An honest look at AI agent memory in 2026: memory SDKs you build into your own app, MCP memory servers, the memory built into ChatGPT, Claude and Cursor, and team knowledge bases — and where OwlMem fits.
Facts checked against each vendor's public docs and pricing pages on October 25, 2026. Features and prices change; if we got something wrong, email [email protected] and we'll fix it.
OwlMem is a shared memory layer for the AI tools your team already uses. It is a managed MCP server: Claude, ChatGPT, Claude Code, Codex, Cursor, Gemini and any other MCP client connect to one URL and share the same knowledge base, long-term memory and end-to-end encrypted vault. Knowledge is plain Markdown in git, so every change can be audited and rolled back. Agents can also leave each other notes and hand off tasks, so one AI can pick up where another stopped.
Most other tools solve a different problem:
What OwlMem doesn't do: it is not an SDK you embed in your own code, it doesn't extract memories from chat transcripts automatically or build a knowledge graph, and it can't be self-hosted — it is a managed service.
How each tool handles what matters when several AI agents share memory. “Not published” means we couldn't find it in the vendor's own docs — not that it's missing.
Swipe sideways to see every column.
| Tool | What it is | MCP | Shared across AI apps | Team sharing | How data is stored | Audit and rollback | Self-host / open source | Secrets vault for agents | Extracts memories automatically | Starting price |
|---|---|---|---|---|---|---|---|---|---|---|
| OwlMem | Hosted service for teams: knowledge base, memory and vault | Yes: Hosted MCP server with OAuth sign-in | Yes: Claude, ChatGPT, Cursor, Codex, Gemini, any MCP client | Yes: Organizations, members, one identity per AI | Markdown in git, plus facts recalled by meaning | Yes: Every write is a git commit: diff, revert, export | No: No, managed only (servers in Canada); CLI is MIT | Yes: End-to-end encrypted vault, approval per use | No: No, agents save lessons and facts on purpose | Free for 1 AI agent; from $19/month |
| Mem0 | Open-source memory SDK and hosted platform | Yes: Hosted Mem0 MCP; OpenMemory (local) | Yes: Yes, via its MCP (Claude Code, Cursor, Codex and more) | Yes: Organizations and projects with member roles | Vector store; graph memory on the platform | Partly: Change history per memory; audit logs on Enterprise | Yes: Yes, Apache-2.0 | Not published: Not described in their docs | Yes: Yes, an LLM extracts memories from chats | Free tier; from $19/month |
| Zep | Hosted context layer; Graphiti is open source | Yes: Hosted Context MCP server | Yes: Claude, ChatGPT, Claude Code, Codex, Cursor | Yes: Shared graphs, RBAC and ABAC | Temporal knowledge graph | Partly: Old facts are invalidated, not deleted; console audit log | Partly: Graphiti (Apache-2.0); full Zep via Enterprise BYOC | Not published: Not described in their docs | Yes: Yes, an LLM builds the graph from messages | Free tier; from $125/month |
| Letta | Framework for stateful agents (formerly MemGPT) | Partly: Hosted MCP to create and message Letta agents | Not published: Not documented for other AI apps | Yes: Teams plan; shared memory repositories (cloud) | Git-backed memory files plus a vector archive | Yes: Every memory change is a git commit | Yes: Yes, Apache-2.0 | Partly: Per-agent tool secrets (environment variables) | Yes: Yes, the agent edits its own memory | Free tier; from $20/month |
| LangMem | Open-source memory library for LangGraph | Not published: Not in the LangMem docs | Build it yourself: You build it | Build it yourself: Namespaces you design | Key-value and vector store you choose (e.g. Postgres) | Not published: Not documented for the store | Yes: Yes, MIT | Not published: Not described in their docs | Yes: Yes, a background memory manager | Free (MIT); LangSmith Plus $39/seat/month |
| Supermemory | Memory and RAG API with a hosted MCP server | Yes: Hosted MCP server with OAuth | Yes: Claude, Cursor, Codex, ChatGPT and more | Yes: Spaces with scoped access; team seats | Temporal facts graph with vectors | Partly: Fact versions kept; restore not documented | Yes: Yes, MIT local version | Not published: Not described in their docs | Yes: Yes, extracts facts from chats and documents | Free tier; from $19/month |
| Cognee | Open-source knowledge-graph memory, plus a cloud | Yes: cognee-mcp server | Yes: Claude Code, Cursor, Codex and more, on a shared backend | Yes: Users, tenants, roles, permissions per dataset | Knowledge graph plus vectors | Not published: Not documented | Yes: Yes, Apache-2.0 | Not published: Not described in their docs | Yes: Yes, builds a graph from text and code | Free tier; usage-based, $1 per 1M tokens |
| MCP Memory Server | Reference MCP server that runs locally | Yes: It is an MCP server (stdio) | Partly: Apps on one machine can share one file | No: No built-in users or permissions | Knowledge graph in a local JSONL file | Build it yourself: None built in; version the file yourself | Yes: Local only, MIT | Not published: Not described in their docs | No: No, the model calls tools when prompted | Free |
