Is the SimpleMem MCP server actively maintained?

The SimpleMem MCP server was last committed 2mo ago and has 18 contributors. Issue health: 85% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

SimpleMem MCP Server

Name: aiming-lab/SimpleMem
Rating: 3.6 (1 reviews)
Author: aiming-lab

aiming-lab/SimpleMem

Score: 65.6 Rank #4554 Search & RAG

Are you the maintainer of aiming-lab/SimpleMem? Claim this listing →

[ICML'26] SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal

Add AgentRank to Claude Code Discover and compare tools like aiming-lab/SimpleMem — your AI finds the right one automatically

Get API Access →

claude mcp add agentrank -- npx -y agentrank-mcp-server

Overview

aiming-lab/SimpleMem is a Python tool licensed under MIT. [ICML'26] SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal Topics: agent, compression, llm, mcp, memory, rag, retrieval, semantic-search, simplemem, knowledge-graph, lifelong-memory, multimodal, python, audio, video, vision.

Ranked #4554 out of 24840 indexed tools.

Has 3,802 GitHub stars.

Has 18 contributors.

Ecosystem

Python MIT 19 pypi/wk

agentcompressionllmmcpmemoryragretrievalsemantic-searchsimplememknowledge-graphlifelong-memorymultimodalpythonaudiovideovision

Score Breakdown

Stars 15% 3,802

3,802 stars → well-known project

Freshness 25% 2mo ago

Last commit 2mo ago → moderate activity

Issue Health 25% 85%

50/59 issues closed → responsive maintainer

Contributors 10% 18

18 contributors → active community

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads 13% 19/wk

19 weekly installs → early adoption

Forks 397

Description Good

License MIT

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Description low impact

Expand your description to 150+ characters for better discoverability

Freshness high impact

Last commit was 63 days ago — a recent commit would boost your freshness score

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

Badge all embed codes →

Markdown

[![AgentRank](https://agentrank-ai.com/api/badge/tool/aiming-lab--SimpleMem)](https://agentrank-ai.com/tool/aiming-lab--SimpleMem/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge)

HTML

<a href="https://agentrank-ai.com/tool/aiming-lab--SimpleMem/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge"><img src="https://agentrank-ai.com/api/badge/tool/aiming-lab--SimpleMem" alt="AgentRank"></a>

Embed Widget docs →

HTML

<script src="https://agentrank-ai.com/embed.js" data-tool="aiming-lab/SimpleMem"></script>

Details

Stars: 3,802
Forks: 397
Open Issues: 9
Closed Issues: 50
Contributors: 18
Dependents: —
Language: Python
License: MIT
Last Commit: 2mo ago

Matched Queries

topic:mcp

From the README

<div align="center">

<div align="center">

## Efficient Lifelong Memory for LLM Agents — Text & Multimodal

<small>Store, compress, and retrieve long-term memories with semantic lossless compression. Now with multimodal support for text, image, audio & video.</small>

</div>

<p><b>Works with any AI platform that supports MCP (text memory) or Python integration (full multimodal)</b></p>

<p align="center">
  <a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=SimpleMem">
    
  </a>
</p>

<p align="center">
🎁 <b><a href="https://www.atlascloud.ai/?utm_source=github&utm_medium=link&utm_campaign=SimpleMem">Atlas Cloud</a></b> is a full-modal, OpenAI-compatible AI inference platform — point SimpleMem's <code>OPENAI_BASE_URL</code> at it to power memory construction, retrieval, and judging with DeepSeek, Qwen, GLM, Kimi, MiniMax and more through a single API, no multi-vendor setup needed. Budget-friendly <a href="https://www.atlascloud.ai/console/coding-pla

Read full README on GitHub →