Google PageRank for AI agents. 25,000+ tools indexed.

memory-bridge MCP Server

jiabaobei/memory-bridge

Score: 58.1 Rank #7122 MCP Server
[![AgentRank](https://agentrank-ai.com/api/v1/badge/jiabaobei--memory-bridge)](https://agentrank-ai.com/tool/jiabaobei--memory-bridge/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge)
Are you the maintainer of jiabaobei/memory-bridge? Claim this listing →

记忆桥 MemoryBridge — 跨设备、跨平台的 AI 共享记忆层 | Cross-device and cross-platform shared memory for AI (CDSMP implementation)

Add AgentRank to Claude Code Discover and compare tools like jiabaobei/memory-bridge — your AI finds the right one automatically
Get API Access →
claude mcp add agentrank -- npx -y agentrank-mcp-server

Overview

jiabaobei/memory-bridge is a Python tool licensed under MIT. 记忆桥 MemoryBridge — 跨设备、跨平台的 AI 共享记忆层 | Cross-device and cross-platform shared memory for AI (CDSMP implementation) Topics: agent, ai, cross-device, cross-platform, llm, mcp, memory, python, semantic-memory, cdsmp.

Ranked #7122 out of 24846 indexed tools.

Ecosystem

Python MIT 4 pypi/wk
agentaicross-devicecross-platformllmmcpmemorypythonsemantic-memorycdsmp

Score Breakdown

StarsFreshnessIssue HealthContributorsDependents
Stars 15% 318

318 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → moderate activity

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 3

3 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A
PyPI Downloads 13% 4/wk

4 weekly installs → early adoption

Forks 2
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 39 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 →

AgentRank score for jiabaobei/memory-bridge
[![AgentRank](https://agentrank-ai.com/api/v1/badge/jiabaobei--memory-bridge)](https://agentrank-ai.com/tool/jiabaobei--memory-bridge/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge)
<a href="https://agentrank-ai.com/tool/jiabaobei--memory-bridge/?utm_source=badge&utm_medium=readme&utm_campaign=agentrank_badge"><img src="https://agentrank-ai.com/api/v1/badge/jiabaobei--memory-bridge" alt="AgentRank"></a>

Embed Widget docs →

Embed a rich score widget on your site or blog.

<script src="https://agentrank-ai.com/embed.js" data-tool="jiabaobei/memory-bridge"></script>

Matched Queries

topic:mcp

From the README

# 记忆桥 MemoryBridge

> 🌉 **给 AI 一个跟着你走的记忆** —— 跨设备 × 跨平台的共享记忆层
>
> CDSMP(大模型跨设备语义记忆连续性架构)的官方工程实现。
> [English](README_EN.md) · [设计 RFC](docs/RFC-001-architecture.md) · [容器一致性 RFC](docs/RFC-002-container-consistency.md) · [路线图](docs/roadmap.md) · [移动端接入](docs/mobile.md) · [隐私威胁模型](docs/threat-model.md) · [版本历程](CHANGELOG.md)

## 这解决什么问题

早上通勤时你在手机上和 AI 讨论到一半的推理,回到办公室想在 PC 上继续——
今天的做法是:翻聊天记录、复制粘贴、重新解释一遍背景。

云全量同步很重也未必安全;RAG 是"到了新设备再被动检索";主流记忆系统
(Mem0、MemGPT/Letta 等)本质上是**单机的**。记忆桥的答案是三个差异化主张:

1. **跨设备连续性**:记忆跟着人走,而不是跟着 App 走。手机、PC、平板共享同一份语义记忆,通过增量差分同步。
2. **边缘预加载**:在你打开新设备之前,高热度的记忆已经被推送到位——切换即连续,而不是切换后等待检索。
3. **内容冻结原则**:记忆桥只提取语义关联、只调节结构参数,**永不改写你的原始记忆内容**。这正是论文所依据的 Faulty Memory 研究的结论:让 LLM 自动改写/摘要记忆,必然引入幻觉式失真。

同时,记忆桥是**跨平台**的:通过 MCP 协议,同一个记忆库可以被 Claude Code、Cursor、Cline 等任意 MCP 客户端共享使用(平台覆盖详情见下文矩阵)。

## 当前能力(v0.31)

| 能力 | 说明 | 状态 |
|---|---|---|
| **跨设备互通零配置**(密码写在项目文档里,加密形态) | 通道密钥由**源码种子确定性派生**(PBKDF2-HMAC-SHA256 200000 轮,纯标准库),各端各自算、逐字节相同;通道目录不再存明文口令。README 逐字公布算法/盐/轮数/自检密文,**任何 A
Read full README on GitHub →

Get the weekly AgentRank digest

Top movers, new tools, ecosystem insights — straight to your inbox.