| Basic Memory | Local Markdown notes over MCP, plus a cloud | Yes: Built-in MCP server | Yes: Claude, Codex, Cursor, VS Code, Gemini | Yes: Teams on the paid cloud | Markdown files plus a SQLite index | Partly: Cloud: file history and snapshots; local: do it yourself | Yes: Yes, AGPL-3.0 | Not published: Not described in their docs | Not published: Notes are written by the AI when asked | Free locally; cloud from $15/seat/month |
| ChatGPT | Memory built into ChatGPT | Partly: MCP client only (connectors) | Not published: No official way to use it in other AI apps | Partly: Personal; shared projects use project-only memory | OpenAI's cloud | Partly: View and delete; no version history published | No: No, hosted by OpenAI | Not published: Not described in their docs | Yes: Yes, from your chats | Included, from the Free plan |
| Claude | Memory and Projects in Claude; CLAUDE.md in Claude Code | Partly: MCP client only (connectors) | Partly: CLAUDE.md is a plain file; app memory stays in Claude | Partly: Shared Projects on Team and Enterprise; memory is personal | Anthropic's cloud; CLAUDE.md files on your disk | Partly: View, edit, export; CLAUDE.md through your own git | No: No, hosted by Anthropic | Not published: Not described in their docs | Partly: Yes in the app; CLAUDE.md is written by hand | Included, from the Free plan |
| Cursor | Rules in the Cursor editor | Partly: MCP client only | Partly: Rule files and AGENTS.md live in your repository | Yes: Team Rules on the Teams plan, plus rules in git | .mdc files in the repository; Team Rules in Cursor's cloud | Partly: Through your own git; audit logs on Enterprise | Not published: Not offered on the pricing page | Not published: Not described in their docs | No: No, rules are hand-written (Memories removed in 2.1) | Included; Team Rules from the Teams plan |
| Windsurf | Rules and memories in the editor (now Devin Desktop) | Partly: MCP client only | Partly: Rules and AGENTS.md in the repository; memories stay local | Partly: Workspace rules through git; memories aren't shared | Local files | Not published: Not documented; rules through your own git | Partly: Enterprise: dedicated or VPC deployment | Not published: Not described in their docs | Yes: Yes, in the legacy Cascade agent | Free plan; Pro $20/month |
| Notion | Team workspace with Notion AI | Yes: Hosted Notion MCP server with OAuth | Yes: Claude Code, Cursor, Codex and other MCP clients | Yes: Workspace and page permissions | Pages in Notion's cloud | Yes: Page history from 7 days (Free) to unlimited (Enterprise) | No: No, hosted by Notion | Not published: Not described in their docs | People write pages; agents follow an instructions page | Free plan; full Notion AI on Business ($20/member) |
| Obsidian | Local Markdown notes app | Not published: No official MCP server; community plugins | Partly: Plain files any tool can read | Partly: Shared notes with Obsidian Sync | Markdown files on your device | Partly: Sync keeps version history (1–12 months) | Partly: Local app, free; not open source | Not published: Not described in their docs | Notes are written by hand | App free; Sync from $4/month (yearly) |
RAG (retrieval-augmented generation) fetches passages from a set of documents when a question comes in. Agent memory keeps what an agent learned — facts, preferences, decisions — across sessions, so it doesn't start from zero every time. A knowledge base holds longer pages that people and agents read again: how a system works, the fix that took an afternoon to find. Context engineering is deciding which of these goes into the model's context, and when.
OwlMem gives agents two of them over MCP: a knowledge base of Markdown pages (kb_search, kb_write) and a memory of short facts recalled by meaning, with an optional expiry date (kb_recall, kb_retain). For multi-agent systems the point is that every agent reads and writes the same place, each under its own identity.
Every tool here is good at something. A quick guide:
Choose Mem0 if you are building your own AI app and want an open-source memory SDK that extracts user preferences and facts from conversations, with a large ecosystem of vector stores and LLMs.
Choose OwlMem if you want the AI tools your team already uses — Claude, ChatGPT, Cursor, Codex — to share one memory without writing code, with readable Markdown history and a vault for credentials.
OwlMem vs Mem0 in detail →Choose Zep if you need an enterprise context layer that merges business data and conversations into a temporal knowledge graph, with RBAC and ABAC governance and SOC 2 and HIPAA options.
Choose OwlMem if you want a lightweight shared memory and knowledge base for your team's AI agents, priced per AI agent and set up in a minute over MCP.
OwlMem vs Zep in detail →Choose Letta if you are building long-running, stateful agents that manage and rewrite their own memory, and you want an open-source framework you can self-host.
Choose OwlMem if you want the agents you already use to share one memory and knowledge base, instead of building new agents on a framework.
OwlMem vs Letta in detail →Choose LangMem if you build agents with LangGraph or LangChain and want a library that keeps memory extraction, storage and prompt optimization in your own code.
