Agent Recall Trail vs Montycat MCP — Shared AI Memory

In-depth architectural comparison of the Agent Recall Trail and Montycat MCP — Shared AI Memory MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.

Which MCP Server Should You Choose?

Choose Agent Recall Trail when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Agent Recall Trail Details

M

Choose Montycat MCP — Shared AI Memory when:

• You need dedicated capabilities in the Knowledge & Memory domain.
• You prefer local stdio subprocess transport architecture.
• Your security boundary fits: No auth required (Free / Open Source).

Explore Montycat MCP — Shared AI Memory Details

Feature & Specification Comparison

Specification	Agent Recall Trail Knowledge & Memory	M Montycat MCP — Shared AI Memory Knowledge & Memory
Summary	Private Agent memory and human-reviewed shared knowledge.	One shared, persistent memory across AI agents and systems, with semantic recall and live updates.
Category & Scope	Knowledge & Memory	Knowledge & Memory

Tools & Capabilities Breakdown

Ready-to-Paste Client Configurations

Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).

{
  "mcpServers": {
    "agent-recall-trail": {
      "command": "npx",
      "args": [
        "-y",
        "agent-recall-trail"
      ]
    }
  }
}

{
  "mcpServers": {
    "montycat-mcp-shared-ai-memory": {
      "command": "uvx",
      "args": [
        "montycat-mcp"
      ]
    }
  }
}

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