# Local Deep Research — Deep Research with Local LLMs and 10+ Search Engines

> An open-source research tool that achieves high accuracy on benchmarks using local or cloud LLMs, with support for arXiv, PubMed, and private document search, all encrypted and self-hosted.

## Install

Save in your project root:

# Local Deep Research — Deep Research with Local LLMs and 10+ Search Engines

## Quick Use
```bash
pip install local-deep-research
local-deep-research --query "Recent advances in transformer architectures"
```

## Introduction
Local Deep Research is an open-source tool that performs multi-source deep research using local or cloud LLMs. It searches across arXiv, PubMed, the web, and your private documents, then synthesizes findings into a grounded summary. Everything runs locally and encrypted.

## What Local Deep Research Does
- Performs multi-step research across 10+ search engines and data sources
- Supports local LLMs via Ollama, llama.cpp, and cloud providers
- Searches arXiv, PubMed, Brave, SearXNG, and private document collections
- Synthesizes findings into structured, citation-backed reports
- Keeps all data and processing local with encryption support

## Architecture Overview
The system uses an iterative research loop. Given a query, it first generates search sub-queries, dispatches them to configured search engines in parallel, collects and deduplicates results, then uses an LLM to analyze and synthesize findings. If the initial results are insufficient, it generates follow-up queries and repeats the cycle. The final report includes citations with links to source material. All LLM inference can run locally through Ollama or llama.cpp.

## Self-Hosting & Configuration
- Install via pip: `pip install local-deep-research`
- Configure your LLM backend (Ollama, OpenAI, Google, Anthropic) in settings
- Enable search engines by providing API keys or connecting to SearXNG
- Add private document directories for local corpus search
- All configuration stored in a local YAML file

## Key Features
- Multi-engine parallel search across academic, web, and private sources
- Supports fully local operation with Ollama or llama.cpp
- Citation-backed reports with links to original sources
- Iterative research loop that refines queries based on initial findings
- Encrypted local storage for research history and document indexes

## Comparison with Similar Tools
- **Perplexica** — open-source AI search engine; Local Deep Research supports more specialized academic sources
- **ChatGPT Deep Research** — cloud-based; Local Deep Research runs entirely self-hosted
- **Khoj** — personal AI assistant with search; Local Deep Research focuses specifically on multi-source research synthesis
- **Storm (Stanford)** — research article generation; Local Deep Research is more focused on quick, grounded answers

## FAQ
**Q: Does it work completely offline?**
A: LLM inference can be fully offline with local models. Web and academic searches require internet access by nature.

**Q: Which local models work best?**
A: Models with strong instruction following in the 7B-27B range work well. The project documentation lists tested configurations.

**Q: Can I add my own document collection as a source?**
A: Yes. Point it to a directory of PDFs, Markdown, or text files and it will index and search them alongside web sources.

**Q: Is it suitable for academic research?**
A: It is a research assistant tool. Always verify findings against primary sources before citing in academic work.

## Sources
- https://github.com/LearningCircuit/local-deep-research


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Source: https://tokrepo.com/en/workflows/asset-2d129ca7
Author: AI Open Source