mindpm: A Free MCP Server for Persistent Project Memory with Claude
mindpm is a free, open-source MCP server that gives Claude persistent project memory across conversations using a local SQLite database. It was built specifically to solve the problem of re-explaining projects at the start of each Claude Code session.
What it does
mindpm provides Claude with a local SQLite database to read and write during conversations, tracking:
- Tasks (status, priority, blockers, sub-tasks)
- Decisions (what was decided, why, alternatives rejected)
- Notes (architecture, bugs, ideas, research)
- Sessions (what was done, what's next)
The next conversation picks up exactly where you left off. For example: You ask "What should I work on next?" and Claude responds: "Last session you finished the auth refactor. You have 3 high-priority tasks: rate limiting, API docs, and the webhook retry bug. Rate limiting is unblocked — start there."
Technical details
mindpm works with any MCP-compatible client, including Claude Code, Cline, and Cursor. The entire project was built using Claude Code for MCP tool definitions, SQLite schema, session handoff logic, and the built-in Kanban board UI.
Setup takes 30 seconds with this command:
claude mcp add mindpm -e MINDPM_DB_PATH=~/.mindpm/memory.db -- npx -y mindpmEverything runs locally with no cloud, accounts, or subscriptions. The built-in Kanban board is available at localhost:3131.
Source and availability
mindpm is available on GitHub and npm. The project lives in Claude's memory via mindpm itself, creating a meta development experience.
📖 Read the full source: r/ClaudeAI
👀 See Also
Claude Code vs OpenCode: Key Technical Differences a Developer Found
A developer compares Claude Code and OpenCode on context, memory, tool use, subagents, permissions, safety, and model flexibility, concluding Claude Code still wins for production repos.
Managing Multiple AI Agent Tasks with Kanban Boards
A developer shares their experience running multiple Claude AI agents in terminal tabs and identifies three key workflow challenges: lack of progress visibility, context loss when switching between tasks, and rate limit interruptions. Their solution involves treating AI tasks like work items on a Kanban board.
LLMs Leak Reasoning into Structured Output Despite Explicit Instructions
A developer building a tool that makes parallel API calls to Claude and parses structured output found that validation models intermittently output reasoning text before corrected content, despite explicit instructions to return only corrected text. The fix involved prompt tightening plus a defensive strip function that runs before parsing.
AI Trading Agent with Risk Guardrails for Educational Investing
A developer built an AI-powered trading assistant that connects Claude to a brokerage account with a risk engine between the AI and money. The system includes safety checks like blocking trades that exceed 50% of portfolio allocation, automatic shutdown at 3% daily loss, and a kill switch at 20% drawdown.