EMMS - Enhanced Memory Management System MCP Server

@supermaxlol

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

supermaxlol

提交者

Shehzad Ahmed

关于 EMMS - Enhanced Memory Management System MCP Server

Cognitive memory system for AI agents with 129 MCP tools. Persistent hierarchical memory, emotional recall, knowledge graphs, spreading activation, hybrid retrieval (BM25+RRF), metacognition, goal tracking, spaced repetition, dream consolidation, and more.

#memory #knowledge-graph #ai-agents#cognitive-architecture#mcp #retrieval#emotions#metacognition

EMMS - Enhanced Memory Management System MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "emms": {
      "command": "uvx",
      "args": [
        "emms-mcp"
      ]
    }
  }
}

EMMS - Enhanced Memory Management System MCP Server 工具

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点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is EMMS - Enhanced Memory Management System?

EMMS is a cognitive memory system for AI agents, providing 129 MCP tools for storage, retrieval, knowledge graphs, reflection, and metacognition. It is available under the MIT License and can be installed via pip, npx, or uvx.

How to use EMMS - Enhanced Memory Management System?

Install the package using pip install emms-mcp, npx -y emms-mcp, or uvx emms-mcp. Configure it in an MCP client by adding a server entry with command uvx and args ["emms-mcp"]. The server exposes 129 tools across categories like Storage, Retrieval, Knowledge Graph, Reflection, Emotions, Goals, Metacognition, Memory Management, Identity, Prediction, and Multi-Agent.

Key features of EMMS - Enhanced Memory Management System

Provides 129 MCP tools for cognitive memory tasks
Supports hybrid and associative retrieval methods
Includes knowledge graph building and analysis tools
Offers reflection and dream-like synthesis capabilities
Tracks emotional states and mood trends
Enables multi-agent memory merging and migration

Use cases of EMMS - Enhanced Memory Management System

AI agents maintaining long-term memory across sessions
Building and querying knowledge graphs from stored information
Performing metacognitive analysis and bias detection
Coordinating shared memory across multiple AI agents
Enabling adaptive retrieval based on context and affect

常见问题

What platforms or runtimes does EMMS require?

It requires a Python environment (via pip) or Node.js (via npx), and can also be run via uvx. It must be used with an MCP-compatible client.

What types of memory operations does it support?

It supports storing, retrieving, decaying, deduplicating, and reconsolidating memories, along with advanced retrieval like hybrid, adaptive, and affective retrieval.

Can EMMS be used with multiple AI agents?

Yes, it includes multi-agent tools such as `merge_from`, `list_namespaces`, and `migrate_namespace` for sharing or moving memory between agents.

Does it support emotional modeling?

Yes, it provides tools for managing current emotion, regulating emotions, tracking mood trends, and viewing the emotional landscape.

Is there any built-in reflection or self-improvement?

Yes, the Reflection category includes `reflect`, `dream`, `synthesize_wisdom`, and `abstract_principles` tools, along with metacognitive reports and consciousness metrics.