Side by side
Competitor claims last verified 1 November 2026| Dimension | Verified facts | Agent memory |
|---|---|---|
| Purpose | Hold your company's own approved facts: prices, policies, product details | Remember users, preferences and past sessions so an agent has context |
| Where it comes from | Written and verified by your team. Agents can propose facts for a person to review | Extracted from conversations and interactions, usually by an LLM |
| Who verifies | A named person verifies each fact | Usually no one. Memory is meant to capture what happened, not to approve it |
| When something changes | A new version supersedes the last. The old one is kept for history, not served as current | A memory is added, updated or marked invalid, decided by the system from the data it sees |
| Permissions | Roles and fact permissions across the whole company | Scoped to a user, an agent, a session or an account |
| How agents access it | Remote MCP server (OAuth or an API key), REST API with an OpenAPI spec, and an open Agent Skill. Plain JSON from any language, no SDK needed | APIs, SDKs and MCP servers from memory providers such as Mem0 and Zep |
| Where it fits | Under every conversation, app and page: the record they defer to | Inside the conversation: who this user is and what was said before |
When agent memory is the right tool
- An agent should remember each user: their preferences, their history and what they asked last time
- Conversations should pick up across sessions without anyone curating anything
- You want context about each customer or account that builds itself as data arrives
When you need verified facts
- Agents state your prices, policies or product details to customers, and the answer has to be the approved one
- You need to show who verified a fact, when, and what it said before
- Many agents, apps and your website should read the same facts, and one change should reach all of them
- Facts an agent proposes should wait for a person before anything treats them as true
The deeper comparison
Memory recalls what was said. NOAN holds what was decided.
Memory is built from the conversation. A user mentions they prefer email, an agent learns which report a manager asks for on Mondays, a customer explains their setup. Capturing that without anyone curating it is the whole point, and memory layers such as Mem0 and Zep are good at it.
Your price list is a different kind of thing. Nobody wants the Team plan's price inferred from the last call where someone mentioned it. It should be decided once, by the person who owns it, and every agent should read that decision until it changes. That is a fact, and facts need a different home from memory.
Memory, retrieval and facts compared
Retrieval, the R in RAG, is the third option teams reach for. It indexes your documents and returns the passages closest to a question, which is the right tool when the answer could be anywhere in thousands of pages.
Each one answers a different question. Memory answers what this user said and wants. Retrieval answers what your documents say. A fact layer answers what your company has decided is true right now.
Memory and retrieval both return what they find, including stale or conflicting versions. A fact layer returns the current approved version, and keeps the older ones for history without serving them as current.
What goes wrong when memory is your source of truth
Nobody approved it. Memory records what was said or inferred. A source of truth for AI agents needs someone to stand behind each answer, and a way to see who that was.
It changes from the data, not from a decision. Memory systems add, update or invalidate entries based on what they see next. That is right for context about a user, and wrong for your refund policy, which should change only when your team changes it.
It belongs to one user or one agent. Memory is scoped to a person, a session or an account. Your business facts belong to the whole company, and every agent, app and page should get the same answer.
Using memory and verified facts together
Most teams running agents in production will want both. Memory for the conversation: who the user is, what they asked before, what they prefer. NOAN for the approved facts: what the product costs, what the policy says, what the company offers today.
The rule that makes it work is one line in the system prompt: when memory and NOAN disagree about the business, NOAN wins. Memory stays useful for context, and nothing a user said on a call can quietly become your new price.
Both connect over MCP, so the same agent in Claude, ChatGPT, Cursor or your own code can read a memory server and NOAN's MCP server for company knowledge side by side.
Common questions
Is agent memory a source of truth?
Not for your business facts. Memory records what was said and what an LLM inferred from it, usually with no one approving it. A source of truth needs one current, approved answer with a named person behind it. Use memory for context about users and a fact layer such as NOAN for the facts agents state.
What's the difference between agent memory, RAG and a fact layer?
Memory holds what was said, retrieval holds what was written, and a fact layer holds what was decided. Memory personalizes a conversation, RAG searches large document sets, and a fact layer like NOAN serves your company's own approved facts, versioned and verified, to every agent over MCP or REST.
How do I give my AI agent verified facts about my company?
Put your company's own facts in a fact layer and connect the agent to it. In NOAN, your team verifies each fact and every version is kept. Agents read the current version over the remote MCP server at https://mcp.getnoan.com/mcp or the REST API at https://api.getnoan.com/v1.
Do I need both agent memory and a fact layer?
If your agent talks to the same people over time and also states facts about your business, yes. Memory carries the conversation, and the fact layer carries the approved answers. Tell the agent that the fact layer wins when the two disagree about the business.
Sources: Mem0: how it works · Zep: facts · NOAN API spec · This page as Markdown · All comparisons
Next step
Connect your agent to verified facts
A remote MCP server for your company's own facts. Sign in over OAuth or send a NOAN API key. Install steps for Claude, ChatGPT, Cursor and Codex.
Plain REST with a bearer key, JSON in and out, from any language. Every endpoint is in the OpenAPI spec.
Or tell any agent: Read https://www.getnoan.com/setup.md and follow it.
See the fact layer with your facts in it
Every plan includes the API and MCP, and paid plans add unlimited keys. Start with one verified fact and build from there.