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Documentation Index

Fetch the complete documentation index at: https://docs.moodmnky.com/llms.txt

Use this file to discover all available pages before exploring further.

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Agent Database System

The MOOD MNKY agent system is powered by a sophisticated network of interconnected databases that store and manage all aspects of agent functionality. This modular database architecture follows a first-principles approach to agent design, with clear separation of concerns between agent identities, capabilities, knowledge sources, memory systems, and integration points.

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System Architecture

Agent Database Architecture Diagram The system consists of seven specialized databases:

Agents Database

Core identity and characteristics of each agent, including personality traits, communication styles, and archetypes

Agent Capabilities

Technical and functional capabilities of each agent, with implementation details and performance metrics

Integration Points

Connection points with other systems, including API specifications and data exchange formats

Knowledge Base

Information sources powering agent capabilities, with indexing mechanisms and update frequencies

Memory Systems

Agent memory architecture enabling context retention, with storage mechanisms and retrieval strategies

User Profiles

User preference data enabling personalization across all agent interactions

Agent Training

Training methodologies, data sources, and performance metrics for agent improvement

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Database Relationships

The power of this system comes from the relationships between databases:

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Implementation in Notion

The agent database system is implemented in Notion, providing:
  • Centralized Management: All agent information managed in one workspace
  • Real-time Collaboration: Multiple team members can update simultaneously
  • Automated Integration: Changes sync with production systems via n8n workflows
  • Version History: Complete history of agent development and changes
  • Rich Media Support: Documentation with images, videos, and interactive elements
  • Customizable Views: Different perspectives for different stakeholders

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Using This Documentation

Each database has its own dedicated page in this documentation, covering:
  • Purpose and Role: What the database stores and why it’s important
  • Schema and Structure: Data model, fields, and relationships
  • Integration Details: How it connects with other databases
  • Usage Examples: Common queries and operations
  • Best Practices: Guidelines for maintaining and extending
For implementation details, developer guides, and technical reference, see the Developer Guide.