Memory Systems
Working memory, long-term storage, retrieval, and context management.
Conversation Management
Conversation management tracks dialogue state across multiple turns, deciding when to keep full history versus summarize, how to structure system/user/assistant message roles, and how to maintain coherent multi-turn context within token constraints.
Episodic Memory
Episodic memory stores records of past interactions, task attempts, and experiences, enabling agents to learn from previous successes and failures and to retrieve relevant episodes that inform current decision-making.
Long-Term Persistent Memory
Long-term persistent memory enables agents to store and retrieve information across sessions using vector stores, databases, and files, overcoming the ephemeral nature of context windows through systems like MemGPT’s hierarchical memory management.
Memory Architecture Overview
Agent memory architectures mirror cognitive science’s division of memory into sensory (raw input buffering), working (active processing), and long-term (persistent storage), with each type serving a distinct purpose and implemented through different technical mechanisms.
Memory Compression
Memory compression reduces the token footprint of stored information through summarization, hierarchical compression, and selective forgetting, enabling agents to maintain longer effective memories within fixed context window budgets.
Memory Retrieval Strategies
Memory retrieval strategies determine how agents find the right memories at the right time, combining recency (recent is relevant), relevance (semantic similarity), and importance (scored by impact) into hybrid scoring functions that surface the most useful information.
Semantic Memory
Semantic memory stores factual knowledge – entities, relationships, concepts, and structured data – that agents access and update beyond what is encoded in the LLM’s weights, implemented through knowledge graphs, databases, and entity stores.
Short-Term Context Memory
Short-term context memory is the agent’s working memory implemented through the LLM context window, a finite buffer of tokens that holds the current conversation, recent observations, and active reasoning, constrained by the fundamental limit of context window size.