Graphql Api Laravel Package

Technical Evaluation

Architecture fit. This is a Bagisto module, not a general-purpose Laravel GraphQL server. It sits on top of Bagisto core, is built on Lighthouse, and exposes separate shop and admin SDL schemas whose resolvers bind directly to Bagisto repositories and services for pricing, tax, promotions, and stock. The fit is therefore binary: if Bagisto is the commerce platform, this package removes schema design, resolver implementation, token auth, and the cart/checkout mutation surface from the critical path — months of work the team would otherwise design, build, and defend in review. If Bagisto is not the platform, the package has no product value to weigh. Treat this as a go/no-go gate, not a scoring exercise.

Integration feasibility. High, conditional on version alignment. Install is composer require bagisto/graphql-api, publish the provider, migrate, and link storage; the endpoint defaults to /graphql. Prerequisites are a working Bagisto storefront (Laravel 10/11, PHP 8.x, MySQL/Postgres, Redis) confirmed before touching GraphQL. The package ships a GraphiQL tool for schema exploration and publishes config/lighthouse.php, which is where route, middleware, guards, and query depth/complexity are configured. Pin the package major to the Bagisto core major — they ship and upgrade together. Use ^2.3.2 or newer; earlier 2.3.x releases had a broken GraphiQL install command and namespace.

Technical risk. Ranked by impact:

• Version coupling (highest). Resolvers track Bagisto core internals closely. A patch release can carry an invisible-under-composer update fix — v2.3.2 contained both a GraphiQL install-command repair and a GDPR module fix. Upgrade package and core in lockstep, every time.
• Thin community support. 110 stars, zero dependents, MIT, no SLA. Third-party writeups are effectively absent; introspection and source are the documentation. Budget 0.25–0.5 FTE for self-support.
• Checkout scope gap. The schema provides cart mutations and order placement, but payment gateways, 3DS, webhook idempotency, and inventory reservation are ours to build. This is a named workstream, not an assumption.
• Resolver performance. Commerce graphs are deep (category → product → price → stock) and Bagisto's Eloquent relations do not always batch cleanly under GraphQL. N+1 on catalog and cart is real until dataloaders are enabled on the traversed relations.
• Guest cart loss. Token expiry between addToCart and placeOrder loses the cart with no user-facing error. Silent, and easy to miss in QA.
• Security surface. Unbounded pagination (first: 10000) is a trivial DoS vector on a public commerce graph; depth/complexity limits must be set from day one, not after launch.

Key questions for the product decision.

1. Are we on Bagisto, and committed to staying on it through the next two major cycles? (If not, stop here.)
2. How many consuming clients do we have or credibly plan — mobile, PWA, B2B portal, partner portal? One simple SEO-led front end rarely justifies the added operational surface; two or more makes this compelling.
3. Do we have, or will we hire, GraphQL fluency — dataloaders, complexity limits, cache tagging, schema diffing? This is a skill, not a checkbox.
4. Who owns schema versioning and the deprecation policy, and what is the client-pinning contract per release? This is a first-release workstream, not a later discovery.
5. Who funds upstream fixes, and are we prepared to contribute patches rather than fork?
6. What is our checkout scope beyond the schema — payments, 3DS, idempotency, inventory reservation — and does the launch date reflect it?
7. Does adopting this change the build-vs-buy comparison against Shopify Storefront API, Medusa, or Saleor? Re-run that analysis with this integration cost included, or the replatform business case is overstated.
8. Is the front-end framework decision (Next.js vs. Nuxt vs. native) being deferred on purpose? GraphQL is transport-agnostic, so the data layer can be committed while the client choice stays open — real option value in a volatile hiring market.

Integration Approach

Stack fit. Laravel 10/11 + Bagisto 2.3.x + Lighthouse + MySQL/Postgres + Redis, with a CDN in front of cacheable catalog reads. Clients are generated from introspection with graphql-codegen — never hand-written, never regenerated on a developer laptop. Server-side commerce rules stay authoritative; any pricing or tax logic appearing in the storefront is a defect.

