Mem0 alternatives
Checked against Mem0’s own documentation on
Mem0 is the most widely used memory layer for AI agents, and the one most teams try first. A language model reads each exchange as it arrives and files the facts worth keeping.
People look elsewhere when a decision that is right for most products is wrong for theirs. We make one of the alternatives below: WOS never rewrites what you send and never runs a model over it.
- 2.8×less context per query2,484 tokens against Mem0’s ~6,956
- +3.4BEAM-1M, at the same 1M scale67.5% against 64.1%
- 95.2%recall@5 across 70 language pairsMem0 publishes no figure for this
Where WOS comes out ahead. Where it does not is in the table below.
Mem0 and WOS
| Mem0 | WOS | |
|---|---|---|
| What each one has published | ||
| LongMemEval | 94.4% as Mem0 reports it, reader not stated | 95.7% Tablet 2 read by Claude Opus 5; 93.7% read by GPT-5.6-sol |
| BEAM-1M | 64.1% at the same 1M scale | 67.5% Tablet 2 |
| LoCoMo | 92.5% | Not run we publish on LongMemEval-S and BEAM |
| Latency | 148ms p50 search on LoCoMo, from their paper; p95 200ms | ~320ms median engine round trip; 393ms at 2.21 million memories |
| Tokens per query | ~6,956 mean per retrieval call | 2,484 BEAM-1M at 2.2M memories with verify 3 — the run the score above comes from |
| Also published | Per-category LoCoMo breakdowns single hop 94.6, multi-hop 95.4, temporal 82.3 | Latency ~320ms, languages 95.2% engine round trip; recall@5 over 70 language pairs |
| Source | mem0.ai/research | how we benchmark |
| How it is built | ||
| Stored as | Facts a model extracted | Kept, not rewritten |
| A model reads it | Yes, at extraction | Never |
| Organised around | Scope layers, conversation to organisation | One store per end-user |
| Retrieval | Vector, graph on higher tiers | Not published |
| Correcting a fact | Extraction and consolidation passes | supersede, with lineage |
| Licence | Apache-2.0 | Closed source |
| Self-hosting | Yes, a server you run | Case by case |
| Metered by | Requests, split into reads and writes | Tokens, plus a flat fee per request |
- Both columns are self-reported: a placement, not a verdict. Some rows go to Mem0.
- Across languages is a grid of 70 store-language by query-language pairs.
What WOS does
The difference that matters against Mem0 is when the thinking happens. Mem0 does its work on the way in, deciding what is worth keeping. WOS does its work on the way out: nothing is rewritten on the way in, and what is relevant gets decided at query time, against the question actually being asked.
For your AI Won is the part of the product addressed to the model rather than to you, and it carries no charge. Your agent can ask how much of its own memory has been rewritten before it leans on a fact, and the answer is the same size for a hundred memories or a hundred million. the page written for an arriving agent.
- Nothing is rewrittenWhat you send is what comes back. Recall is not capped by what an extraction pass judged durable on the day it arrived.
- No model over the storeNothing reads your users’ exchanges, at write time or read time. That shortens the privacy conversation from a discussion to a sentence.
- One meter, sized to the textInput and output tokens plus a flat fee per request, so a long session and a one-line exchange do not cost the same.
- Repeated queries bill a tenthTurn on caching for a query and each hit inside the window costs 0.1x. Any write to that store invalidates it instantly, so a cached answer can never be stale.
- Your data stays yoursWe do not train on it, look at it, or use it, and one call erases an end-user completely.
- Calls the model makes for itselfWon is the part of the product addressed to your agent rather than to you, and it carries no charge. revisions is the first of them: the model can ask how much of a memory has been rewritten before it leans on it, and the answer is the same size at a hundred memories or a hundred million.
- Multi-hop without a modelEngrams search and then expand around what they found: deep recall, timelines, drafts. No language model runs in that loop, so a question that takes two steps is one call and there is no per-hop bill.
Where Mem0 is the better answer
- A permissive licence, and a self-hosted path that costs only infrastructure
- The largest integration surface in this field, and the largest body of examples and community answers when something breaks at 2am
- Graph memory, if you are using itWOS does not build a graph of any kind.
