Advanced Memory Systems

AgentDock's Advanced Memory Systems will provide long-term context management capabilities for AI agents, enabling them to maintain relevant information across conversations and sessions.

Current Status

Status: In Progress

We are developing a comprehensive memory management system that will enhance agent capabilities through improved context retention and retrieval.

Feature Overview

The Advanced Memory Systems will include:

• Short-term Memory: Conversation history and immediate context
• Long-term Memory: Persistent storage of important information
• Episodic Memory: Records of past interactions and decisions
• Semantic Memory: Understanding of concepts and relationships
• Procedural Memory: Storage of workflows and procedures
• Memory Retrieval: Context-aware access to relevant memories
• Memory Prioritization: Focus on the most important information

Architecture Diagrams

Memory Types Hierarchy

Memory Retrieval System

Implementation Details

The memory system is built on these key components:

// Core memory interface
interface MemoryManager {
  // Add a new memory item
  add(item: MemoryItem): Promise<string>;
  
  // Retrieve memories based on query
  retrieve(query: MemoryQuery): Promise<MemoryItem[]>;
  
  // Update existing memory
  update(id: string, updates: Partial<MemoryItem>): Promise<void>;
  
  // Forget/delete a memory
  forget(id: string): Promise<void>;
  
  // Get importance rating for a memory
  getImportance(item: MemoryItem): number;
}

// Memory item structure
interface MemoryItem {
  id?: string;
  content: string;
  type: MemoryType;
  metadata: Record<string, any>;
  timestamp: number;
  importance: number;
  associations: string[];
  embedding?: number[]; // For vector-based retrieval
}

// Memory types
enum MemoryType {
  FACT = 'fact',
  CONVERSATION = 'conversation',
  PROCEDURE = 'procedure',
  ENTITY = 'entity',
  RELATIONSHIP = 'relationship'
}

Current Implementation Status

Currently, we have implemented:

• Basic Message History: Core functionality for tracking conversation history
• Memory Interface Design: The foundation interface for memory operations
• Short-term Memory Management: Functionality for handling immediate context

Memory Retrieval Approaches

The system will support multiple retrieval methods:

1. Recency-based: Prioritize most recent memories
2. Importance-based: Prioritize memories with high importance scores
3. Relevance-based: Vector similarity search for context-relevant memories
4. Associative: Follow relationship links between memories
5. Hybrid: Combine multiple approaches for optimal retrieval

Dependency on Storage Layer

The Advanced Memory Systems build directly on the Storage Abstraction Layer:

• Persistent Storage: Uses the storage layer for saving memories across sessions
• Provider Flexibility: Leverages different storage backends based on deployment needs
• Scalability: Utilizes distributed storage for handling large memory volumes
• Security: Employs secure storage for sensitive memory content

Integration with Vector Storage

The Advanced Memory System leverages the Vector Storage system for:

• Semantic Search: Find memories based on meaning rather than keywords
• Contextual Relevance: Retrieve memories relevant to the current context
• Concept Clustering: Group related memories and concepts
• Cross-reference: Link related information across different memory types

Use Cases

Advanced Memory Systems will enable several key capabilities:

1. Personalization: Remember user preferences and adapt accordingly
2. Relational Context: Understand relationships between entities
3. Procedural Knowledge: Execute multi-step procedures correctly
4. Learning from History: Apply past experiences to new situations
5. Context Maintenance: Maintain consistent context over long conversations
6. Knowledge Persistence: Retain important information between sessions

Agent Integration

The memory system will integrate with AgentDock agents through:

• Memory Nodes: Specialized nodes for memory operations
• Automatic Importance Rating: Determining what to remember
• Context Window Management: Intelligent selection of memories to include
• Memory Prompting: Crafting effective prompts based on memory
• Memory-Aware Tools: Tools that can store and retrieve state

Future Enhancements

After the initial implementation, we plan to develop:

• Memory Summarization: Compress memories to conserve token usage
• Memory Forgetting: Intelligent removal of less important memories
• Memory Consolidation: Combine related memories to improve coherence
• Memory Conflict Resolution: Handle contradictory information
• User Memory Control: Allow users to manage what agents remember

Timeline

Connection to Other Roadmap Items

The Advanced Memory Systems have strong connections to:

• Storage Abstraction Layer: Provides the persistent storage foundation
• Vector Storage: Enables semantic search and contextual retrieval
• Multi-Agent Collaboration: Allows agents to share context and knowledge
• Evaluation Framework: Helps measure memory effectiveness and accuracy

Documentation

Comprehensive documentation will be provided on:

• Configuring memory capabilities for agents
• Implementing custom memory providers
• Tuning memory retrieval parameters
• Best practices for effective memory usage
• Performance and scaling considerations