Choose OwlMem if you don't want to build or host a memory store, and just want every AI on the team to read and write the same memory over MCP.
Choose Supermemory if you want a memory and RAG API for your own product, with automatic fact extraction, connectors such as Gmail and S3, and an MIT-licensed local version.
Choose OwlMem if you want a team knowledge base in plain Markdown with git history, team rules for every AI, and an end-to-end encrypted vault next to the memory.
Choose Cognee if you want an open-source, self-hosted knowledge-graph memory or GraphRAG pipeline with pluggable graph and vector databases and permissions per dataset.
Choose OwlMem if you want a managed service with nothing to run: one URL, OAuth sign-in and memory shared by all your AI apps.
Choose MCP Memory Server if you are one person trying agent memory locally, or you want a minimal reference to learn from — it's free and keeps everything in one readable file.
Choose OwlMem if several people or machines need the same memory, with sign-in, one identity per AI, history you can roll back and a team plan.
OwlMem vs MCP Memory Server in detail →Choose Basic Memory if you want local-first Markdown notes that you and your AI edit together and open in Obsidian, with an AGPL open-source core and an optional paid cloud.
Choose OwlMem if you want a managed team space where each AI has its own identity, plus memory with expiry, team rules, shared third-party tools and a secrets vault.
Choose ChatGPT if you mostly use ChatGPT on your own and want it to remember your preferences automatically, with nothing to set up.
Choose OwlMem if you also use Claude, Cursor or Codex and want them to share what ChatGPT knows — and share it with your team.
OwlMem vs ChatGPT in detail →Choose Claude if you work mainly in Claude: memory and shared Projects cover the app, and CLAUDE.md files in your repository guide Claude Code.
Choose OwlMem if you want the same memory in Claude, ChatGPT, Cursor and Codex, and a knowledge base that every AI on your team writes to.
OwlMem vs Claude in detail →Choose Cursor if your rules belong next to your code: project rules and AGENTS.md in git, with Team Rules enforced on the Teams plan.
Choose OwlMem if the knowledge isn't tied to one repository or one editor — lessons, decisions and facts that every AI on the team should find.
Choose Windsurf if you work in Windsurf and want rules in your workspace plus memories that stay on your machine.
Choose OwlMem if you want memories shared across machines, people and AI apps instead of kept on one computer.
Choose Notion if people write and organise your team's documents, and you want Notion AI and Notion's own MCP server to read them.
Choose OwlMem if your AI agents write the knowledge themselves — lessons and facts in Markdown, with duplicate checks and expiry — and you also need a vault for their credentials.
Choose Obsidian if you want a free, local, personal Markdown notebook with a huge plugin ecosystem, and you're happy to wire up AI access yourself.
Choose OwlMem if you want hosted, shared memory that your AI agents reach from anywhere over MCP, with team members and permissions.
It is what lets an AI agent keep what it learned between sessions — facts, preferences, decisions, how-tos — instead of starting from zero each time. Long-term memory for LLMs is usually kept outside the model, in a database, a graph or files, and brought into the context when needed. Shared memory goes one step further: several agents read and write the same store.
RAG retrieves passages from a set of documents to answer a question. Memory is written while agents work: it changes over time, and old facts need to be updated or retired. In OwlMem a memory can carry an expiry date, so snapshots such as prices or versions retire on their own.
For some teams. Mem0 is a memory layer developers build into their own apps, with automatic extraction from conversations. OwlMem is a managed MCP server that the AI tools you already use connect to, with a shared Markdown knowledge base, memory and a vault. If you are writing your own AI product, Mem0, Zep or Letta may fit better; if you want Claude, ChatGPT, Cursor and Codex to share memory across a team, OwlMem is built for that.
Their built-in memories aren't shared with each other — each product keeps its own. They can share memory through an MCP server that all of them connect to: add OwlMem in Claude, ChatGPT, Cursor, Claude Code or Codex, sign in once, and they all read and write the same persistent memory. See the setup guide.
An MCP server that gives AI clients tools to store and recall memories. The official reference server keeps a knowledge graph in a local file on one machine. OwlMem is a hosted MCP memory server and MCP knowledge base: one URL, OAuth sign-in, and the same memory for every AI in your organization.
No. OwlMem is a managed service running on servers in Canada. Your knowledge base is still yours: it is plain Markdown in git that you can export at any time or sync to your own GitHub repository. If self-hosting is a hard requirement, open-source options such as Mem0, Letta, Cognee, Graphiti or Basic Memory run on your own infrastructure.
Not in memory, and not in the chat. The OwlMem vault is end-to-end encrypted: items are encrypted on your machine, agents need your approval to use them, and owlmem run hands secrets to a command instead of to the model. None of the other memory tools compared here describe a credential vault for agents in their docs.
Official pages we checked on October 25, 2026:
Free for one AI, forever. Connect Claude, ChatGPT, Cursor or Codex in a minute.