Memory Types
Understand how AI agents store, retrieve, and manage information across different memory systems
Your Progress
0 / 5 completedMaster Memory Systems for AI Agents
Review these core principles of agent memory architecture. Check off each concept as you understand it. Your progress is tracked below.
Your Mastery Progress
0/15Memory Fundamentals
Memory transforms AI agents from stateless responders into intelligent systems that learn, adapt, and maintain context across interactions.
Without memory, agents start every conversation from scratch. With memory, they build relationships, remember preferences, and improve over time.
Human memory architecture (working, episodic, semantic, procedural) provides a proven blueprint for designing agent memory systems.
Short-Term Memory
The context window is agent short-term memory—fast and immediately accessible but severely limited by token count (2K-128K).
Working memory capacity is 7±2 items. Beyond this, agents struggle to maintain coherent context and information gets lost or confused.
Manage context overflow with sliding windows, summarization, importance filtering, or hybrid approaches that combine these strategies.
Long-Term Memory
Episodic memory stores specific interactions with timestamps—"What did user say on Tuesday?" Implemented via SQL/NoSQL databases with event logs.
Semantic memory stores facts and concepts without personal context—"What is X?" Implemented via vector databases with embedding-based similarity search.
Procedural memory stores executable skills and workflows—"How to do Y?" Implemented as tool functions and reusable agent capabilities.
Retrieval Strategies
Recency-based retrieval prioritizes newest memories. Fast and simple but ignores semantic relevance—good for conversational flow, poor for knowledge lookup.
Relevance-based retrieval uses vector similarity to find semantically related memories. Computationally expensive but finds truly relevant information across time.
Hybrid retrieval combines recency and relevance with weighted scoring. Recent memories get a boost, but highly relevant older ones can still surface.
Implementation Best Practices
Add timestamps and rich metadata to all memories. Enable filtering by user, topic, importance, and age before running expensive similarity searches.
Use approximate nearest neighbor (ANN) algorithms like HNSW or FAISS for fast similarity search at scale. Trade slight accuracy for massive speed gains.
Implement memory decay (older = less relevant), expiration policies, and user deletion rights. Memory systems must respect privacy and data retention laws.