A PHP agentic framework for building production-ready AI-driven applications. Connect components (LLMs, vector DBs, memory) to agents that interact with your data. Its modular architecture makes it ideal for building RAG, multi-agent workflows, or business process automation.
One-Minute Overview#
Neuron is a PHP framework for creating and orchestrating AI Agents. It allows you to integrate AI entities in your existing PHP applications with a powerful and flexible architecture. We provide tools for the entire agentic application development lifecycle, from LLM interfaces, to data loading, to multi-agent orchestration, to monitoring and debugging. In addition, we provide tutorials and other educational content to help you get started using AI Agents in your projects.
Core Value: Provides a complete development toolchain for PHP developers to build complex agent systems with simple and intuitive methods.
Quick Start#
Installation Difficulty: Medium - Requires PHP knowledge and basic AI concepts, but provides CLI tools to simplify the initialization process
composer require neuron-core/neuron-ai
Is this right for me?
- ✅ PHP Application Integration: Adding AI capabilities to existing PHP applications
- ✅ RAG Applications: Building Q&A systems based on retrieval-augmented generation
- ✅ Business Process Automation: Automating complex business processes through agents
- ❌ High-Performance Scenarios: Not ideal for extremely high concurrency or ultra-low latency requirements
- ❌ AI Beginners: Developers without basic AI/LLM knowledge may find it challenging to start
Core Capabilities#
1. Agent Creation and Orchestration#
- Provides an Agent base class that automatically manages advanced mechanisms like memory, tools, and function calls Actual Value: Reduces development complexity, allowing developers to focus on business logic rather than low-level implementation
2. Multi-Model Provider Support#
- Supports switching between multiple LLM providers with just one line of code without affecting agent implementation Actual Value: Avoids vendor lock-in, allowing flexible selection of the most suitable model based on requirements
3. Tools and Toolkits System#
- Enables agents to perform specific tasks like reading from databases by adding tools or toolkits Actual Value: Extends agent capabilities for seamless integration with external systems
4. Monitoring and Debugging#
- Provides Inspector integration to visualize agent execution processes and understand model decision-making Actual Value: Improves AI application interpretability and reliability, making troubleshooting easier
5. RAG Support#
- Complete RAG implementation supporting vector store and embedding model integration Actual Value: Builds Q&A systems based on private knowledge, improving response accuracy and relevance
6. Structured Output#
- Allows agents to understand natural language but output structured data Actual Value: Facilitates integration of AI output into other downstream systems for business process automation
Tech Stack and Integration#
Development Language: PHP 8.1+ Main Dependencies: Composer, various LLM API providers (Anthropic, OpenAI, Gemini, etc.), vector databases (Pinecone, etc.), embedding model providers (Voyage, etc.) Integration Method: PHP library/framework
Ecosystem and Extensions#
- Framework Integration: Deep integration with Laravel and Symfony, following respective ecosystem patterns
- MCP Connector: Can connect to tools exposed by MCP servers without manual implementation
- Monitoring Tools: Integration with Inspector platform for powerful monitoring and debugging capabilities
Maintenance Status#
- Development Activity: Actively maintained with new features and tutorials
- Recent Updates: Recent stable releases available
- Community Response: Provides contact email for security vulnerabilities with prompt response to issues
Documentation and Learning Resources#
- Documentation Quality: Comprehensive
- Official Documentation: https://docs.neuron-ai.dev/overview/fast-learning-by-video
- Example Code: Rich code examples including agent creation, tool usage, RAG implementation, etc.