Memory is what turns a clever agent into a useful long-term collaborator — the difference between a tool that re-asks what you told it yesterday and one that picks up where you left off. But "memory for AI agents" now spans very different products: open-source memory layers, knowledge-graph engines, stateful agent frameworks, and closed per-account features. This guide compares the main options — Mem0, Zep, Letta (formerly MemGPT), Supermemory, OpenAI's built-in memory, and Kit for AI — so you can pick by what you're actually building. New to the concept? Start with the complete guide to persistent memory for AI.
What "agent memory" actually has to do
Before comparing tools, it helps to name the job. Good agent memory does four things: it stores durable facts (decisions, preferences, gotchas) outside the model; it recalls them by meaning, not keywords, so the right memory surfaces even when no word matches; it deduplicates, so saving a similar fact twice doesn't create noise; and it's reachable from every tool you use, not locked inside one app. A related-but-different job — reading your documents — is RAG, and serious agents need both, ideally in one retrieval layer (more on that in agent memory vs RAG).
Memory for AI agents at a glance
| Tool | Core approach | Reads your documents too? | Best for |
|---|---|---|---|
| Kit for AI | Memory (remember/recall) + document knowledge bases with hybrid retrieval, over one API + MCP | Yes — ingestion + cited search built in | Agents that must remember AND read your content |
| Mem0 | Open-source memory layer: store and search memories | No — memory only | Bolting a memory store onto a custom agent |
| Zep | Memory as a temporal knowledge graph of facts over time | Focused on memory | Reasoning about how facts change over time |
| Letta (MemGPT) | Stateful agent framework with self-editing memory blocks | Via tools you add | Building agents around a managed memory model |
| Supermemory | A memory / "second brain" API for apps | Primarily memory | A drop-in memory API for a product |
| OpenAI memory | Built into ChatGPT / Assistants, per account | No | ChatGPT users — not portable across tools |
How to choose
- Need memory AND document retrieval in one place? A knowledge-base-first tool (Kit for AI) saves you stitching a memory API to a separate RAG stack.
- Building a custom agent and just want a memory store you control? An open-source memory layer like Mem0 is a natural fit.
- Your questions are about relationships and how facts evolve over time? A temporal knowledge graph (Zep) is worth a serious look.
- You want the agent framework itself to manage memory? Letta (MemGPT) is a framework, not just an API.
- You only need memory inside ChatGPT? OpenAI's built-in memory works — but it can't follow you to Cursor, Claude Code, or your own app.
Two of these overlap with what we've written before: read our Mem0 alternative and Zep alternative takes for the deeper trade-offs.
Where Kit for AI fits
Kit for AI is the pragmatic pick when your agent has to both remember and read. remember/recall give you deduplicated, semantic memory; the same account ingests files, URLs, and scans into knowledge bases with hybrid retrieval and citations; and everything is reachable over one REST API and one MCP server, with per-end-user namespaces for multi-tenant apps. That's the memory layer pattern in full — memory and documents behind a single integration, instead of a memory API on one side and a RAG stack on the other.
Head-to-head comparisons
Comparing two specifically? We have focused write-ups: Mem0 vs Zep, Mem0 vs Supermemory, Zep vs Supermemory, Letta vs Mem0, Mem0 vs LangMem, and Mem0 vs OpenAI memory.
FAQ
What is the best memory for AI agents?
There's no single winner — it depends on the job. If your agent must remember decisions and read your documents, a knowledge-base-first tool like Kit for AI covers both in one API. If you just want a memory store for a custom agent, an open-source layer like Mem0 fits. If you reason over evolving relationships, a temporal graph like Zep is strong.
Do AI agents need memory and RAG?
Usually both. RAG retrieves what's written in your documents; memory persists what happened — decisions, preferences, corrections that exist in no document. An agent with only RAG re-litigates every decision; one with only memory can't read your files. The least-friction setups put both behind one retrieval layer.
Is OpenAI's memory enough?
For ChatGPT users, it's convenient. But it's per-account and closed: it can't be shared with Cursor, Claude Code, or your own product, and you can't build features on it. A memory API or MCP server gives you portable memory across every tool and app.
Give your agents memory that reads your documents too — Start free and connect over MCP or the REST API.