AFEAF is a framework that empowers AI agents to become capable digital coworkers while maintaining enterprise-grade reliability and security.
We envision a future where AI agents work seamlessly alongside humans in enterprises, forming a collaborative ecosystem that amplifies human capabilities rather than replacing them. For a detailed exploration of our vision and implementation examples, see docs/vision.md.
- Registry: Central hub for discovering and managing AI capabilities
- Message Broker: Reliable communication backbone between components
- LLM Service: Centralized gateway for AI reasoning and decision-making
- Authentication: Security and access control infrastructure
- Project Database: Core project and operational data
- Knowledge Bases: Shared enterprise knowledge and context
- Applications: Event monitors and enterprise solutions
- Tools:
- Memory Tools: Agent knowledge and conversation persistence
- Integration Tools: Email, calendar, etc.
- Analysis Tools: Data processing and insights
- Agents: AI entities that perform complex tasks
afeaf/
├── docs/ # Architecture, patterns, interfaces
├── registry/ # Central service registry
├── broker/ # Message broker service
├── auth/ # Authentication service
├── databases/ # Database implementations
├── applications/ # Applications and monitors
├── tools/ # Reusable tool implementations
│ └── memory/ # Memory persistence tools
├── agents/ # AI agents
└── README.md
- Vision and Concepts - Detailed overview of AFEAF's vision and implementation examples
- Architecture Guidelines - Technical guidelines and patterns
- Registry Service - Component registration and discovery
- Message Broker - Communication patterns
- LLM Integration - Language model integration
- Tools - Available tools and capabilities
- Memory - Memory and persistence capabilities