Laravel
Grok AI chat and vision inside Laravel apps through one Composer package and a GrokAI facade.
For Grok-on-Laravel work this removes a weekend of boilerplate: facade, published config, Model enum, streaming via ChatOptions, URL-based vision, and a Testbench harness in one MIT package. The tradeoffs are scope and bus factor. It wraps one provider only, vision takes URLs rather than local uploads, and it is a single-maintainer project at 167 stars with four open issues and 4,414 installs. Great for prototypes, internal tools, and Laravel-native teams already committed to Grok; thin for anything needing provider redundancy, file-upload vision, or a support contract.
Verified 6d ago · liveness 54/100 · cite: rightaichoice.com/tools/laravel
- Laravel developers adding a Grok-powered chatbot or assistant to an existing app
- PHP teams who want NLP or text automation without writing their own HTTP client
- Projects needing both text and image understanding from one package
- Internal tools, dashboards, and MVPs where Grok is already the chosen provider
- Non-PHP stacks or anything outside the Laravel framework
- Teams that expect to swap between OpenAI, Anthropic, and Grok behind one interface
- Production features needing file-upload vision rather than URL-based image analysis
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Skip grok-php/laravel if you need one interface spanning OpenAI, Anthropic, and Grok, need to analyze locally uploaded images rather than public URLs, or need a support contract behind the wrapper.
Your Grok API usage is billed separately by the provider — the package itself is MIT-licensed and free, but every chat and vision call consumes your GROK_API_KEY quota.
The package itself is free and MIT-licensed, so the real cost line is your Grok API consumption, billed by the provider rather than the package author. That makes it cheap for solo Laravel devs and small internal tools, but teams that need provider redundancy should compare against a multi-vendor abstraction layer whose own overhead may be worth it.
In short
Laravel — Grok AI chat and vision inside Laravel apps through one Composer package and a GrokAI facade. Best for Laravel developers adding a Grok-powered chatbot or assistant to an existing app, PHP teams who want NLP or text automation without writing their own HTTP client, Projects needing both text and image understanding from one package. Free to use.
What's new in Laravel
Checked 6 days agoAcross the latest 1 update: 1 changelog entry.
What people actually say about Laravel — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
116 mentions across 7 sources (Hacker News, YouTube, Product Hunt, Bluesky, Stack Overflow, GitHub, Lemmy) · researched Jul 5, 2026.
Average across the 7 sources that answered — each source counts once, not each post.
- +Tight integration with Laravel's service provider and facade pattern.
- +Supports Laravel versions 10, 11, and 12 with PHP 8.2+.
- +Clean, intuitive API for chat, vision, and streaming.
- +Published config file for easy customization of model and timeout.
- +Setup artisan command makes configuration quick.
- −Very limited community feedback—hard to gauge real-world reliability.
- −No public testimonials or case studies on this package.
- −Dependent entirely on Grok API, no local fallback.
- −Vision and streaming features untested in community posts.
- −Laravel-specific; not usable outside Laravel ecosystem.
- • Grok API usage fees may apply if not free tier.
Viability Score
How well maintained and how widely used is Laravel? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Chat completions with Grok models through the GrokAI Laravel facade
- Vision analysis sending a public image URL plus a text prompt
- Streaming responses via ChatOptions with stream: true for real-time output
- Model enum covering grok-2, grok-2-1212, grok-2-latest, grok-beta, and vision variants
- Configurable model, temperature, and timeout in config/grok.php
- One-command setup: php artisan grok:install publishes config and writes .env key
- GROK_API_KEY read from .env instead of hardcoded credentials
- All errors wrapped in a single GrokException class
- Supports Laravel 10, 11, and 12 on PHP 8.2, 8.3, or 8.4
- Streaming-friendly for chatbots, assistants, and real-time apps
- PHPUnit config plus Orchestra Testbench support for Laravel integration tests
- MIT-licensed open source, distributed and auto-updated on Packagist
- Facade and ServiceProvider registration for Laravel's container
- Depends on grok-php/client ^1.3 for the underlying Grok API calls
- Vision entries including grok-2-vision-1212 and the experimental grok-vision-beta
About Laravel
grok-php/laravel is an MIT-licensed Composer package that puts Grok AI chat and vision inside Laravel applications without you hand-rolling HTTP calls to the xAI endpoints. It targets PHP teams already running Laravel 10, 11, or 12 who want conversational or image-understanding features bolted onto an existing codebase rather than a separate service. Install is two steps: composer require grok-php/laravel, then php artisan grok:install, which publishes config/grok.php and writes GROK_API_KEY into your .env. Daily use runs through the GrokAI facade. Chat calls take a standard role/content message array plus a ChatOptions object where you set the model, temperature, or timeout, or flip on stream: true for real-time output. Vision calls chain GrokAI::vision()->analyze() against a public image URL with a prompt. Responses expose a plain content() accessor, so you are not parsing nested JSON yourself. The Model enum ships several Grok endpoints, from the default Model::GROK_2 through grok-2-1212, grok-2-latest, grok-beta, and vision entries including grok-2-vision-1212 and the experimental grok-vision-beta. Errors funnel through a single GrokException class, and the package ships PHPUnit config plus Orchestra Testbench support so integration tests run in a Laravel harness rather than against the live API. Where it sits: this is a single-provider wrapper, not a multi-vendor abstraction. If your roadmap includes swapping between OpenAI, Anthropic, and Grok behind one interface, look at the official OpenAI PHP SDK or a broader Laravel AI package. If Grok is already the decision, the lean scope is the point.
