Control Systems
Top open-source Control Systems AI systems, agents, and MCP tools.
Discover and install verified Control Systems AI systems, autonomous agent skills, and Model Context Protocol servers on AIStoreHub. Reusable, security-scanned, and ready for 1-click execution in Cursor, Claude Code, and terminal workflows.
Available AI Systems & Skills (24)
Open in Advanced FilterA lightweight desktop IDE for editing and running MATLAB .m scripts and Simulink .slx models, with visual editing, plots, parameter sweeps, and AI assistance.
Official Android agent skill library and autonomous device control primitives for interacting with mobile OS, notifications, and application intent systems.
An open-source platform for orchestrating AI agents — secure, simple, and built for everyone. Multi-agent, real-time task board, web control panel, skills system, browser automation, multi-provider, scheduler, and more. One command to start. Everything from the browser. No coding required.
A decision brain for agentic systems: perceive context, compare options, and control execution.
Build agentic systems. Run them with confidence. Orchestrate agents, automate business processes, inspect every execution, and keep humans in control. Deploy Heym on your own infrastructure.
⛓️RuleGo is a lightweight, high-performance, embedded, next-generation component orchestration rule engine framework for Go.
Spring Boot Starter: Auto-convert existing REST APIs (@RestController) to MCP Server with zero/low-code. Expose controllers as MCP Tools instantly.
Provider-neutral control plane for durable-state agent swarms: subprocess workers, leases, artifacts, memory, and deterministic stitching.
Open-source mission control for coding agents. Run cross-vendor workers in isolated worktrees, audit every submission, and revert an entire mission.
[Community Supported] Perforce P4 MCP Server is a Model Context Protocol (MCP) server that integrates with the Perforce P4 version control system.
The MATLAB Agentic Toolkit brings proven MATLAB capabilities to AI agents, making engineering and scientific workflows agent-ready.
Node.js/TypeScript MCP server for Atlassian Bitbucket. Enables AI systems (LLMs) to interact with workspaces, repositories, and pull requests via tools (list, get, comment, search). Connects AI directly to version control workflows through the standard MCP interface.
A host-side, scriptable command-line terminal for Segger-RTT; Also the AI-ready command line for embedded validation.
Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control
Autonomous AI-agent orchestration engine for job discovery with decision traces, tool adapters, and production-grade run control.
Run a 28.9M-parameter TinyLM on ESP32-S3 with an RP2040 OLED display node for fully local embedded AI inference.
Claude AI skill for cinematic Higgsfield AI prompts — 32 sub-skills covering Seedance 2.5 (omni-reference, video edit + extend) and 2.0, the Hell Grind feature-film pipeline, an acting system, Cinema Studio 2.5/3.0/3.5, MCSLA, Soul ID consistency, Kling 3.0 Motion Control, the DISCIPLINE framework, and 18 templates.
Turn Android Termux into an autonomous AI agent and security orchestrator. Powered by a local ReAct framework with 50 system tools and MCP support, a background task │ scheduler, on-device ADB controls, and a unified Web UI & Telegram Bot.
The Systems Thinker's Deterministic Claude Code Control Plane
👑 The all-in-one macOS menu bar companion: customizable controls, system monitor, AI Agent, Shortcuts, widgets, remote control, and more.
Synapse is a local-first personal memory operating system designed for AI agents. It provides durable, typed, and highly reliable long-term memory using only local SQLite — giving agents the ability to remember, update, and reason over facts across sessions with full user control.
Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption
AINL helps turn AI from "a smart conversation" into "a structured worker." It is designed for teams building AI workflows that need multiple steps, state and memory, tool use, repeatable execution, validation and control, and lower dependence on long prompt loops. AINL is a compact, graph-canonical, AI-native programming system for (READ: README)
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