Enables AI assistants to orchestrate multi-step workflows by converting natural language into executable plans, coordinating tools, agents, and services via the Model Context Protocol.
A Model Context Protocol implementation that enables LLMs to execute complex, multi-step workflows combining tool usage with cognitive reasoning, providing structured, reusable paths through tasks with advanced control flow.
Provides structured workflows (phases, gates, coordination) for AI agents, enabling complex task execution with quality enforcement and multi-agent coordination via Model Context Protocol.
Enables AI models to seamlessly integrate with external tools and data sources via the Model Context Protocol, extending functionality without modifying the core model.
A declarative framework that manages LLM resources, prompts, and tools through YAML configuration files. It enables LLMs to interact with external data, CLI commands, and Python functions using the Model Context Protocol.