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Neuron Ai Laravel Package

neuron-core/neuron-ai

Neuron is a PHP framework for building full-featured agentic applications: AI agents with LLM interfaces, event-driven workflows, human-in-the-loop, multi-agent orchestration, streaming/UI protocols, MCP connectors, and async execution.

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The Agentic Framework of the PHP ecosystem to build production-ready AI driven applications. Connect components (LLMs, Tools, vector DBs, memory) to agents that interact with your data and UI.

Frequently asked questions about Neuron Ai
What is neuron-core/neuron-ai?
Neuron AI is a PHP framework for building and orchestrating AI agents in production applications. It covers the agentic development lifecycle, including LLM interfaces, data loading, RAG, multi-agent workflows, streaming, and monitoring. It works with Laravel and Symfony.
How do I install Neuron AI in a Laravel application?
Install the package with Composer, then use the vendor/bin/neuron CLI to scaffold and work with agents. Neuron provides a Laravel demo project, laravel-travel-agent, as a reference onboarding path. You can also register agents and tools through Laravel service providers.
Does Neuron AI support Laravel?
Yes. Neuron maps well to Laravel conventions through service container bindings, Eloquent chat history, filesystem chat history, database toolkits, events, queues, and streaming adapters. The architecture aligns with Laravel service providers, Artisan commands, and the DB facade. Laravel is a primary integration target alongside Symfony.
What PHP version does Neuron AI require?
Neuron AI requires PHP 8.1 or newer, so legacy Laravel applications on older PHP versions may be blocked. For pre-8.1 projects, you would need to upgrade PHP or isolate Neuron in a separate microservice or containerized instance. Modern Laravel applications should meet this requirement.
Which LLM providers can I use with Neuron AI?
Neuron supports more than 15 LLM providers and uses an AIProviderInterface abstraction to reduce provider lock-in. Switching from OpenAI to Anthropic or another provider typically requires configuration changes rather than rewriting agent logic. Local models such as Ollama are also mentioned as a cost-control option.
How does Neuron AI handle RAG and vector databases?
Neuron includes a RAG layer with high-level abstractions for retrieval-augmented generation. You can connect vector databases, load data, and let agents interact with your data and tools. This makes it suitable for applications that need agents to answer from private or external knowledge sources.
Can Neuron AI build multi-agent workflows and tool-using agents?
Yes. Neuron provides a Workflow core for orchestration, Agent abstractions, multi-agent workflows, checkpointing, human-in-the-loop interruption, MCP, and tool integration. Built-in toolkits such as MySQLToolkit and PostgreSQLToolkit let agents work with databases. For simpler cases, you can start with a single Agent and expand later.
How do I store chat history in Laravel with Neuron AI?
Neuron offers EloquentChatHistory, FileChatHistory, and SQLChatHistory integrations for persisting agent conversations. In Laravel, EloquentChatHistory bridges chat storage with the ORM and database layer. Teams with privacy or audit requirements should secure stored histories and customize audit trails where needed.
Can Neuron AI agents run asynchronously with Laravel queues?
Yes. Neuron supports asynchronous execution and event-driven workflows, and long-running agent workflows can be offloaded to Laravel queues. The Workflow system supports both synchronous and asynchronous operation, but async execution depends on your Laravel queue setup. This helps keep web requests responsive when agents perform complex tasks.
Is Neuron AI production-ready and how do I monitor agents?
Neuron is positioned for production-ready AI-driven applications and includes monitoring and debugging integrations, including Inspector. Monitoring adds latency and cost, so use it selectively for critical agents in production. Neuron EventBus can also integrate with Laravel events and logging for observability.
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