Add multimodal lifelong memory to MCP and Python agents with SimpleMem git:20260710.2925510B

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---
title: "Add multimodal lifelong memory to MCP and Python agents with SimpleMem"
description: "Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations."
verification: "security_reviewed"
source: "https://github.com/aiming-lab/SimpleMem"
author: "Aiming Lab"
publisher_type: "organization"
category:
  - "Integrations & Connectors"
framework:
  - "MCP"
tool_ecosystem:
  github_repo: "aiming-lab/SimpleMem"
  github_stars: 3522
---

# Add multimodal lifelong memory to MCP and Python agents with SimpleMem

Use SimpleMem to store, compress, index, and retrieve text or multimodal memories for agents through MCP or Python integrations.

## Prerequisites

SimpleMem Python package or MCP server, MCP-capable client or Python agent runtime, source memory data, and configured model/embedding dependencies for the selected backend

## Installation

Choose whichever fits your setup:

1. Copy this skill folder into your local skills directory.
2. Clone the repo and symlink or copy the skill into your agent workspace.
3. Add the repo as a git submodule if you manage shared skills centrally.
4. Install it through your internal provisioning or packaging workflow.
5. Download the folder directly from GitHub and place it in your skills collection.

Install command or upstream instructions:

```
Install simplemem from PyPI for Python workflows or configure the documented SimpleMem MCP server for text-memory workflows, add scoped dialogue or multimodal memories, finalize or index them, then query relevant memory before agent responses.
```

## Documentation

- https://github.com/aiming-lab/SimpleMem

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/add-multimodal-lifelong-memory-to-mcp-and-python-agents-with-simplemem/)