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
docker run -d --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 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 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

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)]

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. Or run fully local with Ollama + SearXNG and nothing ever leaves your machine.

In-memory credentials: Like any application that uses secrets at runtime, credentials are held in process memory during active sessions — mitigated with session-scoped credential lifetimes and core dump exclusion. See the Security Policy for the full threat model.

Supply Chain Security: Docker images are signed with Cosign using GitHub's keyless OIDC flow, include SLSA provenance attestations, and ship with attested SPDX SBOMs. See Verifying images and SBOMs for the step-by-step verification commands.

Security Transparency: Scanner suppressions are documented with justifications in Security Alerts Assessment, Scorecard Compliance, Container CVE Suppressions, and SAST Rule Rationale. Some alerts (Dependabot, code scanning) can only be dismissed or are very difficult to suppress outside the GitHub Security tab, so the files above do not cover every dismissed finding.

Detailed Architecture → | Security Policy → | Security Review Process →

🔒 Privacy & Data

Local Deep Research contains no telemetry, no analytics, and no tracking. We do not collect, transmit, or store any data about you or your usage. No analytics SDKs, no phone-home calls, no crash reporting, no external scripts. Usage metrics stay in your local encrypted database.

The only network calls LDR makes are ones you initiate: search queries (to engines you configure), LLM API calls (to your chosen provider), and notifications (only if you set up Apprise).

Since we don't collect any usage data, we rely on you to tell us what works, what's broken, and what you'd like to see next — bug reports, feature ideas, and even which features you love or never use all help us improve LDR.

📊 Benchmarks

Headline results from the community benchmarks using the langgraph-agent strategy with Serper search, fully local via Ollama:

Model	SimpleQA	xbench-DeepSearch
Qwen3.6-27B	95.7% (287/300)	77.0% (77/100)
Qwen3.5-9B	91.2% (182/200)	59.0% (59/100)
gpt-oss-20B	85.4% (295/346)	–

Caveats: small samples, LLM-grader noise, and SimpleQA contamination risk on newer base models.

Picking a local model? The same community-maintained dataset tracks accuracy across models, search engines, and research strategies — the fastest way to see which Ollama / LM Studio / llama.cpp models actually work well for deep research before you download multi-GB weights. Browse the full leaderboard on Hugging Face →

Submit your own results → (contributors are listed in CONTRIBUTORS.md), or run benchmarks locally →.

✨ Key Features

🔍 Research Modes

Quick Summary - Get answers in 30 seconds to 3 minutes with citations
Detailed Research - Comprehensive analysis with structured findings
Report Generation - Professional reports with sections and table of contents
Document Analysis - Search your private documents with AI

🛠️ Advanced Capabilities

LangChain Integration - Use any vector store as a search engine
REST API - Authenticated HTTP access with per-user databases
Benchmarking - Test and optimize your configuration
Analytics Dashboard - Track costs, performance, and usage metrics
Journal Quality System - Automatic journal reputation scoring with 212K+ indexed sources, predatory detection, and quality dashboard. Powered by OpenAlex (CC0), DOAJ (CC0), and Stop Predatory Journals (MIT). See the v1.6.0 announcement.
Real-time Updates - WebSocket support for live research progress
Chat Mode - Multi-turn research conversations with streaming progress and accumulated context across turns
Export Options - Download results as PDF or Markdown
Research History - Save, search, and revisit past research
Adaptive Rate Limiting - Intelligent retry system that learns optimal wait times
Keyboard Shortcuts - Navigate efficiently (ESC, Ctrl+Shift+1-4)

📰 News & Research Subscriptions

Automated Research Digests - Subscribe to topics or specific queries; AI filters and summarizes only the most relevant developments
Customizable Delivery - Daily, weekly, or custom schedules, as markdown reports or structured summaries

🌐 Search Sources

Free Search Engines

Academic: arXiv, PubMed, Semantic Scholar
General: Wikipedia, SearXNG
Technical: GitHub, Elasticsearch
Historical: Wayback Machine
News: The Guardian, Wikinews

Premium Search Engines

Tavily - AI-powered search
Google - Via SerpAPI or Programmable Search Engine
Brave Search - Privacy-focused web search

Custom Sources

Local Documents - Search your files with AI
LangChain Retrievers - Any vector store or database
Meta Search - Combine multiple engines intelligently

LDR respects robots.txt and identifies itself honestly when fetching web pages — no stealth or anti-detection techniques. In rare cases this means a page that blocks automated access won't be fetched, which we consider the right trade-off.

