Agentic Loop Memory Server ♾️

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):

npx skills add meharajM/agent-loop-mcp@agentic-loop -g -y

Preview the skill before activation:

gh skill preview meharajM/agent-loop-mcp agentic-loop

2. Configure the MCP Server

Add the following to your `mcp_config.json`:

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

Related MCP server: hmem

🌟 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