LearningCircuit/local-deep-research

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 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