# AI Memory Systems
## Context
AI Memory Systems provide persistent storage and retrieval capabilities for AI applications, enabling them to recall past interactions and knowledge. The Memory System is a core component of the [[NeuroCore]] framework, allowing AI applications to maintain context and build upon previous interactions.
## Main Content
Effective memory management is essential for creating AI applications that can maintain context over time. Without memory, each interaction becomes isolated, limiting the AI's ability to provide personalized, contextually relevant responses.
### Types of AI Memory
1. **Short-term Memory**: Temporary storage for the current interaction or session
2. **Long-term Memory**: Persistent storage for information across multiple sessions
3. **Episodic Memory**: Storage of specific events or conversations
4. **Semantic Memory**: Storage of facts, concepts, and knowledge
5. **Procedural Memory**: Storage of action sequences or workflows
### Core Components in NeuroCore
The NeuroCore Memory System consists of:
1. **MemoryManager**: Central component responsible for CRUD operations on memories
2. **Vector Embeddings**: Mathematical representations of text that enable semantic search
3. **Memory Types**: Structured categories for different types of memories
- Messages (conversation turns)
- Summaries (condensed overviews)
- Facts (discrete pieces of information)
- Documents (longer-form content)
- Knowledge (structured information)
- Reflections (AI's thoughts on information)
### Key Features
- **Semantic Search**: Finding memories based on meaning rather than just keywords
- **Time-based Retrieval**: Accessing memories based on recency
- **Relevance Scoring**: Ranking memories by relevance to the current context
- **Memory Types**: Different storage approaches based on the nature of the information
- **Hierarchical Storage**: Organizing memories in nested structures
- **Cross-referencing**: Connecting related memories
### Implementation Example
```typescript
import { MemoryManager, MemoryType } from 'neurocore';
// Initialize memory manager
const memoryManager = new MemoryManager(database, embeddingProvider);
// Store a memory
await memoryManager.createMemory({
userId: "user123",
roomId: "room456",
content: {
text: "User prefers dark mode in applications",
role: "system"
},
type: MemoryType.FACT
});
// Retrieve memories semantically
const memories = await memoryManager.searchMemoriesByEmbedding(
queryEmbedding,
{
userId: "user123",
count: 5,
filters: { type: MemoryType.FACT }
}
);
```
### Integration with Other Systems
In NeuroCore, the Memory System integrates with:
1. **Context Builder**: To provide relevant memories for building context
2. **Reasoning System**: To supply information for reasoning processes
3. **RAG System**: To store and retrieve knowledge for generation
4. **Model Context Protocol**: To maintain context across different providers
## References & Links
- [[NeuroCore]] - Main project page
- [[202504121408 - Model Context Protocol]] - Integration with MCP
- [[202504121408 - Context-Aware AI Applications]] - Applications using memory systems
## Personal Insights
Memory systems represent one of the most critical components in moving beyond stateless AI interactions. The vector embedding approach provides a powerful capability to retrieve information based on semantic relevance rather than exact matching, which more closely mimics human memory retrieval.
The distinction between different memory types in NeuroCore is particularly valuable, as it recognizes that not all information should be stored or retrieved in the same way. Separating factual knowledge from episodic memories, for example, allows for more nuanced context building.
## Questions & Ideas
- How can memory decay be modeled to prioritize more recent or important memories?
- What are the privacy implications of persistent AI memory, and how should they be addressed?
- Could memory systems benefit from more cognitive science-inspired architectures?
- How might conflicting memories be reconciled when contradictions arise?
- What role does memory compression play in managing token limitations?
## Related Topics
- Vector Databases
- Semantic Search
- Episodic vs. Semantic Memory
- Knowledge Graphs
- Memory Consolidation
- Information Retrieval Systems