Why Memory Matters
AI systems need memory for three key purposes:- Maintaining context during conversations
- Learning from past interactions
- Building personalized experiences over time
Short-Term Memory
The most basic form of memory in AI systems holds immediate context - like a person remembering what was just said in a conversation. This includes:- Conversation History: Recent messages and their order
- Working Memory: Temporary variables and state
- Attention Context: Current focus of the conversation
Long-Term Memory
More sophisticated AI applications implement long-term memory to retain information across conversations. This includes:- Factual Memory: Stored knowledge about users, preferences, and domain-specific information
- Episodic Memory: Past interactions and experiences
- Semantic Memory: Understanding of concepts and their relationships
Memory Characteristics
Each memory type has distinct characteristics:Type | Persistence | Access Speed | Use Case |
---|---|---|---|
Short-Term | Temporary | Instant | Active conversations |
Long-Term | Persistent | Fast | User preferences and history |
How Mem0 Implements Long-Term Memory
Mem0’s long-term memory system builds on these foundations by:- Using vector embeddings to store and retrieve semantic information
- Maintaining user-specific context across sessions
- Implementing efficient retrieval mechanisms for relevant past interactions