"AI memory" in 2026 means at least three different things: developer libraries you wire into your own agents (Mem0, Zep, Letta), consumer memory built into ChatGPT and Claude, and shared company brains that every tool reads from. Most comparison posts blur these together. This one separates them — because the right pick depends entirely on which problem you actually have.
One pattern worth naming up front, because it shapes the whole landscape: almost every memory product is per-user by design. Mem0 and Zep scope memory to a user ID. ChatGPT and Claude remember you, not your company. If what you want is one memory your whole team and all your AI tools share, that's a different category — we'll get to it.
The developer memory layers
These are infrastructure: APIs and libraries for developers building their own agents.
Mem0 — the popular default
Mem0 is a memory layer you bolt onto any agent loop: it extracts facts from conversations with an LLM, resolves conflicts against existing memories, and stores them in a vector store (with an optional graph store on higher tiers). It's open source (Apache-2.0), has SDKs in Python and TypeScript, and integrates with most agent frameworks. Pricing runs from a free hobby tier to $19/mo starter and $249/mo Pro — graph memory is gated to Pro. There's a hosted MCP server plus OpenMemory, their local-first MCP memory for coding tools.
Where it fits: you're a developer adding per-user memory to your own product. Where it doesn't: shared team memory is DIY (you'd fake it with a shared scope ID), and conflict resolution is update-in-place — old facts are overwritten, not kept as history.
Zep — the temporal knowledge graph
Zep is built on Graphiti, their open-source temporal knowledge graph engine. Its defining feature is a bi-temporal fact model: every fact carries validity windows, and contradicted facts are invalidated rather than deleted, so the graph can answer both "what's true now" and "what was true then." It's the strongest temporal model in the developer category. Cloud pricing starts free and steps up to ~$1,250/yr for the Flex tier; the self-hostable community edition was retired, so self-hosting now means running raw Graphiti yourself.
Where it fits: you're building agents where facts change over time and stale answers are costly. Where it doesn't: it's an API for your code, not a product your team uses — and the center of gravity is still per-user memory.
Letta — memory inside an agent runtime
Letta (formerly MemGPT) isn't a memory API at all — it's a full agent runtime where the LLM manages its own memory through tool calls: editable in-context memory blocks, message history, and archival vector storage. It's open source (Apache-2.0) with free and $20/mo tiers. Its shared memory blocks — attachable to multiple agents — are the only first-class cross-agent shared-state primitive among the developer tools.
Where it fits: you're all-in on Letta's runtime for long-running stateful agents. Where it doesn't: you can't easily embed its memory into an existing agent loop the way Mem0 or Zep slot in, and there's no fact-level temporal model.
The consumer memory features
ChatGPT memory got a major upgrade in June 2026 ("Dreaming"): a background process that reads across your conversation history and maintains a self-updating memory state. It's genuinely good — and strictly personal, opaque, and locked in. There's no API, no export of the synthesized state, and no way for your team or your other tools to read it.
Claude memory keeps an editable, per-project memory summary, free for all users since March 2026, with import from other AI tools and export for backup. Same fundamental shape: per-user, per-tool, not shared with teammates or other clients.
Both are worth turning on. Neither is an answer to "how do my agents share what my company knows" — notably, both companies are MCP clients, but neither exposes its memory as an MCP server. Memory flows into these tools, never out.
The shared company brain
This is the category Agently is in, and it's the slot the tools above leave open: memory that belongs to the workspace, not the user.
The Agently Brain is a temporal knowledge graph built continuously from what your company connects — uploaded docs, Slack, Gmail, HubSpot, Linear, GitHub, and 25+ other sources. Facts carry validity windows (current truth vs. superseded history), and everything is shared: every teammate and every agent pointed at the workspace reads the same brain. Through the Agently MCP server, that includes the AI tools you already use — Claude, ChatGPT, Cursor, VS Code — via one URL with workspace-scoped keys or OAuth.
The honest trade-off: it's a product, not a library. If you're building your own app and need embedded per-end-user memory, Mem0 or Zep is the right shape. Agently is for the team that wants its own AI stack to share one memory without building anything.
Head-to-head
| Type | Memory scope | Temporal model | MCP server | Open source | |
|---|---|---|---|---|---|
| Mem0 | Developer API/library | Per-user | Update-in-place | Yes (hosted + local) | Apache-2.0 |
| Zep | Developer API | Per-user (+ group graphs) | Bi-temporal validity | Yes (OSS + cloud) | Engine (Graphiti) |
| Letta | Agent runtime | Per-agent (+ shared blocks) | None | Client only | Apache-2.0 |
| ChatGPT memory | Consumer feature | Per-user | Opaque self-updating | No | No |
| Claude memory | Consumer feature | Per-user | Rewritten summary | No | No |
| Agently Brain | Team product | Shared, workspace-wide | Validity windows | Yes (hosted) | No |
How to choose
Building an app that needs per-user memory? Mem0 for the fastest path; Zep if temporal correctness matters; Letta if you want the whole runtime.
Just want your personal context to follow you between tools? Turn on ChatGPT and Claude memory, and look at OpenMemory for a portable, local-first version.
Want your team and all your AI tools sharing one current picture of the company? That's a company brain, and it's what Agently ships — no pipeline to build, connected to everything over MCP.