> ## Documentation Index
> Fetch the complete documentation index at: https://docs.laragent.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# What are agents?

> Agents are the core building blocks of LarAgent, representing AI-powered assistants that can interact with users, execute tools, and maintain conversation context.

<Note>
  Agent classes are the foundation of everything in LarAgent. They define how your AI assistants behave, what capabilities they have, and how they process information.
  Other LarAgent features like Tools, Storage, Prompts, etc all revolve around agents.
</Note>

## What are Agents?

In LarAgent, an **Agent** is a PHP class that represents an AI-powered assistant. Each agent encapsulates:

* **Instructions** — The system prompt that defines the agent's personality, role, and behavior
* **Model configuration** — Which LLM to use and how it should respond
* **Tools** — Functions the agent can call to perform actions or retrieve information
* **Context** — How conversations (chat history) and other data models are stored and retrieved

Think of an agent as a specialized AI assistant tailored to a specific task or domain.

You might have a `CustomerSupportAgent` for handling support tickets, a `DataAnalysisAgent` for processing reports, or a `SchedulingAgent` for managing appointments.
Or one agent that ochestrates all mentioned above!

## Why Agents?

LarAgent's agent-centric architecture provides several benefits:

<CardGroup cols={2}>
  <Card title="Reusability" icon="recycle">
    Define an agent once and use it across your application with different chat sessions.
  </Card>

  <Card title="Encapsulation" icon="box">
    Keep all AI-related configuration in one place — instructions, tools, and settings.
  </Card>

  <Card title="Testability" icon="flask-vial">
    Test your agents in isolation with predictable inputs and outputs.
  </Card>

  <Card title="Flexibility" icon="sliders">
    Override any configuration at runtime for specific use cases.
  </Card>
</CardGroup>

## Agent Lifecycle

When you interact with an agent, here's what happens:

<Steps>
  <Step title="Initialization">
    The agent is created with its configured instructions, model, and tools.
  </Step>

  <Step title="Message Processing">
    Your message passes through the `prompt()` method, where you can add context or transform the input.
  </Step>

  <Step title="LLM Request">
    The agent sends the conversation history and your message to the configured LLM.
  </Step>

  <Step title="Tool Execution">
    If the LLM requests tool calls, the agent executes them and continues the conversation.
  </Step>

  <Step title="Response">
    The final response is returned and stored in the chat history.
  </Step>
</Steps>

## Quick Example

Here's what a simple agent looks like:

```php theme={null}
namespace App\AiAgents;

use LarAgent\Agent;

class AssistantAgent extends Agent
{
    protected $model = 'gpt-4o-mini';
    
    protected $history = 'cache';

    public function instructions()
    {
        return "You are a helpful assistant. Be concise and friendly.";
    }
}
```

Using the agent is straightforward:

```php theme={null}
// Start or continue a conversation
$response = AssistantAgent::for('user-123')->respond('Hello, how can you help me?');

// Or for one-off interactions without persisted history
$response = AssistantAgent::ask('What is 2 + 2?');
```

## Next Steps

<CardGroup cols={2}>
  <Card title="Create & Configure" icon="plus" href="/v1/agents/creation">
    Learn how to create agent classes and configure their behavior.
  </Card>

  <Card title="Responses" icon="message" href="/v1/responses/overview">
    Learn how to interact with agents and handle their responses.
  </Card>

  <Card title="Streaming" icon="wave-pulse" href="/v1/responses/streaming">
    Stream responses in real-time for better user experience.
  </Card>

  <Card title="Tools" icon="wrench" href="/v1/tools/overview">
    Extend your agents with custom tools and capabilities.
  </Card>
</CardGroup>