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

# Recall: Persistent Conversational Memory System

> Overview Recall is a memory persistence layer for the AI brain that solves two fundamental limitations in current AI systems: 1. Context window limits — Conv...

<Info>
  Normalized for Mintlify from `knowledge-base/neurigraph-memory-architecture/hyperthyme-memory-framework/legacy-memory-recall-overview.mdx`.
</Info>

# Recall: Persistent Conversational Memory System

## Overview

Recall is a memory persistence layer for the AI brain that solves two fundamental limitations in current AI systems:

1. **Context window limits** — Conversations eventually exceed what the AI can "see" at once
2. **Session persistence** — Information is lost when a chat ends or a new session begins

## How It Works

Recall continuously captures conversation content into simple markdown files at configurable intervals (e.g., every N tokens or based on other metrics). These files serve as a searchable memory archive that exists outside any single conversation.

### The Flow

```
Conversation happens
       ↓
Every [configured interval], save conversation chunk to .md file
       ↓
Files accumulate over time as persistent memory
       ↓
Later: "Do you remember X?"
       ↓
AI checks current context → Not found
       ↓
AI searches recall files → Finds relevant file
       ↓
AI reads file → Now has full context
       ↓
AI responds with remembered information
```

### Key Characteristics

* **Format**: Plain markdown files (simple, readable, portable)
* **Trigger**: Configurable intervals (token count, time, or custom metric)
* **Scope**: Works across any chat session—not tied to a single conversation
* **Retrieval**: Search-based lookup when current context lacks needed information

## Why This Works

Traditional AI memory approaches often involve:

* Complex vector databases
* Embedding-based semantic search
* Summarization that loses detail

Recall takes a simpler path: just keep the actual text. When you need it, read it. The AI can process natural language natively, so there's no need to transform the memory into a different format—markdown files are already in the language the AI understands.

## Use Cases

* Recalling project decisions made weeks ago
* Picking up a topic from a previous session
* Cross-referencing information discussed in different chats
* Building continuity in long-running projects

***

*Part of the AI Brain architecture*