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reprobate 🖨️

Budget-controlled repr for Python objects.

Renders any Python object into a string that fits within a character budget. Nested structures degrade gracefully: full values, then type stubs, then counts. Zero dependencies. Pluggable via a type registry and a __budget_repr__ protocol.

Features

  • Hard budget guarantee -- output is always <= budget characters
  • Three-phase degradation -- full render, then name=<type(len)> stubs, then ...N more counts
  • Greedy and even policies -- prioritize depth (first fields in detail) or breadth (all fields equally)
  • Uniform collapse -- sequences of one repeated value render as the lossless product form [0.0] * 97
  • Bounded type inference -- exact or best-effort aggregate hints such as <list[str](200)>, with complete sample values when space allows: <list[{'id': int}](80): {'id': 0}, ...>
  • Cycle detection -- circular references render as <...> instead of stack overflows
  • Type registry -- @register(MyType) for custom budget-aware renderers
  • Protocol method -- __budget_repr__(self, budget) on any class
  • Optional extensions -- typed table/array summaries for Arrow, NumPy, pandas, Polars, Pillow, and Pydantic (guarded imports, zero cost if absent)

Install

pip install reprobate

Zero dependencies. Optional renderers activate automatically when their libraries are already installed (numpy, pandas, polars, pyarrow, Pillow, pydantic).

Quick example

import reprobate

reprobate.render({"name": "alice", "scores": [98, 87, 95, 72, 88]}, 60)
# "{'name': 'alice', 'scores': [98, 87, 95, 72, 88]}"

reprobate.render({"name": "alice", "scores": [98, 87, 95, 72, 88]}, 30)
# "{'name': 'alice', ...1 more}"

reprobate.render(list(range(1000)), 40)
# "[0, 1, 2, 3, 4, 5, 6, 7, 8, ...991 more]"

Policies

from dataclasses import dataclass

@dataclass
class Agent:
    desc: str = "A very long description that eats the budget"
    important_note: str = "critical info here"
    status: str = "running"
    config: dict = None
    history: list = None

agent = Agent()

# Greedy: first fields get full detail
reprobate.render(agent, 100, policy="greedy")
# "Agent(desc='A very long d...', important_note=<str(18)>, status=<str(7)>, config=None, history=None)"

# Even: all fields get comparable detail
reprobate.render(agent, 100, policy="even")
# "Agent(desc='A very l...', important_note='critical...', status='running', config=None, history=None)"

"even" uses max-min allocation among visible siblings. A bounded planning probe identifies children whose complete representation needs less than their initial share, then redistributes the unused characters among siblings that can still improve. Opaque custom renderers are not called speculatively.

Inference

Aggregate type hints are controlled independently from budget allocation:

values = ["alice" * 30] * 1000

reprobate.render(values, 25)
# "<list[str](1000)>"

reprobate.render(values, 25, inference="exact")
# "<list(1000)>" -- too large for exhaustive inspection

reprobate.render(values, 25, inference="off")
# "[<str(150)>, ...999 more]"

result = {"users": ["alice" * 30] * 200, "cursor": "abc" * 100}
reprobate.render(result, 42)
# "<{'users': list[str], 'cursor': str}>"

"best_effort" is the default and uses bounded sampling for large containers. "exact" emits aggregate types only after exhaustive bounded inspection; "off" disables aggregate runtime inference. Type expressions are diagnostic hints, not validation guarantees.

Small string-keyed mappings are treated as fixed records, so their compact schemas retain the association between each literal key and its value type. Larger or non-string-keyed mappings use dict[key_type, value_type] summaries instead.

Custom renderers

Register a renderer for any type:

@reprobate.register(MyType)
def render_my_type(obj: MyType, budget: int) -> str:
    return f"MyType({obj.key})"[:budget]

Or implement the protocol directly:

class MyType:
    def __budget_repr__(self, budget: int) -> str:
        return f"MyType({self.key})"[:budget]

For renderers that recurse into child objects, use render_child (inherits policy and cycle detection) and render_attrs (standard TypeName(key=val, ...) pattern):

from reprobate import register, render_child, render_attrs

@register(MyContainer)
def render_my_container(obj: MyContainer, budget: int) -> str:
    # render_child for recursive rendering
    inner = render_child(obj.value, budget - 10)
    return f"MyContainer({inner})"

@register(MyModel)
def render_my_model(obj: MyModel, budget: int) -> str:
    # render_attrs for the standard object pattern
    attrs = {"name": obj.name, "data": obj.data}
    return render_attrs(attrs, "MyModel", budget)

Part of the agex stack

reprobate renders agent workspace objects for LLM context windows in agex, fitting complex types like DataFrames and nested structures within token budgets.

Supported types

Category Types Behavior
Primitives None, bool, int, float repr(), or a <int>-style stub when it cannot fit
Strings str, bytes Quoted, escaped previews with ..., plus length metadata when space allows: <str(150): 'xxx...'>
Containers list, tuple, set, frozenset Head items + ...N more, schema summaries when items cannot fit
Dicts dict Key-value pairs + ...N more; subclasses keep their own repr
Collections deque, defaultdict, Counter Type-aware wrappers (factory name, most-common order)
Structured dataclass, namedtuple Field-aware decomposition, respects repr=False
Objects anything with __dict__ or __slots__ Attribute decomposition, public attrs only
Optional numpy, pandas, polars, pyarrow, Pillow, pydantic Shape, dtype, typed-column schema, and bounded value or leading-row summaries such as DataFrame(3x2, {'name': str, 'score': int64}, [('ada', 7), ...2 more]) (auto-activates when installed)
numpy scalars np.int64, np.float32, np.str_, ... Render as their builtin value (3, not np.int64(3)); extended precision and datetimes keep their own repr

Development

uv sync --extra dev
uv run pytest

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