---
schema_version: "newruntime-agent-readable-v0.1"
type: "post"
slug: "unabyss-shared-context-layer"
title: "Unabyss Builds a Shared Context Layer Across Claude, Codex, and Cursor"
description: "Unabyss points to where agent tooling is going: context stops being one client's internal memory and becomes a separate product layer with governance and portability."
status: "published"
published_at: "2026-07-21"
topics: ["context-engineering","mcp","agent-tools"]
source_urls: ["https://unabyss.com/","https://www.producthunt.com/products/unabyss"]
routes: {"html":"https://newruntime.com/posts/unabyss-shared-context-layer/","markdown":"https://newruntime.com/posts/unabyss-shared-context-layer.md","json":"https://newruntime.com/posts/unabyss-shared-context-layer.json"}
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---

# Unabyss Builds a Shared Context Layer Across Claude, Codex, and Cursor

Claude, Codex, Cursor, and other AI clients increasingly work with the same projects, documents, and decisions. But their memory is usually separate. One tool knows the architecture context, another remembers the codebase style, a third keeps working notes. The user becomes the manual sync layer between agents.

Unabyss presents itself as an MCP-compatible personal context layer: shared structured context that different AI clients can read. This is an important product signal: context becomes a separate layer, not a setting inside one client.

## What is happening

AI clients are gradually becoming less like closed applications. They need access to repositories, tasks, documents, chats, decisions, and user preferences. If every client builds its own memory separately, fragmentation appears:

- different agents draw different conclusions about the same project;
- context gets stale in one place and updated in another;
- access control becomes hard to manage;
- memory provenance becomes difficult to inspect;
- onboarding a new tool starts from zero.

An MCP-compatible context layer moves this capability outside the client: memory and knowledge live separately, and clients connect to them as a shared resource.

## Why it matters

The context layer is not just a note database. For production use, it needs the properties of an infrastructure product:

- governance: who can read and write;
- provenance: where each piece of knowledge came from;
- portability: the user can switch AI clients without losing context;
- access control: different agents see different parts of memory;
- lifecycle: aging, confirmation, archiving, and rollback.

Without this, shared context becomes another uncontrolled prompt dump.

## QWG AI editorial read

> When there are multiple AI clients, memory stops being a feature of a single client and becomes shared access infrastructure.

Practical read: the agent tooling market will move toward role separation. The client handles interface and action. The model handles reasoning. The context layer stores verifiable state between them. The winner is not the tool that simply "remembers more", but the one that can manage trust, access, and provenance.