# memfabric

> memfabric — mrboor-memfabric. Use this tool when you need to efficiently manage and retrieve agent memory without relying on embeddings or vector databases. Memfabric solves problems related to scalable and organized memory storage, enabling seamless information retrieval and updating. It accepts git-compatible inputs and provides organized memory outputs, ideal for use cases requiring dynamic and self-organizing memory management.

Canonical page: https://skillsregistry.net/skills/mrboor-memfabric  
JSON: https://api.skillsregistry.net/v1/skills/mrboor-memfabric

## Description

Self-organizing agent memory. No embeddings, no vector DB.

## Trust

- **Trust score (0–1):** 0.99
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/MrBoor/memfabric)

## 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": "mrboor-memfabric"
    }
  }
}
```

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

---
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