Metadata-Version: 2.4
Name: aacp-pydantic-ai
Version: 0.1.0
Summary: AACP coordination layer for Pydantic AI multi-agent workflows
Author: Andrew Mackay
License: MIT
Project-URL: Homepage, https://aacp.dev
Project-URL: Repository, https://github.com/MackayAndrew/aacp-pydantic-ai
Keywords: aacp,pydantic-ai,agents,multi-agent,coordination,llm
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.9
Description-Content-Type: text/markdown
Requires-Dist: aacp>=1.4.0
Requires-Dist: pydantic-ai>=0.0.14
Requires-Dist: openai>=1.0.0

# 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

```bash
pip install aacp-pydantic-ai
```

## Quick start

```python
from aacp_pydantic.orchestrator import AACPPydanticOrchestrator

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

## Comparison demo

```bash
python3 examples/comparison.py --mock
```

## Links

- Protocol: https://aacp.dev
- Python SDK: https://github.com/MackayAndrew/aacp
- IETF Draft: https://datatracker.ietf.org/doc/draft-mackay-aacp/

## Licence

MIT
