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

# Memory Node

> Enable persistent knowledge with conversation history and knowledge bases

# Memory Node

**Memory** gives your agent persistent knowledge and long-term context.

***

## Overview

**Purpose:** Store knowledge, conversation history, and reference information
**Connection:** Bottom-left handle of main agent card
**Color:** Green
**Icon:** 🧠
**Required:** Optional (0 to many)

***

## What is Memory?

Memory nodes contain markdown files that provide your agent with:

* Domain knowledge
* Company information
* Conversation context
* Reference documentation
* Historical data

***

## Memory Structure

Memory is organized as markdown files in folders:

```
memory-layer/
├── company-info.md
├── team-contacts.md
├── product-specs.md
└── faq.md
```

Each file contains knowledge in plain markdown format.

***

## Memory Types

### 1. Knowledge Base

**Purpose:** Domain-specific information

**Example: Company Knowledge Base**

```markdown theme={null}
# TechCorp Company Information

## About Us
TechCorp is a software company specializing in...

## Products
- Product A: Cloud-based solution for...
- Product B: On-premise software for...

## Support Hours
Monday-Friday: 9am-6pm EST
```

**Use case:** Customer support agents with company context

***

### 2. Prime Context

**Purpose:** Always-available context included in every interaction

**Example: Agent Guidelines**

```markdown theme={null}
# Agent Operating Guidelines

## Communication Style
- Professional but friendly
- Concise responses
- Always cite sources

## Restrictions
- Never share customer data
- Don't make promises about features
- Escalate billing issues to finance
```

**Use case:** Ensure consistent agent behavior

***

### 3. Conversation History

**Purpose:** Maintain context across sessions

**Example: Previous Interactions**

```markdown theme={null}
# Customer: John Doe (ID: 12345)

## 2025-01-05 - Password Reset
Customer requested password reset. Issue resolved.

## 2025-01-08 - Billing Question
Asked about invoice #5678. Referred to finance team.
```

**Use case:** Personalized multi-session support

***

## Creating Memory

**Via Settings → Node Managers → Memory:**

1. Click "Add New Memory Layer"
2. Enter name (e.g., "Company KB")
3. Add markdown files
4. Write content in each file
5. Save to workspace library

**Memory layer appears in sidebar → drag to canvas → connect to main agent.**

***

## Configuration

**When clicked on canvas:**

* Add/edit markdown files
* Organize into subfolders
* Set priority (which files load first)
* Configure memory scope (always vs. contextual)

***

## Real-World Examples

### Example 1: Customer Support Knowledge Base

**Structure:**

```
customer-support-kb/
├── products.md          # Product catalog
├── policies.md          # Company policies
├── troubleshooting.md   # Common issues
└── contacts.md          # Team contacts
```

**products.md:**

```markdown theme={null}
# Product Catalog

## TechCorp Cloud
- Pricing: $99/month
- Features: Unlimited storage, 24/7 support
- Trial: 14-day free trial

## TechCorp Enterprise
- Pricing: Custom
- Features: On-premise, dedicated support
- Contact sales for pricing
```

***

### Example 2: Code Review Style Guide

**Structure:**

```
code-review-guide/
├── style-guide.md       # Coding standards
├── security-checklist.md # Security best practices
└── examples.md          # Good/bad examples
```

**style-guide.md:**

```markdown theme={null}
# Python Style Guide

## Naming Conventions
- Functions: snake_case
- Classes: PascalCase
- Constants: UPPER_SNAKE_CASE

## Imports
- Standard library first
- Third-party second
- Local imports last

## Example
\`\`\`python
import os
import sys

import requests

from .utils import helper
\`\`\`
```

***

## Memory Patterns

### Pattern 1: Layered Knowledge

```
[Main Agent]
     ↓
[General KB] + [Specialized KB] + [User History]
```

**Use case:** Agent with broad knowledge + domain expertise + personalization

***

### Pattern 2: RAG (Retrieval-Augmented Generation)

**Concept:** Agent retrieves relevant memory chunks based on user query

**Implementation:**

1. Store large knowledge base as memory
2. Agent searches relevant sections
3. Agent uses retrieved context for response

**Use case:** Documentation Q\&A, technical support

***

## Best Practices

**✅ Do:**

* Use clear markdown headings
* Keep files focused (one topic per file)
* Update regularly
* Include examples and specifics
* Organize with folders

**❌ Don't:**

* Store huge files (split into smaller chunks)
* Include outdated information
* Use complex formatting (keep simple)
* Duplicate information across files

***

## Memory vs. System Prompt

| Feature         | System Prompt          | Memory                 |
| --------------- | ---------------------- | ---------------------- |
| **When to use** | Instructions, behavior | Facts, knowledge       |
| **Example**     | "You are helpful"      | "Company founded 2020" |
| **Changeable**  | Rarely                 | Frequently             |
| **Scope**       | Every interaction      | Contextual retrieval   |

**Rule:** Instructions in prompt, facts in memory.

***

## Next Steps

* [Commands Node](/docs/node-types/commands-node) - Create slash commands
* [MCPs Node](/docs/node-types/mcps-node) - External integrations

***

<div align="center">
  [← Skills Node](/docs/node-types/skills-node) | [Agent Node](/docs/node-types/agent-node) | [Commands Node →](/docs/node-types/commands-node)
</div>