Glossary · 40+ terms
AI Memory Glossary
Plain-language definitions of AI memory terms — from working memory and episodic memory to embeddings, RAG, consolidation and vector stores — the vocabulary used across AI Memory Works.
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Core
Core concepts
AI memory
External storage and retrieval that lets LLM agents remember information across turns and sessions — distinct from the context window, RAG and fine-tuning. Full guide →
Agent memory
Memory systems built for autonomous AI agents — write, store, retrieve, consolidate and forget. Synonymous with AI memory in this knowledge base.
Long-term memory
Information persisted across sessions in external stores (vector DB, graph, KV). Guide →
Short-term memory
Working memory in the context window — current task and recent turns. Guide →
Working memory
Same as short-term memory for agents — what the model sees this turn. Not computer hardware RAM.
Types
Memory types
Episodic memory
Specific past interactions the agent can reference — event logs with timestamps. Guide →
Semantic memory
Stable facts and learned knowledge — preferences, account details, general knowledge. Guide →
Procedural memory
Skills, tools and how-to procedures — how to do things, not just facts. Guide →
Parametric memory
Knowledge stored in model weights (fine-tuning) — not updatable at runtime. Guide →
Non-parametric memory
External memory stores — vectors, graphs, KV — updatable without retraining. Most agent memory is non-parametric.
Shared memory
Common store read and written by multiple agents. Guide →
Architecture
Architecture terms
Memory retrieval
Searching and ranking stored memories to inject into context. Guide →
Memory consolidation
Merging and promoting short-term memories to durable long-term storage. Guide →
Virtual context
Treating the context window like RAM and paging memories in/out. Guide →
MemGPT
Research architecture for virtual-context memory paging. Implemented in Letta. Guide →
Context rot
Accuracy degradation when stuffing too much into a long context window. Guide →
Memory as a tool
Exposing memory operations as callable agent tools. Guide →
Infrastructure
Infrastructure terms
Embeddings
Vector representations of text for semantic similarity search. Guide →
Vector database
Database optimized for similarity search on embeddings — common LTM backend. Guide →
Knowledge graph
Graph store for facts, relationships and temporal validity. Guide →
RAG
Retrieval-augmented generation — retrieving from a fixed document corpus. Not the same as agent memory. Guide →
Hybrid search
Combining vector similarity with keyword/BM25 search. Guide →
Graphiti
Temporal knowledge-graph engine used by Zep for agent memory.
Frameworks
Frameworks and benchmarks
Mem0
Managed memory API with per-user personalization. Alternatives →
Zep
Temporal knowledge-graph memory via Graphiti. Alternatives →
Letta
Virtual-context paging (MemGPT lineage). Alternatives →
Engram
Weaviate’s vector-native memory layer. Guide →
LangMem
Long-term memory for LangGraph. Guide →
LOCOMO
Benchmark for long-conversation memory recall. Guide →
LongMemEval
Benchmark for cross-session memory recall. Guide →
Disambiguation
Commonly confused terms
Limited memory AI
Not computer hardware RAM. In AI agent context, refers to agents with bounded working memory (context window) that use external stores for long-term recall. Human vs AI memory →
LSTM vs agent memory
LSTM is a neural network architecture for sequences — not the cognitive-science memory taxonomy for agents. Agent memory = external vector/graph/KV stores.
Context memory
Working memory in the context window — what the agent sees this turn. Distinct from long-term external memory.
FAQ
Frequently asked questions
What is AI agent memory?
External storage and retrieval that lets LLM agents remember information across turns and sessions — write, store, retrieve, consolidate and forget. See glossary: AI memory and the complete guide.
Episodic vs semantic memory — what's the difference?
What does MemGPT mean?
A research architecture treating context as RAM with memory paging in/out. Letta implements MemGPT. See glossary: MemGPT.
What is a vector store?
A database that indexes embedding vectors for similarity search — the typical backend for semantic long-term memory. See glossary: vector database and vector databases for memory.
RAG vs memory — are they the same?
No. RAG retrieves from fixed documents; agent memory is dynamic and personal. See memory vs RAG.
What is limited memory AI?
Not hardware RAM. In agent context it means bounded working memory (context window) plus external long-term stores. See glossary disambiguation.
What is context rot?
Accuracy degradation when too much history is stuffed into a long context window. External memory with selective retrieval mitigates it. See context rot.
What is LOCOMO?
A public benchmark testing recall from very long single conversations. See LOCOMO benchmark.