Pantheon ($EON)
  • Welcome
  • Welcome to Pantheon (EON)
    • Introduction to Pantheon (EON)
      • What is Pantheon (EON)
      • Vision & Philosophy
    • Why Pantheon?
      • Challenges Addressed to EON
      • Use Cases & Applications
    • Technology Foundations
      • Overview of Key Technologies
      • Comparisons with Traditional AI Architectures
  • The Pantheon (EON) Ecosystem
    • User Journey
      • User Workflow: From Prompt to Project
  • The Pantheon (EON) Core
    • Overview
      • Core Principles
      • End-to-End AI Workflow
    • Distributed AI Registry
    • Orchestrators
      • Task Management and Resource Allocation
      • Project Mining
    • Agents
      • Execution Lifecycle
      • Integration with Tools & Memory Systems
    • Tools
      • Atomic Functionality and Monetization
      • Development and Registration Guidelines
    • Projects
      • Building Projects
      • Security & Configuration
  • The Knowledge Layers
    • Overview
    • Shared Memory
    • Private Memory
  • Data Sources
    • Real-Time Data Ingestion
    • Data Schemas
    • Event Listeners
  • Security Control
    • Access Control
    • Registry Security
    • Data Security
    • Tool Security
  • Development & Contribution
    • Frequently Asked Questions
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On this page
  • Key Features of Shared Memory
  • 1. Global Knowledge Base
  • 2. Qdrant-Powered Infrastructure
  • 3. Data Contribution and Updates
  • 4. Access Controls
  • Use Cases for Shared Memory
  • 1. Semantic Search
  • 2. Collaboration Across Projects
  • 3. Centralized Learning
  • Why Shared Memory Matters
  • Explore Further
  1. The Knowledge Layers

Shared Memory

Shared Memory in the Pantheon (EON) ecosystem is a global knowledge base that provides agents and tools with access to universal data and insights. Powered by Qdrant, a vector-based semantic search engine, Shared Memory is optimized for scalability, high-speed querying, and collaboration across projects. This layer serves as a foundation for enabling intelligent, context-aware decision-making.

Key Features of Shared Memory

1. Global Knowledge Base

Shared Memory stores general knowledge that can benefit multiple agents and workflows:

  • Semantic Data: Information stored as vector embeddings for efficient similarity matching.

  • Wide Accessibility: Readable by any authorized agent or workflow in the ecosystem.

  • Centralized Context: Provides a universal reference for common concepts and tasks.

This feature ensures consistency and reusability of data across projects.

2. Qdrant-Powered Infrastructure

The Shared Memory system uses Qdrant to enable:

  • Semantic Search: Retrieve relevant data using vector embeddings and similarity scoring.

  • Efficient Querying: Optimized for handling large-scale datasets with low-latency responses.

  • Scalability: Horizontal scaling to support high concurrency and data growth.

Qdrant ensures that Shared Memory remains performant and robust.

3. Data Contribution and Updates

Shared Memory is a dynamic knowledge base that evolves over time:

  • Global Contributions: Agents and workflows can contribute new knowledge to Shared Memory.

  • Versioning: Updates are version-controlled to maintain data integrity.

  • Collaborative Growth: Encourages the sharing of non-sensitive data to enrich the ecosystem.

This adaptability makes Shared Memory a living resource that grows with the ecosystem.

4. Access Controls

To maintain security and governance:

  • Read-Only Access: Most agents and workflows have read-only access to Shared Memory.

  • Restricted Contributions: Only authorized entities can write to Shared Memory.

  • Fine-Grained Permissions: Access controls ensure data is used responsibly.

These measures balance openness with security.

Use Cases for Shared Memory

1. Semantic Search

Agents use Shared Memory to:

  • Retrieve relevant documents or embeddings for context-aware outputs.

  • Perform similarity searches to identify related knowledge.

2. Collaboration Across Projects

Shared Memory enables:

  • Reuse of general knowledge across different workflows.

  • Consistent context for agents working on similar tasks.

3. Centralized Learning

Agents can contribute non-sensitive insights or updates to:

  • Enrich the ecosystem's collective knowledge.

  • Provide reusable data for future workflows.

Why Shared Memory Matters

Shared Memory enhances the Pantheon (EON) ecosystem by:

  • Promoting Collaboration: Facilitates knowledge sharing across projects.

  • Improving Efficiency: Reduces redundancy by providing a centralized knowledge base.

  • Supporting Scalability: Ensures agents and workflows can handle large-scale operations.

By enabling global accessibility to semantic knowledge, Shared Memory lays the foundation for intelligent, scalable AI solutions.

Explore Further

PreviousOverviewNextPrivate Memory

Last updated 3 months ago

Private Memory

Learn how Private Memory handles project-specific and sensitive data

Memory Comparison

Understand the differences between Shared and Private Memory