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0.1.0

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aacp-pydantic-ai

AACP coordination layer for Pydantic AI multi-agent workflows

aacpagentscoordinationllmmulti-agentpydantic-ai
Published15 Jun 2026
Direct dependencies3
Transitive dependenciesNot calculated

README

Version 0.1.0 documentation

aacp-pydantic-ai

AACP coordination layer for Pydantic AI multi-agent workflows.

Pydantic AI already types the result layer -- agents return validated Pydantic models. AACP adds typing to the instruction layer: what the orchestrator says to agents is now also typed, deterministic, and schema-validated. Both layers become fully deterministic.

Without AACP:  NL instruction → Agent → typed result
With AACP:     AACP packet    → Agent → typed result
               ↑ typed                  ↑ typed (already)

Install

pip install aacp-pydantic-ai

Quick start

from aacp_pydantic.orchestrator import AACPPydanticOrchestrator

orch = AACPPydanticOrchestrator(model="openai:gpt-4o-mini")
result = orch.run_workflow("payroll", period="2026-03")

Comparison demo

python3 examples/comparison.py --mock

Links

Licence

MIT

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Package / targetRequirementRelationship
aacp
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.9",
  "evaluation": "NOT_EVALUATED"
}
aacp>=1.4.0runtime
pydantic-ai
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.9",
  "evaluation": "NOT_EVALUATED"
}
pydantic-ai>=0.0.14runtime
openai
Environment
{
  "extras": "",
  "marker": null,
  "requires_python": ">=3.9",
  "evaluation": "NOT_EVALUATED"
}
openai>=1.0.0runtime

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aacp_pydantic_ai-0.1.0-py3-none-any.whl

bdist_wheel · 13.32 KB · Python py3

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aacp_pydantic_ai-0.1.0.tar.gz

sdist · 10.61 KB · Python source

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Repository

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mackayandrew/aacp-pydantic-ai

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Gauge · observed snapshots23 Oct 2026
10.50
2026-10-23: 0 · observed 2026-10-23T11:38:10.3867711Z
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