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 Cerno-AI · Agent Tool · ★ 84
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Cerno : Agentic Deep Research Cerno is an open-source workspace for conducting deep, multi-step research and analysis using autonomous AI agents. Designed for developers and researchers who demand analytical transparency, Cerno exposes every reasoning step—from prompt decomposition to final synthesis—so you can observe, debug, and steer complex agentic workflows with confidence. 📚 Table of Contents Highlights Local-First Principles Active Development & Community Prerequisites Docker Installation CLI Reference Project Structure Screenshots Use Cases Roadmap [Security & Privacy](#-security-
| Stars | 84 |
| Forks | 7 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.1035993727601/100 |
| Last Updated | 2025-09-11 |
| Created | 2025-06-09 |
| Platforms | claude-code, docker, gemini, python |
| Est. Tokens | ~65k |
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Cerno-Agentic-Local-Deep-Research is Cerno is a local-first research platform that leverages agentic AI to break down complex queries into verifiable, multi-step workflows. Switch seamlessly between cloud LLMs and self-hosted models, tra. It is categorized as a Agent Tool with 84 GitHub stars.
Cerno-Agentic-Local-Deep-Research is primarily written in Python. It covers topics such as agentic-ai, agents, anthropic.
You can find installation instructions and usage details in the Cerno-Agentic-Local-Deep-Research GitHub repository at github.com/Cerno-AI/Cerno-Agentic-Local-Deep-Research. The project has 84 stars and 7 forks, indicating an active community.
Cerno-Agentic-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 Cerno-Agentic-Local-Deep-Research on Agent Skills Hub include chibi, mcp-server-code-execution-mode, mcp-server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.