tron-python 0.1.0

Python SDK for the TRON (Token Reduced Object Notation) format — compact, LLM-friendly JSON superset

tron-python

Python SDK for the TRON (Token Reduced Object Notation) format — a compact, LLM-friendly superset of JSON that reduces token usage by 20–40 % by hoisting repeated object schemas into a class-definition header.

See the full specification at tron-format.github.io.

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Why TRON?

When sending structured data to LLMs you pay per token. JSON repeats key names for every object in an array:

[
  {"name": "Alice", "role": "eng", "active": true},
  {"name": "Bob",   "role": "mktg","active": false}
]

TRON defines the schema once and uses compact instantiation syntax in the body:

class A: name,role,active

[A("Alice","eng",true),A("Bob","mktg",false)]

Same data, fewer tokens. TRON is a JSON superset — the body section is valid JSON extended with ClassName(arg, …) syntax, so existing JSON parsers can read the data without modification.

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Installation

pip install tron-python

No runtime dependencies. Requires Python 3.9+.

Optional extras:

pip install tron-python[benchmark]   # adds tiktoken for token-count comparisons
pip install tron-python[pydantic]    # explicit pydantic dependency pin

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Quick start

import tron

# Serialize
data = [
    {"name": "Alice", "role": "eng", "score": 9.5},
    {"name": "Bob",   "role": "mktg","score": 8.1},
    {"name": "Carol", "role": "eng", "score": 9.2},
]
text = tron.stringify(data)
print(text)
# class A: name,role,score
#
# [A("Alice","eng",9.5),A("Bob","mktg",8.1),A("Carol","eng",9.2)]

# Deserialize
result = tron.parse(text)
assert result == data          # True — lossless roundtrip

# Plain JSON passthrough (TRON is a superset)
assert tron.parse('[1, 2, 3]') == [1, 2, 3]

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API reference

tron.stringify(value) → str

Serialize any Python value to TRON.

Input type	Output
None	null
bool	true / false
int, float	JSON number (NaN/Inf → null)
str	JSON-quoted string
list, tuple	JSON array
dict	ClassName(v1,v2,…) with header
datetime, date	ISO-8601 string
Decimal	float (NaN/Inf → null)
set, frozenset	sorted JSON array
dataclass, Pydantic	converted to dict first

Raises TypeError for bytes or unknown types.
Raises ValueError if a dict key contains a comma or newline.

tron.parse(text) → Any

Deserialize a TRON (or plain JSON) string.

Raises TypeError if text is not a str.
Raises ValueError for malformed TRON/JSON.

tron.benchmark_compare(value) → dict

Return character and (optionally) token counts for JSON vs TRON.

import tron

data = [{"id": i, "name": f"user_{i}"} for i in range(100)]
info = tron.benchmark_compare(data)
# {
#   "json": {"chars": 2890},
#   "tron": {"chars": 1750, "char_savings_pct": 39.4},
#   "note": "Install tiktoken for token-count comparisons: pip install tiktoken"
# }

tron.print_benchmark(value)

Pretty-print a comparison table to stdout (convenience wrapper around benchmark_compare).

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Format primer

A TRON document has two sections separated by a blank line:

class A: field1,field2   ← header: one class per unique object shape
class B: x,y,z

A("val1",42)             ← body: JSON with class instantiation

• Any valid JSON is valid TRON (no header required for primitive/array values).
• Class names are uppercase letters: A–Z, then AA, AB, …
• Nested objects produce nested class instantiations: A("hello",B(1,2)).
• The body uses standard JSON encoding for all values inside ().

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Supported Python versions

Python 3.9, 3.10, 3.11, 3.12 (tested in CI).

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Development

git clone https://github.com/tron-format/tron-python
cd tron-python
pip install -e ".[dev]"

# Run the full test suite
pytest

# With coverage
pytest --cov=tron --cov-report=term-missing

# Lint
ruff check src tests

# Type-check
mypy src

Project layout

tron-python/
├── src/tron/
│   ├── __init__.py      Public API
│   ├── _stringify.py    Two-pass serializer
│   ├── _parse.py        Recursive-descent parser
│   ├── _benchmark.py    Benchmark utilities
│   ├── _utils.py        Shared helpers
│   └── py.typed         PEP 561 marker
├── tests/
│   ├── test_stringify.py
│   ├── test_parse.py
│   ├── test_roundtrip.py
│   └── test_edge_cases.py
└── pyproject.toml

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Contributing

1. Fork the repo and create a feature branch.
2. Write tests before code — all new behaviour must be covered.
3. Run ruff check, mypy src, and pytest before opening a PR.
4. Follow Conventional Commits for commit messages.

Please open an issue before starting work on a significant change so we can align on the approach.

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Acknowledgements

TRON was created by Tim Huang.
See the official specification and the reference JavaScript SDK.

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License

MIT — see LICENSE.