by Upsonic · MCP Server · ★ 8.0k
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🔒 Is Upsonic safe to install? View the security audit →
Upsonic Production-Ready AI Agent Framework with Safety First Documentation • Quickstart • Examples • Discord Overview Upsonic is an open-source AI agent framework for building production-ready agents. It supports multiple AI providers (OpenAI, Anthropic, Azure, Bedrock) and includes built-in safety policies, OCR, memory, multi-agent coordination, and MCP tool integration. What Can You Build? Documen
uv pip install upsonic
uv pip install "upsonic[ocr]"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 | 7,957 |
| Forks | 744 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.2736020255398/100 |
| Open Issues | 33 |
| Last Updated | 2026-06-18 |
| Created | 2024-05-26 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~15k |
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Upsonic is Build autonomous AI agents in Python.. It is categorized as a MCP Server with 8.0k GitHub stars.
Upsonic is primarily written in Python. It covers topics such as agent, agent-framework, autonomous-agent.
The README on GitHub (github.com/Upsonic/Upsonic) gives: uv pip install upsonic , then uv pip install "upsonic[ocr]". Usage details are in the same README.
Upsonic is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Upsonic on Agent Skills Hub include casibase, FastGPT, hive. 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: