Memory Architectures
LLM memory has evolved from "history buffers" to a Three-Tiered Cognitive Architecture. This hierarchy mimics human cognitive systems (L1-L3) to balance speed, cost, and recall capacity. Production agent stacks now lean on dedicated memory layers (Mem0, Zep, Letta, Cognee) on top of vector stores rather than rolling their own.
Short-Term Context Management
Short-term context (L1 Memory) is the high-speed interface where reasoning happens. Managing it well is no longer about "message lists" but about KV Cache Optimization and Dynamic Context Allocation.
Long-Term Memory
Long-term memory (L2 & L3) provides persistence across sessions. Production stacks have moved from simple "History RAG" to Multi-Representation Stores that combine Vector, Graph, and Relational data. Dedicated memory services (Zep, Mem0, Letta, Cognee) now wrap these stores with conversation summarization, entity extraction, and temporal awareness out of the box.
Agentic Memory with Mem0
Mem0 (and its peers Zep, Letta, Cognee) represents the shift from "passive logs" to Active Memory. These systems automatically digest conversations to create a persistent, evolving user profile that enhances personalization across every interaction. Pick Mem0 for the broadest standalone memory layer; Zep for temporal-aware production pipelines; Letta for long-running agents that need OS-style paging; Cognee for knowledge-graph-first RAG.
Semantic Caching
Caching has evolved from exact string matching to Semantic Matching. Semantic caching reduces costs by 30-70% and cuts latency from seconds to milliseconds by reusing completions for "equivalent" queries.
State Management Patterns
State management in AI systems has moved from simple "sessions" to Stateful Agent Graphs. Managing the flow and persistence of an agent's "mind" is as critical as the LLM itself: it is one of the main reasons LangGraph has become the default control-flow runtime for LangChain-built agents.