Memory

Your personal AI knowledge vault that remembers everything important about your projects, preferences, and workflows. Never explain the same thing twice.

Overview

Memory Management is an intelligent context persistence system that stores, organizes, and retrieves important information across all your AI interactions. Think of it as your AI’s long-term memory.

Key Benefits

• Never Repeat Yourself: Store context once, use everywhere
• Instant Recall: Find any memory in milliseconds
• Auto-Context: AI automatically references relevant memories
• Rich Metadata: Tags, categories, and custom fields
• Cross-Platform: Web, API, and MCP access

What Are Memories?

Memories are text-based information snippets that provide context to AI conversations:

• Project specifications
• Coding standards and conventions
• Personal preferences and style guides
• Workflow procedures
• Important facts and decisions
• Team guidelines
• Technical constraints

Creating Memories

Manual Creation

1. Navigate to Memory in the sidebar
2. Click Create New Memory
3. Enter memory details:
   • Text: The memory content
   • Project: Organization grouping (optional)
   • Metadata: Tags, category, priority, custom fields
4. Click Save

Example Memory

Project: vibexp-backend
Category: coding-standards
Priority: high

TypeScript Style Guide:
- Use functional components with hooks
- Prefer const over let
- Use async/await over promises
- Follow Airbnb ESLint rules
- Document complex functions with JSDoc

Automatic Creation via MCP

Connected AI tools can create memories during conversations:

vibexp_io_create_memory({
  text: "User prefers React with TypeScript and Tailwind CSS",
  project_name: "user/preferences",
  metadata: {
    category: "coding_preferences",
    priority: "medium"
  }
})

Organizing Memories

Project Grouping

Organize memories by project:

user/preferences
company/main-app
personal/workflows
client/project-x

Metadata Organization

Categories

Organize by category:

• coding_standards
• project_specs
• workflow_procedures
• personal_preferences
• team_guidelines

Priorities

Set importance levels:

• high: Critical context always referenced
• medium: Important but context-dependent
• low: Nice-to-have background information

Custom Tags

Add searchable tags:

• Technology: typescript, react, nodejs
• Domain: frontend, backend, devops
• Purpose: style-guide, architecture, deployment

Searching and Filtering

Full-Text Search

Search across all memory content:

Search: "React hooks best practices"

Finds all memories mentioning React hooks and best practices.

Advanced Filters

Filter memories by:

• Project: Specific project memories
• Category: Group by type
• Priority: Importance level
• Tags: Custom tag filtering
• Creation Date: Time-based filtering

Quick Access

• Recent: Last accessed memories
• Favorites: Star important memories
• Project View: All memories for a project

Auto-Context Injection

How It Works

When using AI tools connected via MCP:

1. You start a conversation
2. AI analyzes the context and topic
3. Relevant memories are automatically searched
4. Matching memories are injected as context
5. AI uses this context in responses

Relevance Matching

Memories are matched based on:

• Keywords: Content similarity
• Project: Current project context
• Priority: High-priority memories preferred
• Recency: Recently accessed memories weighted higher

Manual Reference

You can also manually reference memories:

"Using the coding standards from memory..."
"Apply the deployment procedure we discussed..."

Connected AI tools can search and retrieve specific memories on demand.

Updating Memories

Edit Existing

1. Find the memory
2. Click Edit
3. Update text or metadata
4. Save changes

Version Notes

Add a note when making significant changes:

Updated: 2024-01-15
Changes: Added new TypeScript conventions
Previous: Used any types, now strict typing

Bulk Operations

Batch Update

1. Select multiple memories
2. Click Bulk Actions → Update Metadata
3. Add/remove tags, change category, or update priority

Batch Delete

1. Select memories to remove
2. Click Bulk Actions → Delete
3. Confirm deletion

MCP Integration

Creating Memories

// AI tools create memories during conversations
vibexp_io_create_memory({
  text: "User's testing framework preference: Jest with React Testing Library",
  project_name: "user/preferences",
  metadata: {
    category: "testing",
    priority: "medium",
    tags: ["jest", "react", "testing"]
  }
})

Searching Memories

// AI tools search memories for context
vibexp_io_search_memories({
  project_name: "user/backend-project",
  search: "database",
  limit: 5
})

Retrieving Specific Memory

// Get memory by ID
vibexp_io_get_memory({
  memory_id: "mem_abc123xyz"
})

Updating Memories

// Update memory content or metadata
vibexp_io_update_memory({
  memory_id: "mem_abc123xyz",
  text: "Updated content...",
  metadata: {
    priority: "high"
  }
})

Common Use Cases

Coding Preferences

Category: coding_preferences
Priority: high

TypeScript Preferences:
- Strict mode enabled
- Functional components only
- Use Zod for validation
- Prefer composition over inheritance

Project Context

Project: client/ecommerce-app
Category: project_specs

Architecture:
- Next.js 14 with App Router
- PostgreSQL database
- Prisma ORM
- Tailwind CSS for styling
- Deployed on Vercel

Workflow Procedures

Category: workflows
Priority: medium

Git Workflow:
1. Create feature branch from main
2. Make changes with conventional commits
3. Run tests locally
4. Push and create PR
5. Wait for CI and review
6. Squash merge to main

Team Guidelines

Project: company/main-app
Category: team_guidelines
Priority: high

Code Review Guidelines:
- All PRs require 2 approvals
- Must pass all CI checks
- Update documentation for new features
- Add tests for bug fixes

Tips and Best Practices

Memory Content

• Be specific and concise
• Include relevant context
• Use clear, searchable language
• Update regularly as preferences change

Metadata Strategy

• Use consistent categories across memories
• Assign appropriate priorities
• Add multiple relevant tags
• Include project context when applicable

Organization

• Group related memories by project
• Use hierarchical projects for large organizations
• Regular cleanup of outdated memories
• Archive old memories instead of deleting

Search Optimization

• Include keywords in memory text
• Use tags for common search terms
• Add context in metadata
• Keep memory text focused

API Access

REST API Endpoints

# List memories
GET /api/v1/memories?project_name=user/project

# Get specific memory
GET /api/v1/memories/{memory_id}

# Create memory
POST /api/v1/memories

# Update memory
PUT /api/v1/memories/{memory_id}

# Delete memory
DELETE /api/v1/memories/{memory_id}

See API Keys for authentication.

Frequently Asked Questions

How many memories can I store?

Unlimited. Create as many memories as needed for your context library.

How does auto-context work?

When AI tools are connected via MCP, they automatically search your memories for relevant context based on conversation topics and keywords.

Can I control which memories are used?

Yes. Use priority levels and project grouping to control which memories are most likely to be referenced.

Are memories shared between projects?

Memories can be project-specific or global. Project-specific memories are only referenced in that project context.

Can I export memories?

Yes. Export memories individually or in bulk as JSON or Markdown files.

How secure are my memories?

All memories are encrypted at rest and in transit. Access is controlled via API keys with user-specific isolation.

Related Features

• MCP Server Integration - Auto-inject memories in AI conversations
• Artifacts - Store larger content pieces
• Prompts - Reusable AI templates