# LibSQL Memory

> Use this tool when you need to optimize data search and storage with high-performance vector search capabilities and persistent memory solutions. It solves problems related to slow query performance and inefficient data retrieval, ideal for use cases requiring fast and reliable data access. The tool accepts SQL queries as input and returns optimized search results, making it suitable for applications with large datasets and high traffic.

Canonical page: https://skillsregistry.net/skills/mcp-memory-libsql  
JSON: https://api.skillsregistry.net/v1/skills/mcp-memory-libsql

## Description

High-performance vector search and persistent memory using libSQL.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-04-21

## Source

- **Source listing:** [Smithery](https://smithery.ai/server/mcp-memory-libsql)
- **Repository:** <https://github.com/spences10/mcp-memory-libsql#readme>

## 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": "mcp-memory-libsql"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/mcp-memory-libsql` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/mcp-memory-libsql/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