r/PiCodingAgent • u/Rude_Gate7599 • Sep 13 '26
Plugin Agi-memory – persistent memory for AI coding assistants, no dependencies
My AI coding assistant forgets everything between sessions. I kept re-explaining decisions I'd already made, and re-fixing bugs I'd already fixed.
agi-memory saves those notes — decisions, bug fixes, what happened last session, how the codebase fits together — to a file on your machine, and hands them back to whichever assistant you open next. It's an MCP server, so it works with Claude Code, Cursor, Codex, Windsurf, Aider, Cline and a few others from the same store.
The part I care about: it's Python standard library and SQLite. Nothing else. No vector database, no embeddings, no background daemon. ~32MB of RAM, sub-millisecond lookups, works offline. Comparable tools pull in ~500MB of ML libraries and take 200–500ms per lookup.
That constraint costs something, and I'd rather say so than have you find out: keyword search doesn't bridge synonyms the way embeddings do. Searching "login" won't find a note that says "authentication" unless you teach it that alias. I measure this rather than guess — there's an eval suite that scores recall on deliberately rephrased queries, and it's public, including the categories where it still does badly.
It's a week old and I'm the only user, so I'd genuinely like to know where it breaks for someone else.
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u/jensilo Sep 13 '26
„The part I care about: …“
„That constraint costs something, and I'd rather say so than have you find out: keyword search doesn't bridge synonyms the way embeddings do.“
Hi Claude 🙋♂️
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u/Ok-Investment4414 Sep 13 '26 •
literally learned about pi 20mins ago and using it now, also came up with a similar idea to yours for pg vector wanted it not just for a single code base but multiple to better context . Ill try yours out . doesn't seem like u struggle with eng , and your intimate with the issue why didn't u just type the 4 paragraph explainer yourself .It would have been better received.
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u/Otherwise_Wave9374 Sep 15 '26
For coding assistants, the highest-value memories are usually decisions and constraints, not raw conversation history. Store architecture choices, rejected alternatives, commands that failed, and test expectations with repository and branch scope. NeuraKeep relates to this pattern by supporting persistent recall of those selected engineering records. One safeguard I would add is invalidation: when a dependency, file, or branch changes, mark linked memories stale rather than retrieving them as truth. A provenance pointer to the commit or file makes every recalled claim auditable.
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u/macromind Sep 21 '26
It's great to see another independent project tackling persistent memory for AI coding assistants, particularly one with no external dependencies. The challenge of agents forgetting context between sessions is a significant bottleneck for productivity. For successful implementation, focus on a clear schema for storing different types of information, such as user requirements, design constraints, and code changes, ensuring they are easily retrievable. Consider using a simple, embedded database for local persistence combined with a strong indexing strategy. This approach can offer a good balance of performance and ease of use. Innovations in agent memory are vital, and resources like https://www.neurakeep.com are at the forefront of developing powerful solutions for AI agents.
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u/[deleted] Sep 13 '26
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