# neo-mem

> neo-mem — ptaljaard-neo-mem. Use this tool when you need to enable AI agents to retain and recall complex knowledge graphs across sessions. Neo-mem solves the problem of ephemeral memory in AI systems, allowing for persistent and semantic recall of information. It takes in vector embeddings and graph-based data as input and outputs recalled knowledge graphs, ideal for use cases requiring long-term memory and contextual understanding.

Canonical page: https://skillsregistry.net/skills/ptaljaard-neo-mem  
JSON: https://api.skillsregistry.net/v1/skills/ptaljaard-neo-mem

## Description

Provides persistent, graph-based memory for AI agents using Neo4j and vector embeddings, enabling semantic recall across sessions via MCP.

## Trust

- **Trust score (0–1):** 0.54
- **Verification tier:** scanned
- **Last scanned:** 2026-09-03

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/pqs2mj0xr6)
- **Repository:** <https://github.com/PTaljaard/neo-mem>

## 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": "ptaljaard-neo-mem"
    }
  }
}
```

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