How I Gave My AI Agents a Permanent Memory That Syncs Across Machines

If you've spent any time working with AI coding agents like Claude Code, you've probably noticed the elephant in the room: every session starts from scratch. Your agent debugs a tricky deployment issue, discovers that your project needs a specific environment variable, figures out the architecture of your codebase — and then forgets all of it the moment the session ends. The next session? Back to square one. I built agent-knowledge to fix this. It's an open-source MCP server that gives AI agents a persistent, git-synced memory — a knowledge base they can read from and write to across sessions, across machines, and across different AI clients. The Problem: Ephemeral Agent Sessions When you're working with Claude Code (or any AI coding agent), the conversation is stored as a JSONL transcript file — but the agent itself can't search or learn from previous sessions. Every new session is a blank slate. This means you end up repeating yourself constantly. You re-explain your project structur