AI agent memory
What is AI agent memory?
AI agent memory is the capability that lets an AI agent store, recall, and reuse knowledge across sessions — instead of starting cold on every new conversation.
In short: large language models are stateless — each call gets a context window, then forgets it. For long-running work, lost context becomes the bottleneck. A memory layer fixes that, but the hard part is not storing text. It is consolidation, learning from outcomes, and verification so bad memories do not pollute future work.
How it works
The main approach families
Most memory systems fall into a few design families. They solve different parts of the problem — the full guide covers each in depth.
Vector memory
Embeddings + similarityStore memories as embeddings and retrieve them by similarity. Simple and scalable, but flat — it has no hierarchy, no explicit relationships, and no sense of whether a recalled memory actually helped.
Graph memory
Connected facts over timeRepresent entities and how they relate, including how facts change. Better at "what changed?" and "how are these connected?" than plain similarity, at the cost of more modeling complexity.
Hybrid (vector + graph)
Breadth + structureCombine similarity search for fuzzy recall with graph or key-value structure for relationships and stable facts. Increasingly the practical middle ground for production systems.
OS-tiered / self-editing
The agent manages its own memoryTreat memory as part of the runtime: the agent moves items between in-context and out-of-context tiers and edits them via tool calls during reasoning, rather than relying only on a passive retrieval backend.
Go deeper
The full guide and the cluster
This page is the overview. The in-depth guide and the supporting articles live in the docs.
AI Agent Memory — the full guide
The category hub: the definition, every approach family, how to choose a memory layer, how it is evaluated (LoCoMo, LongMemEval, BEAM), and the open problems — with links to the whole cluster.
Related deep-dives
- Is Mnemoverse a vector database? — memory layer vs. vector store
- Memory MCP — give an agent persistent memory over MCP
- How to evaluate AI agent memory — benchmarks and what they miss
- The 2026 AI agent memory landscape — the market, mapped
Questions
AI agent memory FAQ
AI agent memory is the capability that lets an AI agent store, recall, and reuse knowledge across sessions instead of starting cold on every new conversation. Unlike a single context window, it persists useful information over time and brings it back when it is needed.
A vector database stores embeddings and retrieves similar items. An agent memory layer may use vectors, but it also has to decide what to store, how to consolidate it, when to forget, and how to verify that recalled information is still trustworthy.
Add a persistent layer outside the model that saves facts, events, preferences, or procedures and retrieves them across sessions. With Mnemoverse, one API key gives the same memory to Claude Code, Cursor, VS Code, Windsurf, ChatGPT, Python, and REST — write in one tool, recall in another.
Yes — major platforms now offer cross-session memory. But those features mainly store user preferences and basic continuity, not consolidated, structured knowledge that learns from outcomes or represents relationships between facts.
Give your agents memory
One API key, the same memory across every AI tool. Free tier is 1,000 queries a day and 10,000 memories — no credit card.