A shared context and coordination layer for multiple AI agents over MCP, featuring semantic memory, dependency-aware task DAGs, auto-scheduling, role-based access, real-time push, and a live dashboard.
A multi-agent task management system for AI applications that enables users to create agents with roles and capabilities, delegate tasks with trust-based routing, coordinate file access to prevent conflicts, and monitor performance through a unified dashboard.
An MCP server that provides shared memory, kanban board, and agent registry for AI agents to collaborate as a team, with a live dashboard for human oversight.
A durable DAG-based task planner exposed as an MCP server that lets AI orchestrators break a goal into a dependency graph of tasks, execute them in parallel where possible, track state durably, and handle human-in-the-loop approval through 22 MCP tools.