Building Better Laravel AI Agents with Neo4j Boost

Laravel applications are made up of controllers, models, services, jobs, events, dependencies, database relationships, and many other interconnected components. As applications grow, it becomes increasingly difficult for both developers and AI assistants to understand how these pieces fit together.

In this hands-on workshop, we’ll explore how Neo4j Boost uses Knowledge Graphs to turn Laravel application architecture into connected, queryable context, and how that context can be made available to AI agents through MCP (Model Context Protocol).

We’ll start by understanding how Laravel application architecture can be represented as a graph using Neo4j. From there, we’ll explore how an AI assistant can query this graph to understand relationships that traditional code search may miss.

Throughout the workshop, you’ll see how graph-powered context can help AI agents reason about a Laravel application with greater observability, efficiency, and correctness.

We’ll cover:

• How Laravel components and their relationships can be modeled as a Knowledge Graph.

• How Neo4j stores and queries architectural context.

• How MCP allows AI assistants and agents to interact with that context.

• How Neo4j Boost connects Laravel, Neo4j, MCP, and AI.

• How graph context can help with debugging, code exploration, onboarding, and understanding dependencies.

• How Knowledge Graphs can complement traditional repository and code search.

• How to use Neo4j Boost in a Laravel project and experiment with graph-powered AI workflows.

By the end of the workshop, you’ll have a practical understanding of how Laravel + Neo4j + MCP + AI can work together to give AI agents a richer understanding of application architecture and help developers work more effectively with complex Laravel systems.

By the end of the workshop, participants will be able to:

1. Understand Knowledge Graphs and how they can represent Laravel application's architecture, dependencies, and relationships.

2. Use Neo4j to model Laravel context and query relationships between application components.

3. Connect Laravel application context to AI using MCP (Model Context Protocol).

4. Build better AI-assisted workflows by giving agents structured architectural context beyond traditional code search.

5. Apply graph-powered context to real development problems, including debugging, dependency exploration, onboarding, and architecture discovery.

6. Understand observability, efficiency, and correctness as key benefits of providing AI agents with richer application context.

7. Get hands-on with Neo4j Boost and understand how it can be integrated into Laravel projects.