# simplemem

> simplemem — nantes-simplemem. Use this tool when you need to efficiently manage and retrieve information across multiple sessions, enabling lifelong learning and improved performance for LLM agents. It solves problems of knowledge retention and recall, allowing agents to learn from past experiences and adapt to new information. With semantic compression and intent-aware retrieval, simplemem provides a robust interface for storing and retrieving memories, making it ideal for applications requiring continuous learning and improvement.

Canonical page: https://skillsregistry.net/skills/nantes-simplemem  
JSON: https://api.skillsregistry.net/v1/skills/nantes-simplemem

## Description

Efficient Lifelong Memory for LLM Agents - semantic compression, cross-session memory, and intent-aware retrieval.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/nantes-simplemem)

## 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": "nantes-simplemem"
    }
  }
}
```

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