Agent Loop Mcp

io.github.meharajMPublished by GitHub account meharajMv1.0.3Updated Oct 24, 2026

Agentic memory manager. Install Skill: npx skills add meharajM/agent-loop-mcp

VerifiedSTDIODesktop onlyKnowledge & Memory AI & ML

Server details

Identifier: io.github.meharajM/agent-loop-mcp
Transport: STDIO
Runtime: Desktop only
Trust: Verified
Published: Sep 16, 2026

Security note

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Overview

AI-generated overview

A local MCP server that gives an assistant persistent memory and state management for long-running agentic workflows.

What you can do: Provides state management for long-running agentic workflows, described as an active state manager rather than passive memory. It monitors word counts to trigger compaction cycles and enforces a self-healing strategy on failures to keep agents from looping. The project is a two-part ecosystem: this MCP server plus a separate agent skill for orchestration.
When to use it: Worth considering when running long agentic sessions, especially with smaller models that have limited context windows and need persistence across steps. The README states both the orchestration skill and the MCP server should be installed for the best experience.
Requirements: Runs locally over stdio, started with npx from the npm package @mhrj/mcp-agent-loop, so Node.js and npx are needed. The README also instructs installing a companion agent skill via npx skills add. No authentication, environment variables, or headers are declared.

Before you install

The setup instructions run npx commands that fetch and execute third-party packages and install a skill into the agent host, so review the package and skill before running them. The server stores workflow state locally; no credentials or payments are declared.

Installation

In SourceWeft

Open Agent Loop Mcp in the dashboard and add it to a workspace.
Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

Agentic Loop Memory Server ♾️

[Agent Skills] [skills.sh]

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

Enable any AI model—especially smaller ones with limited context windows—to function with the persistence of high-end models. This project works as a two-part ecosystem: an MCP Server for state management and an Agent Skill for orchestration.

🛠 Complete Setup (Required)

For the best experience, you must install both the orchestration skill and the MCP server.

1. Install the Skill

Install the agentic-loop skill into your AI agent (Codex, Claude Code, Cursor, Gemini CLI, GitHub Copilot, and other Agent Skills hosts):

Preview the skill before activation:

2. Configure the MCP Server

Add the following to your `mcp_config.json`:

json

{  "mcpServers": {    "agent-loop": {      "command": "npx",      "args": ["-y", "@mhrj/mcp-agent-loop"]    }  }}

{  "mcpServers": {    "agent-loop": {      "command": "npx",      "args": ["-y", "@mhrj/mcp-agent-loop"]    }  }}

🌟 Why this approach is unique

Unlike passive memory tools, this is an Active State Manager. It monitors word counts to trigger compaction cycles and enforces a "Self-Healing Strategy" on every failure, preventing AI agents from getting stuck in mindless loops.

📂 Project Structure

src/: TypeScript source for the MCP server.
skills/agentic-loop/SKILL.md: The instruction manual for the AI.
build/: JavaScript artifacts.

📄 License

ISC

Source: README.md at commit e13a399

Tools

SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

v1.0.3LatestSep 16, 2026

Score

40% of the applicable checks met

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Reached in the last 30 days25 pts
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Auth declared10 pts
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Tools listed15 pts
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DescriptionRequired15 pts
Logo10 pts
Documentation15 pts
License10 pts
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