/repositories/14116)
[](https://github.com/LearningCircuit/local-deep-research/commits/main)
[](https://github.com/LearningCircuit/local-deep-research/commits/main)
[](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks)
[](docs/SQLCIPHER_INSTALL.md)
<!-- Well-known security scanners that visitors will recognize -->
[](https://securityscorecards.dev/viewer/?uri=github.com/LearningCircuit/local-deep-research)
[](https://github.com/LearningCircuit/local-deep-research/security/code-scanning)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/semgrep.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/pre-commit.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/docker-publish.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/publish.yml)
[](https://discord.gg/ttcqQeFcJ3)
[](https://www.reddit.com/r/LocalDeepResearch/)
[](https://www.youtube.com/@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](https://www.reddit.com/r/LocalLLaMA/comments/1t1n6o8/we_are_finally_there_qwen3627b_agentic_search_957/) and the [benchmark dataset](https://huggingface.co/datasets/local-deep-research/ldr-benchmarks).
<a href="https://www.youtube.com/watch?v=pfxgLX-MxMY&t=1999">
▶️ Watch Review by The Art Of The Terminal
</a>
</div>
## 🚀 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
> **CPU requirement (x86-64):** 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](https://github.com/LearningCircuit/local-deep-research/issues/4480)).
**Option 1: Docker Run (Linux)**
```bash
# 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
docker run -d -p 5000:5000 --network host \
--name local-deep-research \
--volume "deep-research:/data" \
-e LDR_DATA_DIR=/data \
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](docs/faq.md#port-5000-not-accessible-on-windows) for a working `docker run` recipe.
**Option 2: Docker Compose**
CPU-only (all platforms):
```bash
curl -O https://raw.githubusercontent.com/LearningCircuit/local-deep-research/main/docker-compose.yml && docker compose up -d
```
With NVIDIA GPU (Linux):
```bash
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](docs/docker-compose-guide.md).
**Option 3: pip install**
```bash
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](docs/install-pip.md) 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](https://doc.courtbouillon.org/weasyprint/stable/first_steps.html)).
> If you encounter issues with encryption, set `export LDR_BOOTSTRAP_ALLOW_UNENCRYPTED=true` to use standard SQLite instead.
**Detailed install guides:** [Docker](docs/installation.md#docker) · [Docker Compose](docs/docker-compose-guide.md) · [pip](docs/install-pip.md) · [Unraid](docs/deployment/unraid.md) · [full install reference](docs/installation.md)
## 🏗️ How It Works
### Research
You ask a complex question. 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 from 20+ research strategies for quick facts, deep analysis, or academic research.
**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
```mermaid
flowchart LR
R[Research] --> D[Download Sources]
D --> L[(Library)]
L --> I[Index & Embed]
I --> S[Search Your Docs]
S -.-> R
```
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
<div align="center">
<!-- Static Analysis (additional scanners beyond CodeQL/Semgrep) -->
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/devskim.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/bearer.yml)
<!-- Dependency & Secrets Scanning -->
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/osv-scanner.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/npm-audit.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/retirejs.yml)
<!-- Container Security -->
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/container-security.yml)
[](https://github.com/LearningCircuit/local-deep-research/actions/workflows/dockle.yml)
[. Supports all local and cloud LLMs (llama.cpp, Ollama, Google, ...). 10+ search engines - arXiv, PubMed, your private documents. Everything Local & Encrypted. It has 8.5k GitHub stars and was last updated today.
How do I install local-deep-research?
+
You can install local-deep-research by cloning the repository (https://github.com/LearningCircuit/local-deep-research) or following the README instructions on GitHub. ClaudeWave also provides quick install blocks on this page.
Is LearningCircuit/local-deep-research safe to use?
+
Our security agent has analyzed LearningCircuit/local-deep-research and assigned a Trust Score of 100/100 (tier: Verified). See the full breakdown of passed checks and flags on this page.
Who maintains LearningCircuit/local-deep-research?
+
LearningCircuit/local-deep-research is maintained by LearningCircuit. The last recorded GitHub activity is from today, with 315 open issues.
Are there alternatives to local-deep-research?
+
Yes. On ClaudeWave you can browse similar tools at /categories/tools, sorted by popularity or recent activity.
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