by placet-io · MCP Server · ★ 76
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🔒 Is facio safe to install? View the security audit →
A long-running, human-in-the-loop runtime for reliable AI work. Built to collaborate with the operator, not to erase the operator. self-improving · auditable · resumable · approval-gated · multimodal Quickstart Prerequisites: Docker 24+ with Compose v2. That's it. The script detects your OS, verifies Docker, fetches the public compose file into , asks for the initial Placet admin email and password, generates any missing secrets, pulls , and runs for you. Facio starts with one agent by default. If you leave the password blank, setup generates one and prints it at the end together with the Placet URL where the Facio agent is available. Default URL: http://localhost:8080. Variants: bash Accept all defaults, no prompts curl -fsSL https://raw.githubusercontent.com/placet-io/facio/main/quickstart/setup.sh | bash -s -- --yes Set the initial Placet admin account from the command line curl -fsSL https://raw.githubusercontent.com/placet-io/facio/main/quickstart/setup.sh | \ bash -s -- --admin-email admin@example.com
| Stars | 76 |
| Forks | 1 |
| Language | Python |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 62.2087820664063/100 |
| Last Updated | 2026-05-31 |
| Created | 2026-05-02 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~180k |
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facio is A proactive AI agent for secure, traceable, human-in-the-loop task execution over long-running workflows.. It is categorized as a MCP Server with 76 GitHub stars.
facio is primarily written in Python. It covers topics such as agent, agentic-ai, agentic-workflow.
You can find installation instructions and usage details in the facio GitHub repository at github.com/placet-io/facio. The project has 76 stars and 1 forks, indicating an active community.
facio is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to facio on Agent Skills Hub include hotplex, openlegion, atlas-agent-control-plane. 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.
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