> ## Documentation Index
> Fetch the complete documentation index at: https://docs.statebase.org/llms.txt
> Use this file to discover all available pages before exploring further.

# What is State? (vs. Memory)

> Understanding the difference between long-term facts and active logic.

One of the most common mistakes in agent design is treating "State" and "Memory" as the same thing. In StateBase, they serve two distinct purposes.

## 1. State (Active Logic)

**Think of this as the "RAM" of your agent.**

State is a structured JSON object representing the **current condition** of the agent. It is what the agent uses to make its *immediate next decision*.

* **Structure**: Strongly typed schemas (e.g., `{"status": "onboarding", "step": 2}`).
* **Behavior**: Updated frequently, often every turn.
* **Goal**: Deterministic logic flow.
* **Example**: A shopping assistant's current `cart_items` list.

## 2. Memory (Long-term Facts)

**Think of this as the "Hard Drive" of your agent.**

Memory consists of unstructured or semi-structured facts extracted from previous interactions. It is retrieved semantically when relevant.

* **Structure**: Natural language or key-value pairs (`"User is allergic to peanuts"`).
* **Behavior**: Static until reinforced or corrected.
* **Goal**: Personality and long-term personalization.
* **Example**: The fact that a user lives in Seattle and prefers python.

## The Synthesis: Context

When you call `sb.sessions.get_context()`, StateBase merges these two:

1. It retrieves the **Current State** (Cart items: 3).
2. It fetches **Relevant Memories** (User usually buys gluten-free).

**The Result**: A perfectly primed prompt that is both logically accurate and personally relevant.