Melo is a multi-agent coordination framework designed to enable autonomous collaboration between AI agents in software development workflows. It provides a structured system where multiple models or agents can work together on tasks such as coding, planning, and debugging while sharing context and state. The platform introduces a coordination layer that manages task decomposition, assignment, and progress tracking through transparent systems like boards and structured communication. It emphasizes context-awareness, allowing agents to maintain continuity across sessions and reduce repetitive input or misunderstandings. The architecture includes memory systems that store knowledge from previous interactions, enabling continuous improvement and smarter decision-making over time. marcus-ai is designed to integrate with different AI models, offering flexibility in choosing the underlying intelligence while keeping coordination centralized.
Features
- Multi-agent coordination for collaborative task execution
- Context-aware task assignment and workflow management
- Integration with multiple AI models and providers
- Persistent memory systems for continuous learning
- Board-based communication and transparent tracking
- Automated task decomposition and dependency handling