Behind the Verdict
The value here is mechanical honesty: grok-php/laravel does a small number of things and does them the Laravel way. The ServiceProvider and GrokAI facade drop into the container, php artisan grok:install publishes config/grok.php and writes GROK_API_KEY to .env so no credential ever sits in code, and ChatOptions keeps model, temperature, timeout, and streaming in one typed object instead of scattered arrays. The Model enum is genuinely useful — Model::GROK_2 as the default, grok-2-1212 and grok-2-latest for chat, and four vision entries (grok-2-vision, grok-2-vision-latest, grok-2-vision-1212, plus the experimental grok-vision-beta) — because it stops typos in model strings from becoming runtime failures. Every error surfaces as one GrokException, which makes try/catch blocks predictable. Testing is where this package quietly earns its keep: PHPUnit config plus Orchestra Testbench means you can exercise chat and vision calls in a Laravel harness without burning live API credits. On the weak side, the dependency on grok-php/client ^1.3 means your request shapes are one abstraction removed from the xAI API, so a new xAI parameter may not be reachable until the underlying client ships it. Vision only accepts public image URLs — if your users upload screenshots, you must host them somewhere reachable first, which is a real architectural constraint. The bus factor is genuine: one maintainer, 167 stars, four open issues, and a README-only documentation surface (the docs source was not reached in this run, so treat documentation depth as an open question rather than a verdict). Version 1.1.0 landed 2025-02-25, and dev-main plus tags 1.0.0 and v1.1.0 are the published lineage. Fit: Laravel-native teams already standardized on Grok for chatbots, moderation, classification, or image understanding inside internal tools, dashboards, and MVPs. Misfit: shops that need to swap OpenAI, Anthropic, and Grok behind one interface, need file-upload vision, or need an SLA-backed relationship. For those, a multi-vendor Laravel AI package or a direct SDK integration is the better call, and you should plan an exit path before you scatter Grok AI::chat() calls across controllers.
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Real-world workflow fit
Concrete scenarios for the personas Laravel actually fits — and what changes day-one when you adopt it.
Runs composer require grok-php/laravel, then php artisan grok:install to publish config/grok.php and write GROK_API_KEY to .env, then wires GrokAI::chat() into an existing support form with Model::GROK_2 and stream: true.
Outcome: A streaming Grok assistant answering user messages in the app within an afternoon, with no hand-rolled HTTP client and errors caught through GrokException.
Uses GrokAI::vision()->analyze() with a public image URL and a prompt to classify incoming screenshots, picks grok-2-vision-1212 from the Model enum, and covers the path with an Orchestra Testbench test.
Outcome: Image triage inside the existing Laravel dashboard without standing up a separate AI service, with the vision call tested in a harness rather than against live credits.
Wraps an existing content pipeline with GrokAI::chat() calls using a role/content message array and a ChatOptions timeout, catching GrokException to fall back to a manual review queue.
Outcome: Automated moderation or classification running inside the Laravel queue and controllers already in place, with a single error type to handle.
Use Cases
- Build a Laravel chatbot that answers user queries with Grok AI through the GrokAI facade
- Automate content moderation or classification using NLP inside a Laravel app
- Add image recognition to Laravel apps via Grok's vision models and public image URLs
- Stream real-time AI responses for interactive user experiences with stream: true
- Prototype an internal assistant where Grok is already the chosen provider
- Wrap legacy Laravel admin tools with Grok-powered summarization or triage
- Run Grok chat and vision integration tests in a Laravel harness with Orchestra Testbench
Models Under the Hood
as of 2026-10-02
Limitations
- Requires PHP ^8.2, ^8.3, or ^8.4 and Laravel ^10.0, ^11.0, or ^12.0.
