Stateful Multi-Agent Workflow

Overview

This agent framework solves the critical problem of context loss in multi-step AI workflows. Instead of requiring manual copy-pasting of outputs between specialized agents, this system utilizes a Memory Context Provider (MCP) to store and automatically recall project state.

It is designed for complex, long-running projects—like developing an MVP over several weeks—where maintaining continuity across multiple agent interactions is paramount.

Capabilities

• Persistent State Management: Stores deliverables and context from previous agent steps in a central memory server.
• Automated Context Handoffs: Agents automatically recall necessary prior information (e.g., sprint plans, research findings) when activated, eliminating manual data transfer.
• Workflow Orchestration: Coordinates specialized agents (like UX Researchers, Architects, and Developers) through a defined project lifecycle.
• State Rollback: Supports the ability to 'rollback' or recall previous states for iterative refinement and debugging.

Example Use Cases

• Minimum Viable Product (MVP) Development: Running an end-to-end simulation of building software, where each agent builds upon the last's output without losing track of requirements.
• Large Research Projects: Coordinating multiple domain experts to build a comprehensive report over several sessions, ensuring all prior findings are available for synthesis.
• Complex System Design: Iteratively designing complex systems (like APIs or databases) where initial constraints must be remembered by later design phases.