Skills Plugins MCP Prompt Model 导航 博客 资讯 我的中心
会话与消息 #agent#agentic-ai#ai-agents#chatgpt#claude-code

memmy-agent

🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you. Now supports Claude Code, Codex, OpenClaw and Hermes Agent etc.

MemTensor @MemTensor ⬇ 1 ★ 1,900 TypeScript

安装

dsh plugin add github:MemTensor/memmy-agent
下载安装清单

需要可复现安装时,可在仓库后追加 #commit 固定提交。

🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memory and persistent context — all AI remember the same you. Now supports Claude Code, Codex, OpenClaw and Hermes Agent etc.

该插件未提供要点说明,请参考仓库 README。

agentagentic-aiai-agentschatgptclaude-code
  1. 安装并启动 DeepSeek Harness:npx @deepseek-ai/dsh web
  2. 在终端执行上面的安装命令(CLI 会解析插件并核验来源)
  3. 用 dsh plugins list 确认已安装,必要时重启 Harness 生效

插件以当前 dsh 进程的权限运行,安装时可能执行代码。请先通读仓库源码与许可证,确认无破坏性命令与越权访问;本站只做索引,不对第三方插件安全性作担保。

代码仓库github.com/MemTensor/memmy-agent
许可证MIT
主要语言TypeScript
下载量1
GitHub 星标1,900
最近推送2026-09-17
收录日期2026-09-19
分类会话与消息

事实信息来自公开插件目录快照(2026-10-01),介绍文案由本站再加工。

以下为插件仓库 README 全文(原始内容,由公开目录抓取整理)。

[图片: Memmy Logo]

    [图片: Docs]

    [图片: applenews]

    [图片: Discord]

    [图片: X]

    [图片: Memmy Agent - Let every AI remember the same you. | Product Hunt]

Continue the same work across DeepSeek Harness, Claude Code, Codex, and etc.

Overview · Quick Start · Technical Overview · Roadmap · Acknowledgements · Contributors

English • 简体中文

What Is Memmy?

  [图片: Remember: Memmy remembers what you said and turns your local AI collaboration history into structured memory]

  [图片: Relay: switch tools without losing context—Memmy carries your project background, preferences, and progress forward]

  [图片: Act: Memmy is also an Agent that can organize information, combine approaches, and continue unfinished tasks]

Cross-Agent Task Continuity

Most of the Agents You're Using Can Connect to Memmy

DeepSeek Harness, OpenClaw, Hermes, Claude Code, Codex, Cursor, WorkBuddy, OpenCode, Pi...they all work!

[图片: cross-agent-en.png]

Data Security

[图片: Memmy data security]

How to Use Memmy

For complete installation and configuration instructions, see the Getting Started guide.

1. Desktop App (Recommended)

  [图片: First scan]

  [图片: First Meeting Report]

Download Memmy from the official website or GitHub Releases.

[!TIP]
Sign up for Memmy to receive free tokens and try the complete Memory + Agent Runtime.

Trial credits:

Registration grants Agent task trial tokens; the current balance and usage are shown in the app.

When the trial credits run out, switch to BYOK mode and use your own model API.

2. Use the memmy CLI / TUI

[图片: Memmy TUI]

On Linux x64 or arm64 with Node.js 22 or newer and an available systemd user session:

curl -fsSL https://raw.githubusercontent.com/MemTensor/memmy-agent/main/scripts/install.sh | bash
memmy

The installer enables the local Memory Service immediately as memmy-memory.service. The first bare memmy invocation opens the model setup wizard when needed, then enables memmy-gateway.service, waits for it to become ready, and enters the TUI. Both are systemd --user services bound to localhost and remain available after the TUI or terminal exits. They start again on later logins; the installer does not enable linger. Only the installer launcher activates this service management, so source-built Linux CLIs keep their existing behavior.

Before starting or reconnecting to the Gateway, memmy refreshes a private ~/.memmy/systemd/gateway.env file (mode 0600) with configuration-referenced environment variables, common Provider credentials, and the terminal PATH. If those values change, the next bare memmy invocation restarts the user service with the new environment.

systemctl --user status memmy-memory.service
systemctl --user status memmy-gateway.service

The installer initializes Memory without changing Codex, Claude Code, Cursor, or other agents. Run memmy-memory init (all detected agents) or memmy-memory init --agent <agent> when you explicitly want to install the Memory Skill and the supported Hook/plugin for an agent.

memmy onboard                              # Configure models, providers, gateway, memory, and tools interactively
memmy onboard --defaults                   # Initialize ~/.memmy/config.yaml and the workspace with defaults
memmy status                               # Check the configuration, model, and provider
memmy agent --message "Introduce the current workspace"  # Run a single-turn task
memmy                                      # Enter the interactive TUI
memmy serve                                # Start the OpenAI-compatible API (:18990)

The minimal BYOK configuration is located at ~/.memmy/config.yaml:

agents:
  defaults:
    model: openai/gpt-4.1
    provider: openai
    timezone: "+08:00"
providers:
  openai:
    apiKey: ${OPENAI_API_KEY}

3. Use the memmy-memory CLI

Use it to access the local memory service from agents, scripts, and debugging workflows:

memmy-memory init
memmy-memory health
memmy-memory search "memory policies in this project"
memmy-memory add "a piece of knowledge worth saving"
memmy-memory get <id>

It connects to http://127.0.0.1:18960 by default. Use --url, --token, --config, --source, and --user-id to specify the service and namespace.

4. Start from the Source Code

git clone https://github.com/MemTensor/memmy-agent.git
cd memmy-agent
cp .env.example .env
npm install
npm run build
bash scripts/dev-start.sh

The script installs dependencies, builds the services, and starts the development environment. Node.js >=22 and npm are required; use Git Bash on Windows.

How Is Memmy Built?

For details about the architecture, memory service, and integration methods, see the Memmy documentation.

  [图片: Memmy system architecture: multiple Agents and entry points share the local Memory and Agent Runtime]

Roadmap

Memmy is building personal memory infrastructure, and its scope goes beyond coding Agents:

More memory sources — expanding from AI conversations to browser activity, local documents, and eventually more devices and hardware.

Team collaboration — planned Agent-to-Agent collaboration, letting team members' AI assistants share knowledge under privacy protection.

Acknowledgements

Memmy stands on the shoulders of a group of excellent open-source projects, and we are deeply grateful.

OpenClaw — a pioneer of open-source personal AI assistants; its exploration of multi-platform messaging channels directly inspired Memmy's channel connection design.

hermes-agent — the self-evolving Agent built by Nous Research; its practice in persistent memory and skill self-learning showed us that an Agent can "understand you better the more you use it".

nanobot — grown from a minimal prototype into a fully featured open-source Agent platform; its engineering practice around the Agent loop and MCP integration provided important references for Memmy's core design.

The point of open source is to let good ideas flow, and we hope Memmy becomes part of that river.

Contributors

Thanks to every contributor who makes Memmy better ❤️

数据来源:公开的 DeepSeek Harness 插件目录与各插件 GitHub 仓库。本站为独立第三方目录,与 DeepSeek、幻方(High-Flyer)及插件作者均无隶属或背书关系。

每日精选 Skill 推荐,免费送到你邮箱

输入邮箱,每天接收一个精选 AI Agent 技能推荐。完全免费,持续更新。

提交后我们会发送一封确认邮件,点击邮件里的链接才会开始收信。

完全免费,取消任意时间。我们不会发送垃圾邮件。