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 →
by puppyone-ai · MCP Server · ★ 289
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is DeepWideResearch safe to install? View the security audit →
Open Deep Wide Research Agentic RAG for any scenarioCustomize sources, depth, and width Why Do You Need Open Deep Wide Research? In 2025, we observed 2 critical trends reshaping the Retrieval-Augmented Generation (RAG) tech stacks: Traditional, Rigid, pipeline-driven RAG is giving way to more dynamic agentic RAG systems. The emergence of MCP is dramatica
| Stars | 289 |
| Forks | 6 |
| Language | TypeScript |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 66.7080468622858/100 |
| Last Updated | 2026-07-12 |
| Created | 2025-09-30 |
| Platforms | mcp, node |
| Est. Tokens | ~16k |
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DeepWideResearch is Agentic RAG for any scenario. Customize sources, depth, and width. It is categorized as a MCP Server with 289 GitHub stars.
DeepWideResearch is primarily written in TypeScript. It covers topics such as agent, agentic-workflow, mcp.
You can find installation instructions and usage details in the DeepWideResearch GitHub repository at github.com/puppyone-ai/DeepWideResearch. The project has 289 stars and 6 forks, indicating an active community.
DeepWideResearch is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to DeepWideResearch on Agent Skills Hub include vinagent, flock, OpenDerisk. 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: