Agent-Memory

Ever spent a full afternoon debugging a data pipeline, only to fire up Claude Code the next morning and watch it ask the same questions you answered yesterday? Yeah, me too. But your coding agent treats every fresh claude command like its first day on the job — zero context, zero memory, zero clue about the fix you spent three hours finding yesterday. I’ve tried a few solutions. But vector databases are overkill for what should be a simple problem. And Docker-based memory stacks add complexity when all I want is my agent to remember the bug I fixed at 4 PM. ...

Every AI agent has the same problem — wake up in a new session and it’s amnesia all over again. But you told it about your project structure, your preferences, yesterday’s debugging log. Gone. So OptMem from VictorTaelin (687★, released July 25) fixes this with a 426-token prompt block you paste into your AGENTS.md. That’s it. What Makes OptMem Different Here’s the thing: OptMem is not a vector database. It’s not a knowledge graph either. Nor is it another MCP server. It’s a single Python file (~/.optmem/memo) with zero dependencies that manages persistent memory for any AI agent. The trick? The tool encodes everything into a structured prompt block the agent reads at session start. The tool lives on disk while the “memory” lives in the conversation. But it’s a design philosophy so minimal it almost feels like cheating. ...

You built an AI agent that handles complex workflows — then on the next conversation, it’s a clean slate. No memory of what you discussed, no context carryover. Every session starts at zero. That’s the problem Cognee solves. It’s the open-source AI memory platform (18.4k★, actively maintained) that gives agents persistent long-term memory using a self-hosted knowledge graph. Think structured recall, not just vector embeddings. I pip installed it, checked the API, and ran through the basics. Here’s what I found. ...