# engram-memory

> engram-memory — engrammemory-engram-memory. Use this tool when you need to efficiently store and retrieve information without relying on large language models or excessive token usage. It solves problems related to memory-intensive tasks, such as data storage and recall, by providing a high-scoring and cost-effective alternative. With input capabilities via git, it outputs optimized memory storage and retrieval, making it ideal for applications where token efficiency is crucial.

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

## Description

The highest-scoring AI memory system ever benchmarked that isn't reliant on LLM reranking. And it's free & burns less tokens.

## Trust

- **Trust score (0–1):** 0.16
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-25

## Source

- **Source listing:** [GitHub](https://github.com/EngramMemory/engram-memory)

## 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": "engrammemory-engram-memory"
    }
  }
}
```

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