"Loom" matching MCP servers:

Provides persistent identity and memory for AI agents across MCP-compatible harnesses, enabling agents to retain their name, values, and episodic memories between sessions regardless of the client or model.

Routes multiple ChatGPT chats to allowlisted Git workspaces through a single MCP endpoint, and manages persistent Pi and Codex agent pools for coordinated, checkpointed work.

An intelligent middleware that determines when human intervention is necessary in AI agent operations using a sequential scoring system that evaluates multiple dimensions of a request.

Enables context-loop navigation in agents by exposing goto and loop_start_marker tools that delegate to a host over a Unix socket, allowing bookmarking and jumping between context iterations.

Enables autonomous agents and multi-agent frameworks to route actions through deterministic human approval gates before execution. Enforces risk tiers, reversible checkpoints, and token approval bounds so that risky or costly operations can be reviewed, bounded, and rolled back without LLM round-trips.

Provides coding agents with 63+ bounded, verifiable work loops, enabling goal-driven loop selection and executable protocol generation via MCP tools.

An MCP server that turns any LLM into an escape room player.

An MCP server that automates the full software development lifecycle through an AI-driven TDD state machine. It handles everything from task decomposition and test-driven development to integration testing and automated pull request creation.

Enables AI assistants like Claude to interact with humans through intuitive GUI dialogs, supporting text input, choices, confirmations, and information displays.

The industry-standard persistent memory and state manager for long-running agentic workflows.

MartinLoop is the independent governance and financial control layer for AI coding agents. Set hard spend limits, prevent runaway loops, enforce verification, trigger rollback on failure, and generate auditable receipts showing what changed, what it cost, and whether the work passed.

Enables AI agents to orchestrate tasks as a DAG with automated validation loops, executing validation commands, tracking statuses, and allowing iterative code fixes until tasks pass.

Enables LLMs to process arrays item-by-item or in batches with a specific task, storing and retrieving results with optional summarization after completion.

Transforms complex Pega Platform interactions into intuitive, conversational experiences by exposing Pega DX APIs through the standardized Model Context Protocol, enabling AI applications to interact with Pega through natural language.

A command-line tool that enables the use of MCP with the Room protocol, allowing agents to create and interact in peer-to-peer virtual rooms for goal-oriented collaboration.

A referee for self-improving AI agent loops that mines past sessions, improves workflows, and never stops until told.

A goal-agnostic parallel orchestration framework that enables sophisticated multi-agent coordination for tasks like code generation, UI development, and research through specification-driven architecture. It utilizes wave-based generation and intelligent context management to execute complex, iterative agentic loops.

A referee for self-improving AI agent loops that mines past sessions for working workflows, improves them, and never stops until you stop it.

MCP server that inspects older repos, identifies stale guidance, and installs a managed foundation for better Copilot work including instructions, objectives, specialist agents, and insights.

A referee for self-improving AI agent loops that mines past sessions for effective workflows, improves them, and requires measured proof before declaring anything better or done, never stopping until the user stops it.