# Organism Memory MCP Server

> Use this tool when you need to retain and retrieve knowledge across multiple sessions for AI agents, solving problems of data loss and inconsistency. It provides a self-contained, four-tier memory system, accepting input data from agents and outputting relevant information through a searchable interface. Ideal for applications requiring persistent memory without relying on external APIs.

Canonical page: https://skillsregistry.net/skills/sunnsten-organism-memory  
JSON: https://api.skillsregistry.net/v1/skills/sunnsten-organism-memory

## Description

Provides persistent, searchable memory for AI agents across sessions using a four-tier memory system, without sending data to external APIs.

## Trust

- **Trust score (0–1):** 0.55
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/a7jujumsf0)
- **Repository:** <https://github.com/sunnsten/organism-memory>

## 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": "sunnsten-organism-memory"
    }
  }
}
```

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