- Organisational scope layersWOS isolates one store per end-user rather than promoting facts up through tiers.
- What WOS costs you. It is closed source and hosted, so running it on your own hardware is a conversation rather than a download. There is no knowledge graph, so a question that is really about validity intervals is better served elsewhere. And it is the newest name on this page, with a smaller ecosystem than the ones above it.
- What it buys you. Nothing runs a language model over what you stored, so the privacy review is one sentence rather than a discussion. A query comes back in about 320ms, and the context it hands over was measured at 2,484 tokens over 2.2 million memories. Repeated queries bill a tenth. And it behaves the same in every language: recall@5 of 95.2% over 70 store-language by query-language pairs.
Why teams look at something else
| A model reads the conversation on the way in | Extraction runs a language model over your end-users’ raw exchanges before anything is stored, which is the first question a privacy review asks. |
| Recall is capped at write time | Retrieval can only return what the extraction pass judged durable, so the ceiling is set before anyone has asked a question. |
| Billing counts calls, not the text inside them | A one-line exchange and a long session cost the same per request, which suits uniform traffic better than variable traffic. |
| Self-hosting is real, and it is yours to run | A permissive licence is a genuine advantage, and it also means you operate the vector store, the extraction model’s bill and the pager. |
- None of these are defects. Each follows from a decision that is right for somebody.
Bringing memory over
What is in Mem0 comes out as text, which is the form WOS wants. Create one store per end-user first, because stores are explicit and writing to one that does not exist returns a 404 rather than creating it silently. Then push each user’s history through bulk-store. One thing is worth doing differently on the way over: because WOS does not rewrite what you send into an extracted summary of it, you can send the original exchanges instead of the facts that were distilled from them, and let retrieval do the selecting at read time.
The calls are in the documentation: create a store, bulk-store, and the rest of the endpoints. A key takes a few minutes in the console.
Questions
- Is there a Mem0 alternative that does not run a language model over stored conversations?
WOS. Nothing you send is rewritten, and no model reads the store on the way in or the way out. The trade is that WOS is closed source and hosted, which is the opposite of Mem0 on exactly the axis some teams care about most.
- Is Mem0 open source?
Yes. Mem0 is Apache-2.0 and ships a self-hosted server, so the managed service is a convenience rather than a requirement. WOS is the opposite on this axis: closed source, hosted only. If a permissive licence is a hard constraint, that settles the question in Mem0’s favour before anything else gets compared.
- What is the cheapest Mem0 alternative?
Self-hosting, whether that is Mem0 itself or Cognee, where you pay only for infrastructure and embedding calls. Among hosted options it depends on your traffic shape rather than on a rate card, because some meter per request and some per token. WOS meters tokens in and out plus $0.0001 a request, with no seats and no subscription, and a repeated query bills 0.1x on a cache hit.
- Do I have to move everything at once?
No. WOS isolates memory per store, one per end-user, so you can move a cohort and leave the rest where it is. Running both for a while is the normal way to find out whether recall is actually better on your data rather than on a benchmark.
The other options
- Zep Build a temporal knowledge graph. Right for you if your questions are about when something was true, not just whether it is true.
- Letta Let the agent manage its own memory. Right for you if you want the agent to own and edit its own memory, inside their runtime.
- Cognee Run a pipeline over documents into a graph and vectors. Right for you if what you are remembering is a document corpus, and you want to own the pipeline.
- Supermemory Bundle memory with retrieval over connected sources. Right for you if you want one API over connectors, documents and chat memory together.
- Vector DB Build it yourself on a vector database. Right for you if you are prototyping, or you want no vendor in the path and will build the rest.
- Checked against Mem0’s own documentation on September 1, 2026.
- We quote nobody else’s prices, and use only figures each project published itself.
- Out of date or unfair? Tell us and it gets corrected.
You need the same store to work in many languages, and a per-query cost that does not drift as history grows. If that is not your case, one of the projects above will serve you better.
Three calls: create a store, write a memory, recall against it. No subscription, balance starts at $5. Get an API key or read the docs.