# Mem0.ai Memory Manager

> Use this tool when you need to manage and organize memories for AI assistants, solving problems of information retention and recall across conversations. It provides a cognitive-inspired architecture with short-term and long-term memory operations, and supports custom categories, filtering, and knowledge graph relationships. Ideal for use cases requiring persistent memory, such as tracking personal preferences, project knowledge, or research workflows, with inputs including custom instructions and outputs including organized memory structures.

Canonical page: https://skillsregistry.net/skills/ryaker-mem0-general  
JSON: https://api.skillsregistry.net/v1/skills/ryaker-mem0-general

## Description

The Mem0 MCP Server provides AI assistants with access to Mem0.ai's memory management system through a cognitive-inspired architecture that organizes memories into different types based on their nature, persistence, and purpose. Built with Python using the AsyncMemoryClient from mem0ai, it implements short-term memory operations (conversation, working, attention) and long-term memory types (episodic, semantic, procedural) with support for selective memory filtering, custom categories, and knowledge graph relationships. The server includes advanced features like memory feedback mechanisms and custom instructions, making it ideal for users who need persistent, structured memory across conversations for personal preferences, project knowledge, or research workflows.

## Trust

- **Trust score (0–1):** 0.49
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/ryaker-mem0-general)
- **Repository:** <https://github.com/ryaker/mcp-mem0-general>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ryaker-mem0-general"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ryaker-mem0-general` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ryaker-mem0-general/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt