MCP server that enables AI agents to run a deterministic orchestration loop with decomposition, subagent execution, and review feedback across multiple LLM backends.
MCP server that enables AI assistants to run multi-step agent pipelines (e.g., Issue Analyst → Code Writer → Test Runner → PR Opener) from conversations, with support for Devin, shell, Python, and HTTP agents.
Multi-AI collaboration MCP server enabling message passing, code review workflows, shared todo lists, and agent management with authentication and role-based access.
A multi-agent orchestrator MCP server that enables LLM agents to collaborate on complex tasks by automating role assignment, inter-agent communication, and artifact integration. It provides tools for task decomposition, agent assignment, status tracking, code review, and result merging.
MCP server that enables agents to dynamically switch between multiple AI models (OpenAI, Anthropic, Google, etc.) with unified protocol-driven configuration and capability discovery.
This MCP server provides a local-first control plane for coding agents, enabling task routing, contract management, and shared learning across multiple AI vendors via markdown files.