# Engram

> Use this tool when you need to persist AI agent knowledge across sessions without relying on cloud services. Engram solves the problem of ephemeral agent memory, allowing for seamless recall of previously learned information. It accepts input from AI agents, stores the data locally, and outputs recalled information as needed, making it ideal for applications requiring continuous learning and memory retention.

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

## Description

Engram is a local-first memory server that lets AI agents remember things across sessions with zero cloud dependencies.

## Trust

- **Trust score (0–1):** 0.79
- **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/x8l9jg4roy)
- **Repository:** <https://github.com/HBarefoot/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": "hbarefoot-engram"
    }
  }
}
```

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