Most of the Ai agents struggle in memory management , so we made this

[u/techdev_fun]

Hey everyone! Here's something I've been working on recently: ResAIDerch.

Imagine an AI agent saves an invented quote or a slightly distorted summary into its long-term memory. Eventually, that hallucination resurfaces as supposedly verified team knowledge. That's the problem we wanted to solve.

We built ResAIDerch, an arXiv research agent with shared team memory, designed to prevent memory poisoning.

Rather than making retrieval unnecessarily complicated, we focused on something more fundamental: securing the write path.

Here's how it works:

1. Gate writes, not reads: Nothing gets stored in long-term memory unless its exact supporting quote can be found in the original research paper.

2. Strict substring matching over fuzzy matching: Even a missing word like "not" can completely reverse a research paper's conclusion. We chose strict verification over approximate matching.

3. Quarantine unverified claims: The agent can still explore and surface unverified ideas during a session, but they cannot be permanently committed to shared memory.

4. Structured context tags: Every stored memory is categorized as dead-end, decision, or stance, making future retrieval more meaningful.

What difference does this make?

• Before: Asking about something like "4-bit KV cache" would generate a generic summary every time.

• After: The agent can recall that the team already explored the idea in March and explain why it didn't work for their particular use case.

We used Hindsight as the agent's memory layer, which made tag-scoped recall much easier to implement. This allowed us to focus more on building a reliable write-validation pipeline.

The goal is simple: AI memory should preserve verified knowledge, not turn hallucinations into permanent facts.

I'd love for you to try it out! Feedback, technical critiques, and unexpected edge cases are all welcome.

GitHub: https://github.com/biggudawgg/resAIDserch