Migration path. Sequential and deliberately boring:

1. Stand up Bagisto and confirm the Blade storefront works end-to-end before adding GraphQL.
2. Install the package, publish config, migrate, confirm /graphql resolves behind CORS and auth middleware.
3. Smoke-test in GraphiQL: products(first: 5) and createCart, then chain addToCart, addCustomerAddress, and checkout mutations until payloads are sensible.
4. Commit the introspected SDL to the repo as a versioned artifact and wire php artisan lighthouse:validate-schema plus an SDL diff into CI. Fail the build on unintended schema change.
5. Generate the typed client in CI from that SDL. Nothing downstream starts until codegen is green.
6. Build read paths (catalog, categories, search) before write paths (cart, checkout, account). Enable @dataloader on relations the landing and category pages actually traverse, and set max_query_complexity / max_query_depth before the first client is pointed at it.
7. Keep the Blade storefront deployable as a fallback until one full peak cycle has run headless.

Compatibility discipline. Pin both bagisto/graphql-api and Bagisto core in composer.json, upgrade together, and read release notes before every bump. Contract tests that execute the five or six canonical queries the storefront depends on, re-run on each upgrade, are cheaper than finding drift in production. Keep shop and admin schemas in separate client configs; expose the admin schema only behind an appropriate guard.

Sequencing across the roadmap. The first client is the expensive one; each subsequent channel (mobile app, PWA, partner/B2B portal, kiosk) is materially cheaper because it consumes one contract rather than a bespoke endpoint set. That reframes channel expansion from "N integrations" to "one contract, N clients" — a roadmap-shaping decision. Because clients are generated from a typed schema, small product bets (mobile pilot, price-list portal, a loyalty field added via a Lighthouse @field and a resolver calling Bagisto services) ship without touching the monolith. Extend above the schema; anything requiring edits inside vendor/ is a signal to rethink, since forks make the next upgrade a merge conflict.

Operational Impact

Maintenance. Ongoing cost is dominated by lockstep upgrades with Bagisto core, not by the GraphQL layer itself. Expect 0.25–0.5 FTE for upgrades, SDL diffs, contract-test upkeep, and periodic upstream fixes. Every Bagisto bump is a potential resolver break; the SDL diff in CI is the early-warning system. Recent releases (v2.3.2, August 2025) are a favorable signal of active maintenance, but there is exactly one maintainer organization behind it.

Support. MIT license, no SLA, small community, zero dependents. Support is internal by definition. Mitigate by reading and documenting the resolver behavior you depend on, keeping a short written record of schema assumptions, and treating upstream contribution as a planned activity rather than an escalation path that may not exist.

Scaling. Three levers, all needed before launch:

• Dataloaders on traversed catalog and cart relations, verified with Telescope or a Blackfire trace on a cold category page. When p95 resolver time spikes, the fix is usually a dataloader, not an index.
• Query ceilings. Depth and complexity limits plus a sane cap on cursor pagination, configured in config/lighthouse.php from day one.
• Cache split. Anonymous catalog reads are cacheable — key on query + variables, layer CDN or Redis in front, tag by product/category ID and invalidate through Bagisto events. Cart and checkout reads are per-user and must never be cached. Getting this boundary wrong is the most common way a headless commerce API leaks one customer's cart into another's session.

Failure modes to design against.

Ramp-up. The team needs GraphQL fluency — resolver-level performance, schema diffing, cache tagging — and Lighthouse conventions help but do not substitute for reading this package's source. Plan for a learning curve on the first client and a much shorter one on the second. Keep the Blade storefront deployed as a fallback until a peak cycle has run headless, and staff the checkout workstream (payments, 3DS, webhook idempotency, short-lived stock reservations) honestly in the quarter plan. The trade is fair — months of schema and resolver work skipped in exchange for owning the operational surface — but only if the FTE is actually allocated.