Full Search Engines Guide →

💻 Usage Examples

Python API

from local_deep_research.api import LDRClient, quick_query

# Option 1: Simplest - one line research
summary = quick_query("username", "password", "What is quantum computing?")
print(summary)

# Option 2: Client for multiple operations
client = LDRClient()
client.login("username", "password")
result = client.quick_research("What are the latest advances in quantum computing?")
print(result["summary"])

HTTP API

The code example below shows the basic API structure - for working examples, see the link below

import requests
from bs4 import BeautifulSoup

# Create session and authenticate
session = requests.Session()
login_page = session.get("http://localhost:5000/auth/login")
soup = BeautifulSoup(login_page.text, "html.parser")
login_csrf = soup.find("input", {"name": "csrf_token"}).get("value")

# Login and get API CSRF token
session.post("http://localhost:5000/auth/login",
            data={"username": "user", "password": "pass", "csrf_token": login_csrf})
csrf = session.get("http://localhost:5000/auth/csrf-token").json()["csrf_token"]

# Make API request
response = session.post("http://localhost:5000/api/start_research",
                       json={"query": "Your research question"},
                       headers={"X-CSRF-Token": csrf})

🚀 Ready-to-use HTTP API Examples → examples/api_usage/http/

✅ Automatic user creation - works out of the box
✅ Complete authentication with CSRF handling
✅ Result retry logic - waits until research completes
✅ Progress monitoring and error handling

Command Line Tools

# Run benchmarks from CLI
python -m local_deep_research.benchmarks.cli.benchmark_commands simpleqa --examples 50

See the Command Line Tools guide for the full reference.

🔗 Bring Your Own Knowledge Base

Connect LDR to your existing knowledge base. Unlike the HTTP client above, quick_summary() runs LDR in-process — no server needed — so you can pass it live Python objects such as LangChain retrievers:

from local_deep_research.api import quick_summary

# Use your existing LangChain retriever
result = quick_summary(
    query="What are our deployment procedures?",
    retrievers={"company_kb": your_retriever},
    search_tool="company_kb"
)

Works with: FAISS, Chroma, Pinecone, Weaviate, Elasticsearch, and any LangChain-compatible retriever.

Integration Guide →

🔌 MCP Server (Claude Integration)

LDR provides an MCP (Model Context Protocol) server that allows AI assistants like Claude Desktop and Claude Code to perform deep research. Full setup details in the MCP Server guide.

⚠️ Security Note: This MCP server is designed for local use only via STDIO transport (e.g., Claude Desktop). It has no built-in authentication or rate limiting. Do not expose over a network without implementing proper security controls. See the MCP Security Best Practices for network deployment requirements.

Installation

# Install with MCP extras
pip install "local-deep-research[mcp]"

Claude Desktop Configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "local-deep-research": {
      "command": "ldr-mcp",
      "env": {
        "LDR_LLM_PROVIDER": "openai",
        "LDR_LLM_OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

Claude Code Configuration

Add to your .mcp.json (project-level) or ~/.claude/mcp.json (global):

{
  "mcpServers": {
    "local-deep-research": {
      "command": "ldr-mcp",
      "env": {
        "LDR_LLM_PROVIDER": "ollama",
        "LDR_LLM_OLLAMA_URL": "http://localhost:11434"
      }
    }
  }
}

Any LDR setting can be passed in env as an LDR_* variable — see the auto-generated Full Configuration Reference for the complete list.

Available Tools

Tool	Description	Duration	LLM Cost
`search`	Raw results from a specific engine (arxiv, pubmed, wikipedia, ...)	5-30s	None
`quick_research`	Fast research summary	1-5 min	Yes
`detailed_research`	Comprehensive analysis	5-15 min	Yes
`generate_report`	Full markdown report	10-30 min	Yes
`analyze_documents`	Search local collections	30s-2 min	Yes
`list_search_engines`	List available search engines	instant	None
`list_strategies`	List research strategies	instant	None
`get_configuration`	Get current config	instant	None

Individual Search Engines

The search tool lets you query specific search engines directly and get raw results (title, link, snippet) — no LLM processing, no cost, fast. This is especially useful for monitoring and subscriptions where you want to check for new content regularly without burning LLM tokens.

