Persistent AI Memory via Obsidian MCP: 16 Tools for Claude Cowork
A developer built a persistent memory system for Claude Cowork (Claude Opus 4.6) by connecting it to an Obsidian vault via a custom Model Context Protocol (MCP) server. The system solves session memory loss without bloating the context window.
Key Details
- Core architecture: An Obsidian vault acts as a queryable knowledge store outside the context window. The AI loads only a context manifest at session start, then queries specific knowledge on demand.
- Custom MCP server: A lightweight Python script exposing 16 tools that map to Obsidian's local REST API endpoints, with headers set explicitly. The server runs locally, allowing Claude to read, write, and search the vault.
- Structured vault: Uses frontmatter metadata and Dataview queries for structured retrieval.
- Context budget: Session start allows 5 MCP calls to keep context under control.
- Comparison to CLAUDE.md: The author notes that
CLAUDE.mdfiles solve project state (current state, next steps) but cannot scale to hold research, retrospectives, troubleshooting, or session history without overloading context.
Who It's For
Developers using Claude Cowork who need persistent memory across sessions for projects with extensive history, research, or troubleshooting logs.
📖 Read the full source: r/ClaudeAI
👀 See Also
Developer Uses Claude AI to Build PosturePal Posture Scanner App
A developer built PosturePal: Posture Scanner using Claude AI for multiple aspects including code, product decisions, user feedback communication, and copywriting. The app analyzes side profile photos to provide posture scores, identify specific issues, and generate tailored exercises.
Episode 9 of Building an AI-Run Store: Multi-Agent Coordination for Claude Code Agents
The latest episode in the orchestrator series covers how six Claude code agents coordinate to hand off work, avoid conflicts, and maintain state across sessions when running an AI company.
Claude AI Agents Build Simulator, Optimize Game Algorithm to Beat Human Score
A developer tested Claude AI agents on the programming game The Farmer Was Replaced by having them build a Python simulator of the game, then iteratively develop a sunflower harvesting algorithm. The AI achieved a time of 5:21, beating the developer's personal best and reaching rank 30 on the global leaderboard.
Mesh Architecture for AI Agents: Client Isolation and Cross-Project Coordination
A developer running a micro-agency describes a mesh architecture where each client gets specialized AI agents that communicate via markdown files, enabling domain expertise, cross-project coordination, and client isolation across 44 projects and 14 organizations.