A lightweight Model Context Protocol (MCP) orchestrator designed for efficiency at scale. It features TOON compression (reducing token usage by 30-90%) and Lazy Loading, making it the ideal solution for complex, multi-tool agentic workflows.
Facilitates enhanced interaction with large language models (LLMs) by providing intelligent context management, tool integration, and multi-provider AI model coordination for efficient AI-driven workflows.
Enables efficient AI workflow orchestration by chaining multi-step LLM operations while keeping intermediate results out of the context window, reducing token usage by 90%+ and supporting multiple AI providers.
A production-packaged Model Context Protocol server for coding agents that routes large file, git, web, database, and other tasks through token-budgeted tools and workflows.
Provides context-aware skill selection for AI agents, reducing token usage by 85-98% and improving accuracy through semantic retrieval, session memory, and feedback learning.
Unified context intelligence layer for AI agents, enabling orchestration of memory, reasoning, and self-healing indexes with cognition primitives and churn-aware retrieval routing.
