
Mindpm
io.github.umitkavala由 GitHub 账号 umitkavala 发布v3.2.0更新于 Oct 24, 2026
Project memory and a control plane for AI coding agents.
概览
本地 MCP 服务器,为 AI 编程代理提供基于 SQLite 的项目记忆,记录任务、决策、笔记与会话,并附带看板。
- 可以做什么
- 记录和查询任务、规格、验收标准、优先级与阻塞项
- 保存决策及其理由和被否决的方案,以及架构、缺陷、约定等笔记
- 开始和结束会话,让新对话接着上次的进度继续
- 把带规格的任务交给编程代理,用认领和租约避免并行代理冲突,并记录每次尝试
- 在内置看板上验收提交的工作,可选校验器会先重跑检查
- 适用场景
- 适合跨多次独立 AI 对话推进同一个项目、希望助手无需重复说明就能接上上下文的场景,也适合多个编程代理共用一份任务清单并需要协调时。
- 运行要求
- 以 stdio 在本地运行,通常通过 npx 直接启动,无需预先安装。需要一个 SQLite 数据库文件,由 MINDPM_DB_PATH 指定(默认位于用户主目录下)。可选的 MINDPM_HOST 和 MINDPM_PORT 用于配置内置看板,默认在 localhost 的 3131 端口提供。仅支持桌面版 MCP 客户端。
安装
在 SourceWeft 中
- 打开 控制台中的 Mindpm,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
mindpm
Project memory and a control plane for AI coding agents.
mindpm is an MCP (Model Context Protocol) server that gives LLMs a SQLite-backed brain for your projects. It tracks tasks, decisions, architecture notes, and session context — so every new conversation picks up exactly where you left off. It also hands specced tasks to coding agents, keeps parallel agents from colliding, and only counts work as done once a human has accepted it. For agents that run unattended, an optional verifier reruns the checks itself first.
The Problem
Every new LLM chat starts from zero:
- "Let me remind you about my project..."
- "Last time we decided to use Redis for..."
- "Where did we leave off?"
The Solution
mindpm persists your project state in a local SQLite database. The LLM reads and writes to it via MCP tools. No chat history needed. No memory features needed.
What It Tracks
- Tasks, specs and acceptance criteria, with handoff statuses, priorities and blockers
- Decisions: what was decided, why, and what was rejected
- Notes and context: architecture, bugs, ideas, tech stack, conventions
- Sessions: what was done and what's next, plus a brief of what changed while you were away
- Agent attempts: every run on a task, and what the next attempt should avoid
Quick start
That's Claude Code, and npx fetches mindpm, so there's nothing to install first. Claude Desktop, Cursor, VS Code, Cline and Windsurf use the same JSON config in a different file. Then just talk about your project. The LLM calls start_session to load its context, records tasks, decisions and notes as you go, and calls end_session to save what's next. A Kanban board runs at http://localhost:3131.
How it works
Everything stays on your machine, in one SQLite file (~/.mindpm/memory.db by default). mindpm can also hand specced tasks to coding agents with no conversation context: claims with leases keep parallel agents from colliding, and you accept submitted work on the board. For agents that run unattended, an optional verifier reruns the checks on each submitted commit first.
Security: mindpm has no user accounts and can't stop a process on your own machine. An agent with a shell can call the board's routes as you or edit the database directly. Read the security model before you let agents run unattended.
Documentation
Full documentation: https://umitkavala.github.io/mindpm/
- Setup: every MCP client, agent instructions for any LLM, storage
- Kanban board: lanes, accepting work, network binding and WSL
- Session brief: what changed while you were away
- Agent execution: lifecycle, specs, claims and briefs
- Advanced: unattended agents and verification
- Security model
- MCP tools
- Development
- Changelog
License
MIT
来源:README.md,提交 4c194fd
工具
0版本历史
3- v3.2.0最新Oct 5, 2026
- v3.1.1Oct 5, 2026
- v1.2.41Sep 16, 2026
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