Unabyss

Unabyss

Unabyss provides a universal context layer for AI, delivering self-updating, segmented context to every agent and LLM via MCP.

MCPcontext layerAI agentsLLM integrationreal-time updates
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What is Unabyss?

Unabyss is a universal context layer for AI that delivers self-updating, segmented context to every agent and LLM via the Model Context Protocol (MCP). It solves the problem of fragmented, outdated context across tools like OpenClaw, Cursor, GitHub, and Notion. Users connect their data sources once, and Unabyss automatically tags, segments, and prepares context for retrieval by any connected agent. The result is clean, scoped context that stays current without manual .md file management.

Application scenarios

  • Cross-tool AI coordination

    Ensure Claude Code, Cursor, and ChatGPT all share the same up-to-date project context without manual syncing.

  • Meeting note integration

    Automatically pull context from Fathom, Fireflies, tl;dv, or Granola recordings into your AI agents.

  • Email and calendar context

    Give agents access to Gmail, Google Calendar, and OneNote data for intelligent scheduling and follow-ups.

  • Document and knowledge management

    Keep Notion, Obsidian, and Google Drive files segmented and retrievable by topic, confidence, or sensitivity.

  • Developer workflow

    Connect GitHub, VS Code, and OpenCode so your coding agents understand recent commits, pull requests, and code reviews.

  • Social and professional profiling

    Integrate Slack, X/Twitter, and LinkedIn to give agents context about your personal and professional interactions.

Core Features

  • Context segmentation

    Every incoming piece of context is automatically tagged across topic, confidence, sensitivity, source app, and personal versus professional axes. Retrieval targets only relevant slices.

  • Retrieval efficiency

    Unlike standard RAG that dumps loosely-matched chunks into the prompt, Unabyss scores and extracts only the lines that answer the question, using up to 10× fewer tokens.

  • Self-updating context

    Connected sources (Notion, Slack, Gmail, etc.) automatically refresh context so agents always have the latest information without manual .md file updates.

  • MCP integration

    Plug agents like Claude Code, OpenClaw, and Perplexity via MCP with a generated token, ensuring they have always up-to-date context.

  • Granular access control

    Choose access levels on granular item-level or topical/confidence level, then forget about manual management.

  • Hundreds of integrations

    Connect sources from a wide range of apps including Notion, Slack, Gmail, Google Drive, GitHub, Obsidian, and meeting tools like Fathom, Fireflies, tl;dv, and Granola.

  • Token generation

    Generate a single token for MCP hosts that is shown once and must be copied immediately.

  • Three-layer context engine

    Raw signal enters at the top, passes through context engineering layers (segment, compress, gate), and exits as clean, scoped context at the bottom.

Target users

Unabyss is built for professionals and teams who work across multiple AI tools and data sources—developers using Claude Code, Cursor, and VS Code; product managers managing Notion and GitHub; and knowledge workers who need their AI agents to stay current with Slack, email, and meeting notes. It's especially useful for anyone tired of manually maintaining .md files and fragmented context across apps.

How to use Unabyss?

  1. Connect sources: From hundreds of integrations, extract data, segment it, and prepare for retrieval. Connect apps like Notion, Slack, Gmail, GitHub, and more.
  2. Generate a token: Create a token for an MCP host. Copy it immediately—it won't be shown again.
  3. Plug the agent: Connect Claude Code, OpenClaw, Perplexity, or other MCP-compatible agents using the generated token.
  4. Choose access level: Set granular or topical/confidence-level permissions. From then on, your context stays up-to-date in every agent and app automatically.

Effect review

Unabyss addresses a real pain point for anyone using multiple AI agents across different tools: context fragmentation and staleness. The three-layer context engine and retrieval efficiency (up to 10× fewer tokens) suggest significant practical benefits for both speed and cost. The segmentation by topic, confidence, and sensitivity is a thoughtful touch that prevents irrelevant personal data from leaking into professional queries. While the product is still early-stage (launching on Product Hunt), the integration list is comprehensive and the MCP-based approach aligns with emerging standards. For teams already juggling multiple AI tools, Unabyss looks like a practical way to unify context without adding yet another dashboard to manage.

Frequently Asked Questions

What is Unabyss?
Unabyss is a universal context layer for AI that provides self-updating, segmented context to every agent and LLM via the Model Context Protocol (MCP).
How does Unabyss deliver context to AI agents?
Unabyss uses MCP (Model Context Protocol) to deliver self-updating, segmented context directly to AI agents and LLMs.
What does 'self-updating context' mean?
It means the context automatically refreshes and stays current without manual intervention, ensuring AI always has the latest information.
Can Unabyss handle multiple AI agents simultaneously?
Yes, Unabyss is designed to deliver segmented context to every agent and LLM, supporting multiple AI systems at once.
Is Unabyss compatible with any LLM?
Unabyss works via the MCP protocol, making it compatible with any LLM or agent that supports MCP.
What are the benefits of segmented context?
Segmented context allows different AI agents to receive only the relevant information they need, improving efficiency and accuracy.

Unabyss - AI Tool Detail

Unabyss provides a universal context layer for AI, delivering self-updating, segmented context to every agent and LLM via MCP.

Category：Agents

Visit Link：https://unabyss.com/

Tags：MCP、context layer、AI agents、LLM integration、real-time updates