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 (@fact instances, 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 — no package, no declare; 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 @fact classes 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.py in 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

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Avatar for silverlakeagentics from gravatar.com silverlakeagentics

Credits

Author: Bryan McGuire

GitHub Statistics

Data verified by PyPI on Jul 22, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache-2.0)

Requires

Python >=3.9

Provides Extra

arrow pandas polars

Tags

rules-engine drools rete phreak arrow polars dataframe

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