Keeping Your Context in Unabyss: How It Works
If you've read about context layers and want to know what actually using one looks like day to...
If you've read about context layers and want to know what actually using one looks like day to day, this is the practical version. Here's how Unabyss holds your context, keeps it current, and serves it to the AI tools you already use — and what that changes about working with AI.
What does "keeping context in Unabyss" mean?
It means your context — who you are, how you work, what you're currently doing — lives in one place you own, instead of being scattered across tool-specific memories, hand-written files, and your own repetition. Unabyss is the layer that holds it and feeds it to whatever AI you're using.
The contrast is with how most people do it now: a bit of context in ChatGPT's memory, a CLAUDE.md in one repo (the AGENTS.md pattern), a preferences field in Claude, and the rest re-typed every session. Keeping your context in Unabyss consolidates that into a single source the tools draw from.
How does Unabyss get your context?
By extracting it from the sources where it already exists. You connect tools you already use — email, calendar, Notion, LinkedIn, GitHub — and Unabyss pulls structured context from them: your role, your projects, your writing, your preferences, the shape of your work. The setup takes a few minutes up front, and the point of it is that you're not writing your context by hand. It's drawn from your real activity.
This is the difference from a context file. You don't sit down and type out who you are and keep it updated. Unabyss assembles it from your actual sources and refreshes as those sources change.
How does it stay current?
Because it's connected to your sources, your context updates as your situation does, rather than freezing at whatever you last typed. A project wraps, a new one starts, your role shifts — the context reflects it without you maintaining a file. And when you want to correct or adjust something directly, you can do it conversationally through the context chat: tell it what changed, and it updates the structured context in place.
This is the part that hand-maintained approaches can't match. A Markdown file or a settings field is only as current as the last time you remembered to edit it. A connected context layer keeps the most dynamic part of your context — what you're working on now — actually current.
How does it reach your AI tools?
Through MCP, the open protocol that the major AI tools support for connecting to external context and tools. You authorize the AI tools you use — Claude, ChatGPT, Cursor, other MCP-compatible tools — and Unabyss serves your context to them at the start of a session. The AI starts informed instead of cold, and you didn't paste anything.
Crucially, you decide what each tool sees. Permissions are per-tool: you control which parts of your context a given AI can access, and you can revoke it. Your context isn't copied and scattered into each vendor's system — it stays in the layer you own, served on demand to the tools you've authorized.
What does this change day to day?
The re-explaining stops. You open a new Claude conversation and it already knows your role and your current work. You try a new AI tool and it starts informed instead of blank. You switch between Claude, ChatGPT, and Cursor and they all draw on the same accurate picture of you, rather than each holding a different partial version.
The shift is from maintaining context in many places — and repeating it constantly — to keeping it in one place that travels. Your tools become interchangeable; your context is the durable part you own.
→ What a context layer is, in general: What Is Personal Context for AI?
→ How context gets delivered to tools: How to Deliver Personal Context to AI Tools
→ Keep your context in one place that travels — Unabyss →
