---
source: hn
url: https://xysq.ai/
published_at: '2026-07-12T22:28:38'
authors:
- ximihoque
topics:
- agent-memory
- code-intelligence
- multi-agent-systems
- human-ai-interaction
- software-foundation-model
relevance_score: 0.86
run_id: materialize-outputs
language_code: en
---

# Show HN: Collaborative context-sharing memory platform for agents and teams

## Summary
xysq is a shared memory layer for AI agents and teams. It combines data from workplace tools into isolated, consent-controlled team vaults that agents can query across applications.

## Problem
- Team context is scattered across Slack, Drive, Notion, email, and individual workspaces, so agents lack persistent access to decisions and working knowledge.
- Useful context can disappear when employees change roles or leave, increasing repeated work and weakening institutional memory.
- Sharing memory across agents creates privacy, ownership, deletion, and model-training concerns.

## Approach
- Connectors ingest files and conversations from existing team tools and organize them into a living knowledge graph.
- Team vaults keep each organization’s memory isolated and expose it to authorized agents through an app, API, SDKs, and reference architectures.
- Consent gates cross-agent access, while encryption protects memory in transit and at rest.
- Users can inspect, export, and delete stored memory, and xysq states that it does not use customer memory to train models.

## Results
- The excerpt provides no quantitative results, benchmark dataset, latency measurement, accuracy metric, or baseline comparison.
- The strongest concrete product claims are persistent context across connected AI tools, queryable team knowledge from Slack, Drive, Notion, and other sources, and integration without rebuilding existing agents.
- The product claims user-controlled memory ownership through consent-gated access, export, and deletion controls, but the excerpt provides no independent verification or deployment data.

## Link
- [https://xysq.ai/](https://xysq.ai/)