# SparkVibe MemoryMesh

> Use this tool when you need to store and retrieve AI memories locally without relying on cloud services. It solves the problem of persistent memory storage for AI agents, enabling cross-tool portability and local data management. The SparkVibe MemoryMesh takes in AI memory data as input and outputs stored memories, providing a lightweight and self-contained solution for AI memory management.

Canonical page: https://skillsregistry.net/skills/sparkvibe-memorymesh  
JSON: https://api.skillsregistry.net/v1/skills/sparkvibe-memorymesh

## Description

Provides persistent AI memory storage using SQLite with no external dependencies. Stores and retrieves memories locally with cross-tool portability between different AI clients. Designed as a lightweight, local-first alternative to cloud-based memory solutions.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/sparkvibe-memorymesh)

## 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": "sparkvibe-memorymesh"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/sparkvibe-memorymesh` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/sparkvibe-memorymesh/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