subagents-pydantic-ai: Subagent Delegation framework for Pydantic AI, enabling nested subagents that can spawn their own specialists

by vstorm-co · MCP Server · ★ 62

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install subagents-pydantic-ai

pip install subagents-pydantic-ai
uv add subagents-pydantic-ai

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 73/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-agent-frameworkaiagentsframeworkanthropicartificial-intelligencechatgptclaude-codeframeworkgenaillmmcp

Quick Facts

Stars62
Forks19
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score73/100
Open Issues1
Last Updated2026-10-05
Created2026-01-19
Platformsclaude-code, mcp, python
Est. Tokens~16k

About subagents-pydantic-ai

Subagents for Pydantic AI Multi-Agent Orchestration for Pydantic AI Nested Subagents — subagents spawn their own subagents  •  Runtime Agent Creation — create specialists on-the-fly  •  Auto-Mode Selection — intelligent sync/async decision Subagents for Pydantic AI adds multi-agent delegation to any Pydantic AI agent. Spawn specialized subagents that run synchro

Compatible Skills

These tools work well together with subagents-pydantic-ai for enhanced workflows:

  • modelsmith — semantic(0.21)+complementary+rare_topics+same_lang+similar_pop+shared_platform (57%)
  • swarms-examples — semantic(0.17)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • custom-agent-with-skills — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (54%)
  • langchain — semantic(0.26)+complementary+rare_topics+same_lang+shared_platform (53%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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:

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