# Search arXiv for recent papers
search(query="transformer architecture improvements", engine="arxiv")

# Search PubMed for medical literature
search(query="CRISPR clinical trials 2024", engine="pubmed")

# Search Wikipedia for quick facts
search(query="quantum error correction", engine="wikipedia")

# Search GitHub for code and repositories
search(query="agentic research frameworks", engine="github")

# Use list_search_engines() to see all available engines

Example Usage

"Use quick_research to find information about quantum computing applications"
"Search arxiv for recent papers on diffusion models"
"Generate a detailed research report on renewable energy trends"

🤖 Supported LLMs

Local Models

Ollama — connect to its native API (default http://localhost:11434)
LM Studio — connect to its OpenAI-compatible server (default http://localhost:1234/v1)
llama.cpp — connect to llama-server's OpenAI-compatible endpoint (default http://localhost:8080/v1); start with llama-server -m <model.gguf>
Common models: Llama, Mistral, Gemma, DeepSeek, Qwen
LLM processing stays local (search queries still go to web). No API costs.

💡 Which local model should I pick? See the community benchmarks — community-submitted accuracy numbers across local and cloud models, so you can compare before downloading.

Cloud Models

OpenAI
Anthropic Claude
Google Gemini
100+ models via OpenRouter
100+ models via Requesty

Custom Endpoints

OpenAI-Compatible Endpoint — any service speaking the OpenAI chat-completions API (vLLM, llama.cpp, gateways)
Anthropic-Compatible Endpoint — a self-hosted service speaking the Anthropic Messages API (/v1/messages); set llm.anthropic_endpoint.url

Model Setup →

🔄 Upgrading from Earlier Versions

llm.model no longer has a default. Pre-1.6.3 installs auto-filled gemma3:12b (Ollama) when no model was configured, which silently downloaded a multi-GB binary. The field is now empty by default — pick a model in Settings → LLM, or research will fail loudly with a clear error.
The auto and parallel meta search engines were removed. The default langgraph-agent strategy selects engines dynamically per query, which replaces them. Stored settings are migrated automatically (removed values become searxng); update any explicit search_tool="auto" API calls or LDR_SEARCH_TOOL=auto env overrides to a concrete engine such as searxng.
The llamacpp provider now uses HTTP instead of in-process loading. If you previously set llm.llamacpp_model_path to a local .gguf file, that setting is no longer read. Instead, run llama-server -m <your-model.gguf> (it ships with every modern llama.cpp build) and the default llm.llamacpp.url of http://localhost:8080/v1 will pick it up. Optional API key support is available via llm.llamacpp.api_key if you put llama-server behind an auth proxy.

📚 Documentation

Getting Started

Core Features

Advanced Features

Development

Docker Compose Guide
Development Guide
CodeQL Guide
Release Guide
CI/CD Infrastructure — pre-commit hooks, workflows, security scanning
Workflow Status Dashboard — live health of every GitHub Actions workflow
Test Coverage Report — pytest coverage published to GitHub Pages

Examples & Tutorials

📰 Featured In

"Local Deep Research deserves special mention for those who prioritize privacy... tuned to use open-source LLMs that can run on consumer GPUs or even CPUs. Journalists, researchers, or companies with sensitive topics can investigate information without queries ever hitting an external server."

