# engramia

> Use this tool when you need to manage and govern memory operations for production AI agents, solving problems related to data recall, consensus, and compliance with regulations like GDPR. It provides features like eval-weighted recall, multi-evaluator consensus, and audit logging, accepting input from multiple evaluators and producing governed data outputs. Ideal for use cases requiring secure, multi-tenant, and compliant AI memory management.

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

## Description

The memory operations platform for production AI agents — eval-weighted recall, multi-evaluator consensus, GDPR Art. 17/20 governance, multi-tenant RBAC, and audit logging built in.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/xhx5dw14w4)
- **Repository:** <https://github.com/engramia/engramia>

## 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": "engramia-engramia"
    }
  }
}
```

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