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 vstorm-co · MCP Server · ★ 1.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is pydantic-deepagents safe to install? View the security audit →
Pydantic Deep Agents From framework to terminal -- autonomous AI agents that plan, code, and ship Docs · PyPI · CLI · DeepResearch · Examples <img src="ht
| Stars | 1,033 |
| Forks | 125 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.5057263890868/100 |
| Open Issues | 12 |
| Last Updated | 2026-08-17 |
| Created | 2025-11-29 |
| Platforms | claude-code, cli, docker, mcp, python |
| Est. Tokens | ~17k |
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pydantic-deepagents is Open-source, self-hosted Claude Code - a terminal AI assistant and the Python framework behind it. Tool-calling, sandboxed execution, multi-agent teams, skills, checkpoints, unlimited context - on Pyd. It is categorized as a MCP Server with 1.0k GitHub stars.
pydantic-deepagents is primarily written in Python. It covers topics such as agent-framework, ai-agents, anthropic.
You can find installation instructions and usage details in the pydantic-deepagents GitHub repository at github.com/vstorm-co/pydantic-deepagents. The project has 1.0k stars and 125 forks, indicating an active community.
pydantic-deepagents is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to pydantic-deepagents on Agent Skills Hub include linkedin-mcp-server, Claude-Code-Everything-You-Need-to-Know, ai-devkit. 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: