laravel-travel-agent

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laravel-travel-agent

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Multi-Agent workflow running into a Laravel application with Neuron PHP AI framework

laravel-travel-agent 최신버젼 다운로드

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# Travel Planner Agent in Laravel

![Neuron Travel Planner Agent](https://raw.githubusercontent.com/neuron-core/laravel-travel-agent/main/public/images/screen.png)

## Prerequisites

```yaml
node.js: 20.19.4

php: 8.2
composer: 2.8.9

laravel: 12.0
neuron-ai: 2.0
```

Need to get SerpApi key from https://serpapi.com/dashboard

```dotenv
SERPAPI_KEY=
```

## Getting Started

```bash
npm i
composer install
```

```bash
# migrate SQLite database
php artisan migrate

# generate APP_KEY in .env
php artisan key:generate

######################################
# Or, All-in-One command
composer setup

php artisan serve
```

Visit http://localhost:8000/dashboard

-   http://localhost:8000/register

-   http://localhost:8000/login

## About This Project

This project demonstrates how to integrate multi-agent workflows in a Laravel application
using [Neuron](https://docs.neuron-ai.dev) PHP AI framework.

Stack Used:

-   [Laravel](https://laravel.com) and [Livewire](https://livewire.laravel.com/) for the application.
-   [Neuron Workflow](https://docs.neuron-ai.dev/workflow/getting-started) for multi-agent orchestration.
-   [SerpAPI](https://serpapi.com) for finding hotels, flights and places to visit comprehensive research reports on any topic using large language models,
    with a focus on modularity, extensibility, and real-time results.

![](https://raw.githubusercontent.com/neuron-core/laravel-travel-agent/main/public/images/chart.jpeg)

## Neuron PHP framework

Neuron is an agentic framework that allows you to create full-featured AI Agents in PHP.
It definitively fills the gap for AI Agents development between PHP and other ecosystems like Python or Javascript.

It provides you with a standard toolkit to implement AI-driven applications drastically reducing vendor lock-in.
You can switch between LLMs, vector stores, embedding providers, etc. with just a few lines of code without the
need to refactor big portions of your application.

If you are new to AI Agents development, or you already have experience, Neuron can be the perfect playground
to move your idea from experiments to reliable production implementations.

Check out the documentation: https://docs.neuron-ai.dev

## How to use this project

Download the project on your machine and open your terminal in the project directory and run the command below:

```
composer setup
```

The command will create the `.env` file in your project root, so you need to provide the API keys based on
the services you want to connect with.

```dotenv
# At least one required
ANTHROPIC_API_KEY=
GEMINI_API_KEY=
OPENAI_API_KEY=

#Required
SERPAPI_KEY=

# Optional
INSPECTOR_INGESTION_KEY=
INSPECTOR_TRANSPORT=sync
```

Open the project in your browser, register an account, and start planning your trip.

## Workflow architecture and Nodes

-   **TravelPlannerAgent**: Orchestrates the overall itinerary generation process

### Nodes

-   **Receptionist**: Collect all the information from the user
-   **Delegator**: Generates single reports for flights, hotels, and places to visit
    -   _Flights_
    -   _Hotels_
    -   _Places_
-   **GenerateItinerary**: Generates the final report

## Monitoring & Debugging

Integrating AI Agents into your application, you're not working only with functions and deterministic code,
you program your agent also influencing probability distributions. Same input ≠ output.
That means reproducibility, versioning, and debugging become real problems.

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.

Why is the model taking certain decisions? What data is the model reacting to? Prompting is not programming
in the common sense. No static types, small changes break output, long prompts cost latency,
and no two models behave exactly the same with the same prompt.

The best way to do this is with [Inspector](https://inspector.dev). After you sign up,
make sure to set the `INSPECTOR_INGESTION_KEY` variable in the application environment file to start monitoring:

```dotenv
INSPECTOR_INGESTION_KEY=fwe45gtxxxxxxxxxxxxxxxxxxxxxxxxxxxx
```

After configuring the environment variable, you will see the agent execution timeline in your Inspector dashboard.

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