Enables AI agents to write and execute Python code in an isolated sandbox that can orchestrate multiple MCP tool calls, reducing context window bloat and improving efficiency for complex workflows.
Executes Python code in isolated rootless containers while proxying MCP server tools, reducing context overhead by 95%+ and enabling complex multi-tool workflows through sandboxed code execution.
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.
Converts JSON data and system prompts to and from TOON (Token-Oriented Object Notation) format, reducing token usage by 30-60% when interacting with LLMs while preserving data structure.
A state-based agent orchestration system that allows transitions between different states (IDLE, PLANNING, RESEARCHING, EXECUTING, REVIEWING, ERROR) while maintaining conversation context and providing state-specific prompts.
A lightweight framework for building and orchestrating AI agents through the Model Context Protocol, enabling users to create scalable multi-agent systems using only configuration files.
A production-grade multi-agent workflow orchestrator built on the Model Context Protocol, featuring planner/executor/critic agents, durable run state, and replayable traces.
A service orchestration layer for the Model Context Protocol. Manages multiple MCP services through a single unified interface — spawn, supervise, and route tool calls across all your services.
An MCP orchestration layer that aggregates multiple MCP servers while exposing only 8 meta-tools, dramatically reducing context window usage, and provides SLOP scripting, event monitoring, and tool customization.
A lightweight Claude MCP gateway that dynamically loads tools only when needed, cutting MCP token clutter by up to 95% and keeping your context lean, fast, and focused.