seine-rs
A rules/decisioning engine over Arrow dataframes — a Rust port of a bounded Drools (DRL) subset, differentially certified against real Drools 9.44.0.Final: every supported behavior is pinned by running the same scenarios through both engines, plus multi-seed fuzz campaigns (zero divergences to completion).
import polars as pl
import seine_rs
from seine_rs import Rule, fact
@fact
class Person:
name: str
age: int
score: float
@fact
class Flagged:
name: str
score: float
adults = Rule("Adults")
p = adults.when(Person, Person.age >= 18)
adults.then_insert(Flagged, name=p.name, score=p.score)
people = pl.DataFrame({"name": ["ada", "kurt"], "age": [36, 17], "score": [91.5, 99.0]})
res = seine_rs.run([adults], {Person: people, Flagged: []})
pl.DataFrame(res.derived["Flagged"]) # facts the rules created
pl.DataFrame(res.firings) # full audit trail
-
Bulk, columnar boundary: Arrow tables in (polars / pyarrow / pandas ≥ 2.2, zero-copy via the PyCapsule interface), Arrow tables out. Row-object lists (
@factinstances, dicts, dataclasses, Pydantic models) work too. -
Rules in Python or DRL: the Python builder compiles to DRL text, so both paths run the identical certified engine. Anything outside the certified grammar is a definition-time
CompileError. Coming from Drools: DRL here is rules-only — nopackage, nodeclare; fact types live in Python and schemas are inferred from them. -
Full working-memory lifecycle:
insert → fire → update/delete by handle → fire, each certified differentially. -
No Python in the hot path: conditions, aggregates and salience are native; callbacks are observers over immutable results.
-
The why-machine:
sess.why(handle)answers with the justifying rule, matched tuple and firing seq;justifications()walks the whole graph;acc_sources()walks an aggregate to the exact source facts that sum to its value. The audit surface is certified alongside the semantics. -
Schemas auto-register from the
@factclasses your rules reference —Session([rule])alone works;insert_row(Account(...))needs no type argument. -
Linear where it counts: alpha, joins, aggregates, TMS chains/teardowns, and bulk update churn all measure linear under a doubling-size ladder run through this public API (
tools/bench_wheel.pyin the repo). The headline: 32k updates through a join took 712 s on the 0.4.55 wheel (quadratic — engine and bindings each contributed a lane) and 219 ms on 0.4.57. Honest counterpoint: deep logical-chain TMS still runs ~3× behind warm real-Drools on the same box — the decomposition is in the repo's commit record.
Install: pip install seine-rs — the import is import seine_rs.
Source, scenario corpus, the guided tours (demo/tours/), and the
full decision log (D-001 onward):
https://github.com/sl-agentics/seine
1 maintainer
Metadata
1 maintainer
Release files for seine-rs 0.4.58
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Source distribution (sdist)
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Built distributions (wheels)
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| seine_rs-0.4.58-cp39-abi3-win_amd64.whl | CPython 3.9 | abi3 | Windows x86-64 | Details |
| seine_rs-0.4.58-cp39-abi3-musllinux_1_2_x86_64.whl | CPython 3.9 | abi3 | Linux musl 1.2+ x86-64 | Details |
| seine_rs-0.4.58-cp39-abi3-musllinux_1_2_aarch64.whl | CPython 3.9 | abi3 | Linux musl 1.2+ ARM64 | Details |
| seine_rs-0.4.58-cp39-abi3-manylinux_2_28_x86_64.whl | CPython 3.9 | abi3 | Linux glibc 2.28+ x86-64 | Details |
| seine_rs-0.4.58-cp39-abi3-manylinux_2_28_aarch64.whl | CPython 3.9 | abi3 | Linux glibc 2.28+ ARM64 | Details |
| seine_rs-0.4.58-cp39-abi3-macosx_11_0_arm64.whl | CPython 3.9 | abi3 | macOS 11.0+ ARM64 | Details |
| seine_rs-0.4.58-cp39-abi3-macosx_10_12_x86_64.whl | CPython 3.9 | abi3 | macOS 10.12+ x86-64 | Details |
Total release size: 25.0 MB
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