— Medium: Open-Source Deep Research AI Assistants

News & Articles

Korben.info - French tech blog ("Sherlock Holmes numérique")
Roboto.fr - "L'alternative open-source gratuite à Deep Research d'OpenAI"
KDJingPai AI Tools - AI productivity tools coverage
AI Sharing Circle - AI resources coverage

Community Discussions

r/LocalLLaMA: 95.7% SimpleQA on a single 3090, fully local - Qwen3.6-27B benchmark announcement (161K views)
r/WebAfterAI: LDR as the agentic layer on top of Ollama / LM Studio / LocalAI - Stack-positioning explainer
Hacker News - 190+ points, community discussion
LangChain Twitter/X - Official LangChain promotion
LangChain LinkedIn - 400+ likes

International Coverage

🇨🇳 Chinese

Juejin (掘金) - Developer community
Cnblogs (博客园) - Developer blogs
GitHubDaily (Twitter/X) - Influential tech account
Zhihu (知乎) - Tech community
A姐分享 - AI resources
CSDN - Installation guide
NetEase (网易) - Tech news portal

🇯🇵 Japanese

note.com: 調査革命：Local Deep Research徹底活用法 - Comprehensive tutorial
Qiita: Local Deep Researchを試す - Docker setup guide
LangChainJP (Twitter/X) - Japanese LangChain community

🇰🇷 Korean

PyTorch Korea Forum - Korean ML community
GeekNews (Hada.io) - Korean tech news

Reviews & Analysis

BSAIL Lab: How useful is Deep Research in Academia? - Academic review by contributor @djpetti
The Art Of The Terminal: Use Local LLMs Already! - Comprehensive review of local AI tools, featuring LDR's research capabilities
Fahd Mirza: Local Deep Research + Ollama — Free AI Research Assistant You Control - Installation walkthrough (349K-subscriber channel). The SearXNG startup issue shown in the video has since been fixed in #3881 — clean Docker installs now start out of the box.
BC Adventure Tech: Run ChatGPT Deep Research Locally (Unraid AI Agent Setup) - Unraid setup and showcase

SearXNG LDR-Academic - Academic-focused SearXNG fork with 12 research engines (arXiv, Google Scholar, PubMed, etc.) designed for LDR
DeepWiki Documentation - Third-party documentation and guides

Note: Third-party projects and articles are independently maintained. We link to them as useful resources but cannot guarantee their code quality or security.

🤝 Community & Support

Discord - Get help and share research techniques
Reddit - Updates and showcases
GitHub Issues - Bug reports

🧑‍💻 Contributing

We welcome contributions of all sizes — from typo fixes to new features. The key rule: keep PRs small and atomic (one change per PR). For larger changes, please open an issue or start a discussion first — we want to protect your time and make sure your effort leads to a successful merge rather than a misaligned PR. See our Contributing Guide to get started.

Acknowledgements

Local Deep Research is built on the work of many open-access initiatives, academic databases, and open-source projects. We are grateful to:

Academic & Research Data

Source	What It Provides	License
OpenAlex	Academic metadata for ~280K sources and ~120K institutions, including DOAJ status	CC0
DOAJ	Directory of Open Access Journals — open-access verification (via OpenAlex)	CC0
arXiv	Preprints in physics, mathematics, CS, and more	Various (see arXiv license)
PubMed / NCBI	Biomedical and life sciences literature	Public domain (US Gov)
Semantic Scholar	Cross-discipline academic search with citation data	Terms
NASA ADS	Astrophysics, physics, and astronomy papers	Terms
Zenodo	Open research data, datasets, and software	Various per record
PubChem	Chemistry and biochemistry database	Public domain (US Gov)
Stop Predatory Journals	Predatory journal/publisher blacklist	MIT
JabRef	Journal abbreviation database	CC0

Knowledge & Content Sources

Wikipedia • OpenLibrary • Project Gutenberg • GitHub • Stack Exchange • The Guardian • Wayback Machine

Infrastructure & Frameworks

LangChain • Ollama • SearXNG • FAISS

Support Open Access

These projects run on donations and grants, not paywalls. If Local Deep Research is useful to you, consider giving back to the open-access ecosystem that makes it possible:

arXiv — free preprints for physics, math, CS, and more
PubMed / NLM — open biomedical literature
Wikipedia / Wikimedia — the free encyclopedia
Internet Archive — the Wayback Machine and open digital library
DOAJ — curating and verifying open-access journals worldwide
OpenAlex — open scholarly metadata (sponsored by OurResearch)
Project Gutenberg — free ebooks since 1971

📄 License

MIT License - see LICENSE file.

Dependencies: All third-party packages use permissive licenses (MIT, Apache-2.0, BSD, etc.) - see allowlist

Open Sources

Everything worth knowing in AI.

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