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The SimpleMem MCP server was last committed 3mo ago and has 18 contributors. Issue health: 78% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

SimpleMem MCP Server

Name: aiming-lab/SimpleMem
Rating: 3.5 (1 reviews)
Author: aiming-lab

aiming-lab/SimpleMem

Score: 63.3 Rank #5417 Search & RAG

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

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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 #5417 out of 24846 indexed tools.

Has 3,831 GitHub stars.

Has 18 contributors.

Ecosystem

Python MIT 19 pypi/wk

agentcompressionllmmcpmemoryragretrievalsemantic-searchsimplememknowledge-graphlifelong-memorymultimodalpythonaudiovideovision

Score Breakdown

Stars 15% 3,831

3,831 stars → well-known project

Freshness 25% 3mo ago

Last commit 3mo ago → stale

Issue Health 25% 78%

50/64 issues closed → decent issue management

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 403

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

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Freshness high impact

Last commit was 110 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

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Details

Stars: 3,831
Forks: 403
Open Issues: 14
Closed Issues: 50
Contributors: 18
Dependents: —
Language: Python
License: MIT
Last Commit: 3mo 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 →