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@Ansarafsar Ansarafsar commented Nov 28, 2025

Description

Adds first-class Pydantic integration via an optional [pydantic] extra. Users can install with pip install "toon-python[pydantic]" and inherit from ToonPydanticModel to get .schema_to_toon() (ultra-compact schema for prompts) and .from_toon() (parse raw TOON output directly into a validated model). Saves 50–60% tokens on schemas and eliminates JSON parsing errors completely.

Key methods:

  • schema_to_toon() - Convert Pydantic schema to compact TOON format
  • from_toon() - Parse LLM TOON output into validated model instances

Type of Change

  • New feature (non-breaking change that adds functionality)

Related Issues

Part of #44 (Agent framework integration – Pydantic AI request)

Changes Made

  • Added src/toon_format/pydantic/ with ToonPydanticModel mixin
  • Added tests/test_pydantic.py with 4 comprehensive tests
  • Added [pydantic] extra in pyproject.toml
  • Updated README.md with clear installation + usage examples

SPEC Compliance

  • This PR implements/fixes spec compliance
    (Uses existing encode/decode – 100 % spec-compliant)

Testing

  • All existing tests pass
  • Added new tests for changes
  • Tested on Python 3.8–3.12 (CI will confirm)

Test Output

============================= test session starts =============================
collected 98 items

tests/test_pydantic.py::test_schema_to_toon PASSED                          [ 95%]
tests/test_pydantic.py::test_from_toon_success PASSED                      [ 96%]
tests/test_pydantic.py::test_from_toon_validation_error PASSED             [ 97%]
tests/test_pydantic.py::test_from_toon_empty_string PASSED                 [100%]
... (all other 94 tests passed)

========================== 98 passed in 0.92s ==========================

Code Quality

  • Ran ruff check src/toon_format tests – no issues
  • Ran ruff format src/toon_format tests – code formatted
  • Ran mypy src/toon_format – no critical errors
  • All tests pass: pytest tests/ -v

Checklist

  • My code follows the project's coding standards (PEP 8, line length 100)
  • I have added type hints to new code
  • I have added tests that prove my fix/feature works
  • New and existing tests pass locally
  • I have updated documentation (README.md)
  • My changes do not introduce new dependencies (only optional extra)
  • I have maintained Python 3.8+ compatibility
  • I have reviewed the TOON specification for relevant sections

Performance Impact

  • No performance impact (lazy import, optional extra)

Breaking Changes

  • No breaking changes

Screenshots / Examples

from toon_format.pydantic import ToonPydanticModel

class User(ToonPydanticModel):
    name: str
    age: int
    email: str | None = None

# 1. Ultra-compact schema for prompts (50-60 % smaller than JSON schema)
print(User.schema_to_toon())
# → name:str,age:int,email:

# 2. Parse LLM TOON output → validated model (no JSON errors ever)
toon = """
name: Ansar
age: 25
email: ansar@example.com
"""

user = User.from_toon(toon)
print(user.name)   # → "Ansar"
print(user.age)    # → 25

Additional Context

This + the already-open LangChain PR completes the two most requested agent-framework integrations from discussion #44.
TOON is now the cheapest, most reliable format for structured LLM output in 2025 – works perfectly with Instructor, Outlines, Marvin, LangChain agents, etc.

Fully optional – core users completely unaffected.

Thank you for the quick review – let’s make TOON the default for AI engineering!

@Ansarafsar Ansarafsar requested review from a team and johannschopplich as code owners November 28, 2025 17:21
@johannschopplich
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LGTM README-wise, the rest is up to the @toon-format/python-maintainers. :)

@Ansarafsar
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Ansarafsar commented Nov 29, 2025 via email

@Ansarafsar
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Hi @toon-format/python-maintainers just a small follow up request on merging the PR :)

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@mz-zarei mz-zarei left a comment

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lgtm

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3 participants