# Engram

> Use this tool when you need to retain and recall information over time, enabling agents to learn from experiences and make informed decisions. Engram solves problems of knowledge loss and incomplete context, providing a persistent memory layer for agents to store and retrieve data. It accepts input data, stores it for later use, and outputs recalled information, making it ideal for applications requiring continuous learning and adaptation.

Canonical page: https://skillsregistry.net/skills/tstockham96-engram  
JSON: https://api.skillsregistry.net/v1/skills/tstockham96-engram

## Description

Perstistant Memory Layer for Agents

## Trust

- **Trust score (0–1):** 0.75
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/qr0fcyraw3)
- **Repository:** <https://github.com/tstockham96/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": "tstockham96-engram"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tstockham96-engram` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tstockham96-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