# Knowledge Graph Memory

> Use this tool when you need to build and maintain long-term memory for AI assistants, storing and querying information in a graph structure to enable context retention and relationship modeling between concepts. It solves problems in applications requiring persistent knowledge bases, such as accumulating and reasoning over information across conversations. The Knowledge Graph Memory server takes in AI capabilities and structured knowledge as inputs, and outputs optimized querying and retrieval of stored information.

Canonical page: https://skillsregistry.net/skills/estav-knowledge-graph-memory  
JSON: https://api.skillsregistry.net/v1/skills/estav-knowledge-graph-memory

## Description

This Memory MCP Server, developed by estav, provides a knowledge graph management system for AI assistants using the Model Context Protocol. It offers tools for storing, retrieving, and querying information in a graph structure, enabling assistants to build and maintain long-term memory. The server uses SQLite for persistent storage and implements optimized batch operations for efficiency. By connecting AI capabilities with structured knowledge representation, this implementation allows assistants to accumulate and reason over information across conversations. It is particularly useful for applications requiring context retention, relationship modeling between concepts, or any scenario where an AI system needs to build and leverage a persistent knowledge base.

## Trust

- **Trust score (0–1):** 0.64
- **Verification tier:** scanned
- **Last scanned:** 2026-09-02

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-09-02

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/estav-knowledge-graph-memory)
- **Repository:** <https://github.com/evangstav/python-memory-mcp-server>

## 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": "estav-knowledge-graph-memory"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/estav-knowledge-graph-memory` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/estav-knowledge-graph-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