Getting Started in 3 Steps

1) Install

composer require neuron-core/neuron-ai

2) Create an Agent

Neuron provides you with the Agent class you can extend to inherit the main features of the framework and create fully functional agents. This class automatically manages some advanced mechanisms for you, such as memory, tools, up to RAG (Retrieval Augmented Generation). You can go deeper into these aspects below.

Let's create an Agent with the command below:

vendor/bin/neuron make:agent DataAnalystAgent

<?php

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Agent\SystemPrompt;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;

class DataAnalystAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    protected function instructions(): string
    {
        return "You are a data analyst expert in creating reports from SQL databases.";
    }
}

3) Talk to the Agent

Send a message to the agent to get a response from the underlying LLM:

$agent = DataAnalystAgent::make();


$response = $agent->chat(
    new UserMessage("Hi, I'm Valerio. Who are you?")
)->getMessage();
echo $response->getContent();
// I'm a data analyst. How can I help you today?


$response = $agent->chat(
    new UserMessage("Do you remember my name?")
)->getMessage();
echo $response->getContent();
// Your name is Valerio, as you said in your introduction.

Monitoring & Debugging

Many of the Agents you build with Neuron will contain multiple steps with multiple invocations of LLM calls, tool usage, access to external memories, etc. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly your agent is doing and why.

Supported LLM Providers

Tools & Toolkits

Make your agent able to perform concrete tasks, like reading from a database, by adding tools or toolkits (collections of tools).

<?php

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Agent\SystemPrompt;
use NeuronAI\Tools\ToolProperty;
use NeuronAI\Tools\Tool;
use NeuronAI\Tools\Toolkits\MySQL\MySQLToolkit;

class DataAnalystAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    protected function instructions(): string
    {
        return (string) new SystemPrompt(
            background: [
                "You are a data analyst expert in creating reports from SQL databases."
            ]
        );
    }

    protected function tools(): array
    {
        return [
            MySQLToolkit::make(
                \DB::connection()->getPdo()
            ),
        ];
    }
}

Ask the agent something about your database:

$response = DataAnalystAgent::make()->chat(
    new UserMessage("How many orders we received today?")
)->getMessage();

echo $response->getContent();

MCP Connector

Instead of implementing tools manually, you can connect tools exposed by an MCP server with the McpConnector component:

<?php

namespace App\Neuron;

use NeuronAI\Agent\Agent;
use NeuronAI\MCP\McpConnector;
use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\Tools\ToolProperty;
use NeuronAI\Tools\Tool;

class DataAnalystAgent extends Agent
{
    protected function provider(): AIProviderInterface
    {
        ...
    }

    protected function instructions(): string
    {
        ...
    }

    protected function tools(): array
    {
        return [
            // Connect to an MCP server
            ...McpConnector::make([
                'command' => 'npx',
                'args' => ['-y', '@modelcontextprotocol/server-everything'],
            ])->tools(),
        ];
    }
}

Structured Output

There are scenarios where you need Agents to understand natural language, but output in a structured format, like business processes automation, data extraction, etc. to use the output with other downstream systems.

use App\Neuron\MyAgent;
use NeuronAI\Chat\Messages\UserMessage;
use NeuronAI\StructuredOutput\SchemaProperty;

/*
 * Define the output structure as a PHP class.
 */
class Person
{
    #[SchemaProperty(
        description: 'The user name',
        required: true
    )]
    public string $name;

    #[SchemaProperty(
        description: 'What the user love to eat'
    )]
    public string $preference;
}

/*
 * Talk to the agent requiring the structured output
 */
$person = MyAgent::make()->structured(
    new UserMessage("I'm John and I like pizza!"),
    Person::class
);

echo $person->name ' like '.$person->preference;
// John like pizza

RAG

To create a RAG, you need to attach some additional components other than the AI provider, such as a vector store, and an embeddings provider.

Let's create a RAG with the command below:

vendor/bin/neuron make:rag MyChatBot

Here is an example of a RAG implementation:

<?php

namespace App\Neuron;

use NeuronAI\Providers\AIProviderInterface;
use NeuronAI\Providers\Anthropic\Anthropic;
use NeuronAI\RAG\Embeddings\EmbeddingsProviderInterface;
use NeuronAI\RAG\Embeddings\VoyageEmbeddingProvider;
use NeuronAI\RAG\RAG;
use NeuronAI\RAG\VectorStore\PineconeVectorStore;
use NeuronAI\RAG\VectorStore\VectorStoreInterface;

class MyChatBot extends RAG
{
    protected function provider(): AIProviderInterface
    {
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }

    protected function embeddings(): EmbeddingsProviderInterface
    {
        return new VoyageEmbeddingProvider(
            key: 'VOYAGE_API_KEY',
            model: 'VOYAGE_MODEL'
        );
    }

    protected function vectorStore(): VectorStoreInterface
    {
        return new PineconeVectorStore(
            key: 'PINECONE_API_KEY',
            indexUrl: 'PINECONE_INDEX_URL'
        );
    }
}

Workflow

Think of a Workflow as a smart flowchart for your AI applications. The idea behind Workflow is to allow developers to use all the Neuron components like AI providers, embeddings, data loaders, chat history, vector store, etc., as standalone components to create totally customized agentic systems.

Agent and RAG classes represent a ready to use implementation of the most common patterns when it comes to retrieval use cases, or tool calls, structured output, etc. Workflow allows you to program your agentic system completely from scratch. Agent and RAG can be used inside a Workflow to complete tasks as any other component if you need their built-in capabilities.

Security Vulnerabilities

Official documentation