A symbolic object can be both executed and manipulated, and the two stay in sync. It behaves like any Python object — methods, attributes, validation — and it simultaneously exposes the structure it was built from, so your program is also data your own code can query, edit, diff, and search over.

Ordinary objects are built and then sealed. Trainer(model=Model(units=128)) runs, and the call that produced it is gone — you can read the attributes back, but the structure of how it was assembled is nowhere.

For most objects that's fine. It stops being fine the moment your program is also something you need to operate on: to sweep, to diff, to patch from a flag, to generate, to hand to a search.

A pg.Object keeps the call.

import pygx as pg

class Model(pg.Object):
    units: int = 8

class Trainer(pg.Object):
    model: Model
    lr: float = 0.01

t = Trainer(model=Model(units=128))
t.sym_init_args          # {'model': Model(units=128), 'lr': 0.01}

That one difference is the whole library. Everything below is a consequence of it — and each is something that has no straightforward answer in plain Python.

The idea has a name: symbolic programming, a paradigm where a program can manipulate its own components as if they were plain data. PyGX brings it to ordinary class definitions.

A class that is also a space of programs

You have a config three levels deep and you want to sweep two of its values.

Ordinarily you write a loop that reconstructs the whole config per variant, with the paths hard-coded — and every new parameter means editing that loop.

In PyGX you say it where the value lives:

space = Exp(
    model=Model(
        units=pg.oneof([8, 16]),
        opt=Opt(lr=pg.oneof([0.1, 0.01])),
    )
)

list(pg.iter(space))     # 4 programs: (8, 0.1) (8, 0.01) (16, 0.1) (16, 0.01)
pg.dna_spec(space)       # the space itself, as an introspectable genotype

No separate search-space schema that has to be kept in sync with the config class. No loop to rewrite when a parameter is added. The class is the space, and a space is a value you can pass around, serialize, and search over — which is what drives Google Cloud Vertex AI NAS, Pax, and Vizier.

Programs that aren't finished yet

A hole is usually None, which already means four other things. Here it is a value with a name, that you can pass around and fill in later:

p = Model.partial()
pg.is_partial(p)         # True
p.sym_missing()          # {'units': MISSING_VALUE}

An abstract object's __init__ body does not even run until it becomes concrete. That is what lets a program be assembled in stages — by a user, a config file, a search, or a model — and still be a real, checkable object at every stage.

Edits addressed at the whole program

An override arrives as a string: from a CLI flag, a config file, an experiment sheet. Ordinarily you parse the path, walk it with getattr, setattr the leaf, and validate by hand.

exp.sym_rebind({'model.opt.lr': 0.5})            # validated, at any depth
exp.sym_rebind(lambda k, v, p: v * 2 if isinstance(v, int) else v)
pg.patch(exp, ['scale_lr?factor=3'])             # named, composable, serializable

The rule form is what makes bulk transformation possible at all: every integer under this tree, doubled is one line rather than a bespoke recursive walk that has to know your class layout.

An edit re-initializes what it touched

This is what makes the edit above safe rather than merely convenient. A rebind is not a setattr that leaves you to figure out the consequences — every object the change reached is re-initialized, so derived state cannot go stale:

class Model(pg.Object, topo=True):
    units: int = 8

    def on_sym_ready(self):              # runs at construction…
        super().on_sym_ready()
        self.scale = self.units * 2      # …and again after any rebind

m = Model(units=8)                       # scale == 16
m.sym_rebind(units=64)                   # scale == 128, without being asked

on_sym_ready fires whenever the object is concrete — at the end of __init__ and after every rebind that reaches it, including one addressed at an ancestor three levels up. Anything computed from fields belongs here, and it stays correct for free.

Three hooks, by how much you need to know:

• on_sym_ready — recompute derived members. Fires only when every field is present, so you never guard for half-built state.
• on_sym_bound — same timing, but fires even while the object is still partial. For logic that must run on an incomplete program.
• on_sym_change — receives the exact field_updates, so an expensive derivation can refresh only what the change actually touched.

Notification travels upward: an edit deep in a tree notifies the object, then each ancestor, keyed by the path it saw the change at — so a holder can invalidate a cache it computed from a child it doesn't directly own.

Call super() in these hooks. Overriding on_sym_change without it swallows the cascade — on_sym_ready stops firing and derived state silently goes stale.

For code that cares about position rather than value, on_topo_parent_change and on_topo_path_change fire when an object is adopted, moved, or detached — the advanced end, for caches keyed on where a node sits.

Two programs, structurally compared

pg.diff(baseline, candidate)
# Exp(model=Model(units=Diff(left=8, right=16)))

Not a text diff of two dumps — a structural one that answers which knob differs, at the position it differs. When runs are configs, this is the question you actually ask.

Values that resolve from where they sit

class Layer(pg.Object, topo=True):
    dropout: Any = pg.symbolic.ValueFromParentChain()

Net(dropout=0.5, layer=Layer()).layer.dropout    # 0.5 — read from the ancestor

The alternative is threading the argument through every constructor between the two, or a global. Here the field is optional at construction and resolved at read time from its position in the tree.

Code you don't own

Sym = pg.symbolize(ThirdPartyClass)     # make it symbolic, no source edits

with pg.detour([(Adam, LAMB)]):         # change what a LIBRARY constructs
    third_party_training_loop()         # → returns a LAMB

detour redirects construction inside code you cannot edit — nested, transitive, outer-scope-wins. The usual answers are monkeypatching or forking.

For functions, pg.functor gives you the thing Python has no construct for: a bound function you can hold, inspect, and rebind before calling it.

