Rule Builder Laravel Package

Product Decisions This Supports

• Build vs. buy for conditional logic. Rather than hard-coding business conditions (if plan == 'pro' && seats > 10 && region in [...]) across controllers, jobs, and admin panels, this package gives you a single place to define, serialize, and evaluate rules. That is a real architectural decision with a long tail — every rule you hard-code becomes a ticket later.
• Admin-configurable behavior without a deploy. The core PM value is moving rule authoring from "engineering sprint" to "ops/config change." Discount eligibility, trial extensions, approval thresholds, feature gating, lead scoring, notification routing, and content visibility are all candidates. Quantify the win: how many rule-change requests per quarter currently require a release cycle?
• Front-end rule builder feasibility. A rule model that serializes cleanly (to/from JSON or array) is the prerequisite for a no-code condition editor. This package can serve as the backend contract for that UI, letting you validate the concept before committing to a custom rule schema you'd have to maintain forever.
• Roadmap sequencing. Adopting a thin rule layer lets you ship "configurable rules" as a v1 capability and defer a full DSL, versioned rule engine, or decision-table product until you have evidence of demand. It's a cheap option on a bigger bet.
• Deferring a platform investment. If you have one or two surfaces that need conditional logic, this is cheaper than standing up a rules service — but see the adoption criteria below, because the reverse is also true.

When to Consider This Package

Reasonable fit when:

• You need structured, data-defined conditionals evaluated server-side against a context object — not a full decision engine with chaining, priority, or conflict resolution.
• Rules are authored by a small internal team or via an admin UI you control, not by external customers at scale.
• You're willing to spike it (1–2 days) and wrap it behind your own interface so it stays swappable.
• You accept the maintenance profile: 1 GitHub star, 0 dependents, single-vendor origin, MIT licensed. That means you are effectively the maintainer if it breaks. Budget for vendoring or forking, and pin the version.
• The rule set is modest (dozens to low hundreds) and evaluated per-request or per-job, not in a hot loop over millions of rows.

Look elsewhere when:

• You need mature ecosystem support, security review sign-off, or long-term vendor SLAs. With 0 dependents there's no community signal and no third-party hardening — expect to own the audit yourself.
• Rules must push down into SQL/Elasticsearch for filtering large datasets; most rule-builder libraries evaluate in PHP memory, which will not scale to that use case.
• You need versioning, audit trails, rule conflict detection, or simulation/backtesting — typically enterprise rules-engine territory (or a build).
• Your team has no PHP capacity to absorb an unmaintained dependency. The last release (March 2025) is recent, which is a positive, but a single-maintainer package is a bus-factor risk regardless.
• The logic is genuinely simple. Three if statements behind a config flag don't need a dependency; the abstraction cost exceeds the benefit.

How to Pitch It (Stakeholders)

To executives: This is a small, MIT-licensed PHP library that lets us define business rules as data instead of code. The strategic value isn't the library itself — it's that it lets us test whether "ops can change rules without an engineering ticket" is a capability worth investing in, for roughly a day of engineering time rather than a quarter. Treat it as a time-boxed experiment with a clear kill criterion, not a platform commitment.

To engineering: Low surface area, permissive license, easy to wrap behind our own RuleEvaluator interface so we can swap implementations (including a homegrown one) without touching call sites. Caveat up front: one star, zero dependents, single maintainer — we vendor or fork it, pin the version, and own the security review. The proposed spike is: define two real rules, evaluate them, and measure the effort to replace the dependency behind our interface. If replacement is trivial, the vendor risk is bounded and the experiment is worth running.