- This is an open-source package with a small community: 167 GitHub stars, 4 open issues, 4,414 Packagist installs, and 0 dependents.
- Documented usage is limited to the README.
- It relies on the external Grok API, subject to that API's own limits and pricing.
- Vision analysis only accepts public image URLs, not local file uploads.
- A single maintainer (Muhammed Elfeqy) may affect long-term support, and the package depends on grok-php/client ^1.3, one layer removed from the xAI API.
as of 2026-10-02
Verification history
We have re-verified Laravel 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Laravel's pricing actually pencils out — and where peers do it cheaper.
The package itself is free and MIT-licensed, so the real cost line is your Grok API consumption, billed by the provider rather than the package author. That makes it cheap for solo Laravel devs and small internal tools, but teams that need provider redundancy should compare against a multi-vendor abstraction layer whose own overhead may be worth it.
Setup time & first value
How long it actually takes to get something useful out of Laravel — broken out by persona, not the marketing-page minute.
Solo Laravel developer: about 15-30 minutes from composer require grok-php/laravel through php artisan grok:install, adding GROK_API_KEY, and getting a first GrokAI::chat() response. Adding vision: another 15 minutes if you already have publicly reachable image URLs. Writing Testbench-backed tests: a couple of hours depending on how much of your app you mock.
Switching to or from Laravel
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From direct cURL or Guzzle calls to the xAI API: replace hand-built request payloads with GrokAI::chat() plus ChatOptions and keep GROK_API_KEY in .env via php artisan grok:install.
- →From raw grok-php/client in a Laravel app: add the facade and ServiceProvider layer so chat and vision calls reach the container through GrokAI instead of manual client instantiation.
- →From a prototype controller with hardcoded model strings: move to the Model enum (Model::GROK_2 as default, grok-2-1212 or a vision entry as needed) to remove string-typo failures.
- →From ad hoc error handling around HTTP calls: consolidate on the GrokException class so one catch block covers chat and vision failures.
- ↗To the official OpenAI PHP SDK: rebuild chat and vision calls against OpenAI's client if provider flexibility outweighs Grok-specific model access.
- ↗To a multi-vendor Laravel AI package: wrap your GrokAI::chat() call sites behind an abstraction before swapping, so the facade does not leak into controllers.
- ↗To direct grok-php/client usage: drop the Laravel facade layer and instantiate the underlying client yourself if you are moving off Laravel.
- ↗To a self-hosted model stack: replace remote Grok calls with a local inference service, accepting the GPU and operations cost that comes with it.
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Laravel”, and we withheld 6: 6 could not be judged, because “Laravel” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Laravel.
Official links
Tools that pair well with Laravel
Common stack mates teams adopt alongside Laravel, with the specific reason each pairing earns its keep.
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Chainlit
Open-source Python package for building conversational AI apps with a production-ready chat UI, auth, and persistence.
Featured Head-to-Head Comparisons
Laravel vs Cognition Ai
Cognition AI and Laravel serve completely different purposes. Choose Cognition AI if you need an autonomous AI engineer that handles entire enterprise development workflows, including coding, triaging, and deploying. Choose Laravel if you are a Laravel developer wanting to integrate Grok AI into your PHP applications for chatbots or vision features. Laravel is free and lightweight; Cognition AI is enterprise-grade with a financial guarantee but likely costly at scale.
Laravel vs Poolside Ai
Poolside AI and Laravel serve completely different needs. Poolside is for regulated enterprises requiring air-gapped, auditable AI agents for high-stakes software development, backed by custom models and dedicated engineers. Laravel is a free, open-source PHP package that adds Grok AI to Laravel apps, ideal for developers integrating chatbots or vision without enterprise overhead. Choose Poolside if you need security and governance; choose Laravel if you want a quick, low-cost AI integration in Laravel.
Laravel vs Bito
Laravel and Bito serve completely different needs. Laravel is a free, lightweight PHP package for Laravel developers who want to quickly integrate Grok AI into their apps. Bito is a freemium enterprise-grade context layer for AI coding agents (like Cursor, Claude Code) that provides system-wide awareness across multi-repo projects. Buyers should choose Laravel if they are building AI features within a Laravel app, or Bito if they need to supercharge AI coding agents with cross-repo understanding and automated workflow integrations (Jira, Slack).
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