Provenance for generated programs

with pg.track_origin():
    variant = base.sym_clone()

variant.sym_origin.source is base       # True
variant.sym_origin.tag                  # 'clone'

When programs are produced by other programs — mutated, evolved, sampled — the bookkeeping of where did this one come from is otherwise yours to build.

Programs written by algorithms

Once a program is a value, an algorithm can produce one. Evolution is the clearest case: mutation and crossover are ordinary operations on the structure, so a search algorithm needs to know nothing about your classes.

algo = pg.algo.evolution.regularized_evolution(
    pg.algo.evolution.mutators.Uniform())

for net, feedback in pg.iter(space, 30, algo):
    feedback(score(net))          # your objective; the algorithm does the rest

Each net is a real, validated instance of your class — not a parameter vector you have to decode. The algorithm mutates the representation, so the same Uniform() mutator works on a neural architecture, a tour of cities, or a symbolic expression, without being written for any of them.

Worked examples: OneMax · Traveling Salesperson · Function Regression · writing your own operations

Seeing it

pg.to_html(trainer)      # an interactive, collapsible tree

A nested program is unreadable as repr output. Any symbolic value renders as a browsable tree, in a notebook or a file.

The shape of the thing

Everything above comes from one property — the object retains its own structure — and they compose, because they all speak about the same tree:

• a search space is a program with holes that stand for many values
• materializing one is narrowing those holes to a program
• a partial is a program with holes that are not yet decided
• a patch is a rule from program to program
• a diff is the difference between two of them

So a program stops being only what your code runs and becomes what your code produces. That is the category PyGX is in — not a faster way to validate a dataclass, but a way to write programs whose subject is other programs.

Living with it

The above is the reason to reach for PyGX. This is what it's like once you have.

It is an ordinary Python class. No registration, no separate schema, no config DSL. class Model(pg.Object) gives you a keyword-only __init__, validation on construct and on assignment, JSON round-trip that restores the real class, value equality, and pg.diff — and everything above already works on it.

The tree is opt-in. topo=True adds tree positions (topo_path, topo_parent), change notification that travels up through parents, and contextual values. Same class, same callers, same field declarations — one keyword. Objects that never need a position never pay for one.

Performance

The symbolic model used to be a tax; it isn't anymore. The hot paths run in a native Rust core (pygx-core, installed automatically), while the pure-Python implementation remains the executable specification — the full suite runs against both cores on every PR, so they cannot diverge.

Median ns/op on a 3-field object (Apple Silicon; read ratios, not absolutes — the full report covers ~50 operations across scales):

Validated construction beats pydantic v2 with the whole symbolic model attached, and overtakes a plain dataclass past ~8 fields; attribute reads are at parity. Deserialization and hashing are slower — PyGX emits and dispatches a _type tag so JSON round-trips back to the real class, which is strictly more work than producing a bare dict. The report tracks all of it honestly.

Wheels ship for Linux (glibc + musl, x86_64 + aarch64), macOS (Intel + Apple Silicon), and Windows on CPython 3.12–3.14 — plus a genuinely free-threaded 3.14t wheel (the GIL stays off; crash-freedom and per-operation atomicity per the threading contract in docs/design/gil-free.md §3). On anything else PyGX falls back to the pure-Python core with identical behavior.

When you don't need it

If your objects are only ever built and read — request payloads, plain records, a config that is loaded once and never manipulated — you don't need any of this, and a dataclass or pydantic model is the better tool. Keeping the structure costs memory and construct time you won't spend.

PyGX earns its keep at the point where a program becomes something you operate on. If you have never wanted to ask a program a question, you don't need it.

Install

pip install pygx

Nightly build:

pip install pygx --pre

Optional extras:

pip install "pygx[io]"          # fsspec-backed remote IO (GCS, S3, ...)
pip install "pygx[concurrent]"  # parallel execute/map with retries + progress

Requires Python 3.12+.

Upgrading from an earlier version? See the 0.4 migration guide — the pg.Object default changed from topo=True to topo=False, and the position API moved to topo_*.

Background

PyGX was originally built at Google Brain / DeepMind by Daiyi Peng to power automated machine learning (AutoML), under the name PyGlove. The abstraction underneath — symbolic object-oriented programming — turned out to be much more general than AutoML, and PyGlove grew into a toolkit for advanced Python programming used well beyond ML. The original PyGlove paper was published at NeurIPS 2020.

Documentation & examples

• Bird's-eye view of PyGX — 5–10 minute tour of the core ideas.
• User Guide — topic-specific tracks for Python, ML, AutoML, and Evolution.
• Learning PyGX — conceptual material on Symbolic Object-Oriented Programming and Symbolic Detour.
• Authoring pg.Object subclasses — style guide for field defaults, ClassVar config, and __init__ shape.
• API Reference — generated from source.

Runnable notebooks live in docs/notebooks/ and source examples in examples/.

Citing PyGX

@inproceedings{peng2020pyglove,
  title={PyGlove: Symbolic programming for automated machine learning},
  author={Peng, Daiyi and Dong, Xuanyi and Real, Esteban and Tan, Mingxing and Lu, Yifeng and Bender, Gabriel and Liu, Hanxiao and Kraft, Adam and Liang, Chen and Le, Quoc},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  volume={33},
  pages={96--108},
  year={2020}
}

License

Apache License 2.0. PyGX is derived from PyGlove (also Apache 2.0); see LICENSE and the per-file copyright headers for attribution.

PyGX is developed by Daiyi Peng.