# FEGIS (Schema-Driven Memory)

> Use this tool when you need to enhance the cognitive abilities of LLMs with structured persistent memory, enabling them to maintain context and build knowledge bases across conversations. FEGIS solves problems in applications requiring efficient information storage and retrieval, such as chatbots and knowledge management systems. It takes in predefined archetypes and outputs structured cognitive artifacts, making it ideal for use cases that demand organized and meaningful connections between related ideas.

Canonical page: https://skillsregistry.net/skills/fegis-schema-driven-memory  
JSON: https://api.skillsregistry.net/v1/skills/fegis-schema-driven-memory

## Description

FEGIS is a schema-driven memory engine that gives LLMs cognitive tools and structured persistent memory. Developed by Perry Golden, it uses Qdrant vector database with FastEmbed for efficient storage and retrieval of information based on predefined archetypes. The system allows models to create, store, and search through structured cognitive artifacts like thoughts, reflections, and decisions using a facet-based organization system. This implementation enables LLMs to maintain context across conversations, build knowledge bases with qualitative dimensions, and create meaningful connections between related ideas - making it particularly valuable for applications requiring persistent memory and structured thinking.

## Trust

- **Trust score (0–1):** 0.62
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/fegis-schema-driven-memory)
- **Repository:** <https://github.com/p-funk/fegis>

## 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": "fegis-schema-driven-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/fegis-schema-driven-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/fegis-schema-driven-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