MILKYWEB INTRODUCES

General

Purpose

Graph

— LLM layer that adds long-term dynamic memory to AI agents

GPG structures raw input into a growing semantic graph — allowing the system to remember, reason, and act across time. Instead of relying on ephemeral context windows, it builds persistent memory and world models that evolve with each interaction.

From Chat to Structured Knowledge

It remembers what you say — and helps when you need it.
‍Told it about a meeting last week? Milkyweb Agent can remind you, reschedule it, or even help you follow up. No digging through old notes — your assistant is already on it.

WHY IS THIS NEW

Dynamic Memory: Common AI agents' architectures rely on rigid memory APIs with fixed data structures, which limits scalability — we allow agents to dynamically create, connect, and reason over data within an expanded semantic graph.

Entity Extraction: By structuring information during conversation, the system turns mentions of entities of the real world — people, books, or tasks — into evolving entities it can remember and act upon.

Context Outside LLM: Instead of stuffing all memory into the LLM’s context window, we store it in a graph — making the system more reliable, interpretable, and less prone to hallucinations.

Zero-Friction Input: Users shouldn’t have to think about organizing data — the agent handles it automatically, reducing friction and cognitive overhead.

WHAT IS A SEMANTIC GRAPH?

A semantic graph is a structured representation of knowledge — where entities (like people, places, ideas) are connected by meaningful relationships. It’s how machines can understand and reason about the world, not just memorize facts.

Unlike plain text or flat databases, semantic graphs capture context, hierarchies, and connections between concepts. For example:

"Dune" → is a → "movie"
"Dune" → director → "Denis Villeneuve"
"Dune" → available at → "local cinema"

This structure lets the system build understanding, ask intelligent questions, and take action — all based on how things relate to each other.

HOW IT WORKS?

Text-To-Graph Approach

Traditional tools treat information as text blobs or search vectors.
We treat information as meaning.

With our Text-To-Graph approach, everything you say becomes part of a structured graph — a model of your world that grows as you interact.

Mention a friend, an event, a task, or a preference — and the agent understands what it is, how it's related to other things, and what can be done with it.

You do not tag, categorize, or define anything - it's all handled by the engine.

The system infers structure from your language, builds relationships, creates hierarchies, and connetions.

Over time, it has a detailed semantic model of your world — so it can reason and act within your personal context.

Graph-Powered Automation

As the semantic graph grows, it becomes a powerful engine for automation and reasoning:

Run logic

— Attach JS-like scripts to entities and relationships to power business rules.

Listen for changes

— Trigger behavior when specific patterns appear or data updates.

Query data

— Search and filter structured data just like a database.

Schedule tasks

— Create future actions linked to any entity in the graph.

Reason over structure

— Use the graph to infer new insights and support complex decisions.

Your agent becomes a programmable environment

With structured, contextual data in the graph, you can query information, run scripts, schedule tasks, define triggers, and build workflows. This makes it possible to implement any behavior — from simple reminders to rich, multi-step automations.

Early Access is Open

We’re excited to launch it as a standalone product for developers, researchers, and AI builders who want structured, dynamic memory for intelligent systems.

Thanks! You're in.
We'll let you know the moment early access is ready.

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