by LearningCircuit · Agent Tool · ★ 9.2k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is local-deep-research safe to install? View the security audit →
Local Deep Research [![OpenSSF Scorecard](https://api.securityscorecards.dev/p
pip install local-deep-research
pip install "local-deep-research[mcp]"Taken from the project's README on GitHub. Check the security audit before running it.
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 9,191 |
| Forks | 845 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 63.3147337301187/100 |
| Open Issues | 1101 |
| Last Updated | 2026-10-14 |
| Created | 2025-02-09 |
| Platforms | python |
| Est. Tokens | ~20k |
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local-deep-research is ~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 & Encryp. It is categorized as a Agent Tool with 9.2k GitHub stars.
local-deep-research is primarily written in Python. It covers topics such as academia, anthropic, arxiv.
The README on GitHub (github.com/LearningCircuit/local-deep-research) gives: pip install local-deep-research , then pip install "local-deep-research[mcp]". Usage details are in the same README.
local-deep-research is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to local-deep-research on Agent Skills Hub include agentops, deep-research, maid. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: