# Engram

> Use this tool when you need to enhance AI agents with persistent memory for natural language recall and diverse perspectives. Engram solves problems of knowledge retention and recall, enabling agents to learn and adapt over time. It accepts inputs via MCP and outputs organized memories, ideal for use cases requiring contextual understanding and informed decision-making.

Canonical page: https://skillsregistry.net/skills/gsn2dd-engram  
JSON: https://api.skillsregistry.net/v1/skills/gsn2dd-engram

## Description

Persistent, self-organizing memory for AI agents via MCP, enabling natural language recall and fan-out perspectives.

## Trust

- **Trust score (0–1):** 0.19
- **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/lpucbuwm14)
- **Repository:** <https://github.com/gsn2dd/engram>

## 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": "gsn2dd-engram"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gsn2dd-engram` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gsn2dd-engram/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