Agent InfrastructureUpdated August 2026

Mem0 vs Zep 2026: Which Agent Memory Layer Should You Use?

Both give your agent memory that survives the session. They disagree about what memory is. Mem0 is a drop-in layer with a free tier and enormous open-source adoption; Zep is a temporal knowledge graph that starts at $125 a month and is engineered around facts that change. Here is the decision rule that separates them.

The Short Answer

Choose Mem0 if…

you are still proving that memory improves your agent. The free tier is real, $19 buys a serious allowance, and 62,000-plus GitHub stars mean the self-hosted escape hatch works. It is the cheapest way to find out whether this problem is worth money.

Choose Zep if…

you already know memory is core, and your agent runs long enough that user facts go stale. Temporal graphs, sub-200ms retrieval, published benchmark results and a SOC 2 / HIPAA / VPC path are what the $125 floor pays for.

The sequencing that costs nothing: build on Mem0's free tier, instrument how often your agent retrieves a fact that has since changed, and only spend on Zep if that number is non-trivial.

Mem0 vs Zep, Side by Side

Dimension	Mem0	Zep	What to know
Entry price	Free (Hobby)	$125/mo (Flex)	The single biggest practical difference. Mem0 lets you ship a side project at zero cost; Zep asks you to commit before you have proof the architecture fits
First serious paid tier	$19/mo (Starter)	$125/mo (Flex)	Mem0's Starter gives 50,000 add requests and 5,000 retrievals. Zep's Flex gives 50,000 credits, 600 requests/minute and 5 projects
Metering unit	Add + retrieval requests, metered separately	Credits, across ingestion and retrieval	Mem0's split is easier to forecast because it maps to operations you already count. Zep's credits require modelling against real traffic
Memory model	Flat facts; graph memory with entity linking at Pro	Temporal context graph from the ground up	Zep records superseded facts as superseded. Mem0 reaches relationship-aware retrieval at $249
Open-source core	Mem0, 62k+ GitHub stars	Graphiti, 20k+ stars in under 12 months	Both are genuine self-host paths. Mem0's is more travelled; Graphiti is younger but grew faster
End users	Unlimited on every tier including free	Unlimited memories, retrieval and users on Flex	Neither charges per end user, which is the right call for consumer-scale apps and unusual enough to be worth stating
Stated latency	Not published as a headline figure	Sub-200ms retrieval	If memory has to sit inside the request path rather than run asynchronously, a published latency budget is a meaningful commitment
Published benchmarks	Not headline-published	LoCoMo and LongMemEval results published	Zep publishes against the two standard long-running-agent memory benchmarks rather than asserting quality
Compliance ceiling	On-prem, audit logs, SSO at Enterprise	SOC 2 Type II, HIPAA BAA, VPC, 1-yr logs at Enterprise	Zep's compliance story is more specific and more documented, which matters if a security review is between you and launch
Support at the entry paid tier	Community only ($19 and free)	Included with Flex; SLA-backed at Enterprise	Mem0's $19 tier buys capacity, not help. Part of what Zep's $125 floor pays for is not being alone with a production memory bug

Plan contents as published on each vendor's pricing page in August 2026 — verify before purchase.

The Architectural Difference That Actually Matters

Most agent memory is a vector store with similarity search on top. You ask for context, you get back the past statements most semantically similar to the current question. That works well until a stored fact stops being true.

Consider a user who told your agent in February that they worked at one company and in July that they had moved to another. Both statements are near-identical in relevance to a question about their employer. A similarity-ranked store can return either, and which one it returns is not something you control. The agent then answers confidently and wrongly, and the user loses trust in the memory feature specifically — which is worse than having had no memory at all.

Zep's temporal context graph is built around this case. Facts are tracked across time, and a superseded fact is recorded as superseded rather than left to compete. Mem0 approaches the adjacent problem — relationships between facts — through graph memory with entity linking at its $249 Pro tier, which is genuinely useful but solves relevance rather than recency.

So the diagnostic question is not “which has better memory”. It is: when your agent gets a memory-backed answer wrong, is it because it recalled something irrelevant, or something out of date? The first points to Mem0 Pro. The second points to Zep.

Which One Fits Your Situation

Pick Mem0 when

•You are shipping your first agent and are not yet certain persistent memory is the right answer — the free Hobby tier answers that question for nothing
•Your budget is under $100/month, where Zep simply has no offering and the comparison ends before it starts
•You run a consumer app with a large user base and light per-user memory, where unlimited end users on the free tier is a structural advantage
•You want the largest possible open-source community as an escape hatch — 62,000-plus stars means the self-hosted failure modes are documented by other people
•Your memory workload is write-heavy and predictable, which is exactly the shape Mem0's ten-to-one add-to-retrieval metering is designed around
•You want to test whether memory improves your agent's output before you argue for budget, and you need something running this week

Pick Zep when

•Your agent runs long enough for user facts to change — employment, plan tier, team, preferences — and surfacing a superseded one would produce a confidently wrong answer
•Memory retrieval has to happen inside the request path, where the sub-200ms budget is the difference between usable and not
•You need SOC 2 Type II, a HIPAA BAA or VPC deployment on a documented path rather than a roadmap promise
•You want vendor claims backed by LoCoMo and LongMemEval numbers rather than by architecture diagrams
•Your usage is spiky, and 30-day credit rollover with auto top-up at 20% saves you from provisioning for peak every month
•You are venture-funded and can access Enterprise terms at emerging-company pricing, which changes the economics substantially

Moving From Mem0 to Zep: What Changes

The common path is not a head-to-head purchase decision. It is a migration: a team builds on Mem0's free tier, memory becomes core to the product, stale-fact errors start appearing in support tickets, and they go looking for a temporal model. Three things change in that move.

The bill arrives before the value does. You go from $0 or $19 to $125 a month on day one, with no free tier to overlap on. Budget for a month of running both while you backfill and compare, rather than assuming a clean cutover.

Your metering intuition stops working. Mem0 counts add and retrieval requests, which map to operations you already log. Zep counts credits across both. You cannot convert one to the other from the pricing pages, so plan a fortnight of production-shaped traffic on Flex before committing annually — the 17% annual discount is not worth locking in an under-provisioned tier.

Custom entity types become the design work. Flex caps you at 10 custom entity and edge types, and shaping the graph to your domain is where the value of a temporal model is actually realised. That is modelling effort Mem0 does not ask of you, and it is the most commonly underestimated part of the switch. Against that, one day of API log retention on Flex is worth knowing about before you need to debug something from last week.

Frequently Asked Questions

What is the single biggest difference between Mem0 and Zep?

The price floor, and it decides most evaluations before architecture does. Mem0 has a working free tier — unlimited end users, 10,000 add requests and 1,000 retrievals a month — and a $19 Starter tier above it. Zep starts at $125 a month on Flex with no free hosted tier at all. That means the two products are not usually competing for the same buyer at the same moment. Mem0 wins the builder who is still deciding whether their agent needs memory. Zep wins the team who already knows it does, has a budget line for it, and is now choosing on architecture. If you find yourself comparing them feature by feature, check first whether $125 a month is a real option for you, because that answers it faster.

Does Mem0's graph memory make Zep's temporal graph redundant?

No, they are solving different halves of the problem. Mem0's graph memory with entity linking, available at the $249 Pro tier, adds relationships between facts — knowing that a company a user mentioned is their employer rather than just a string they said. Zep's temporal context graph adds time: it records that a fact was true and has since been superseded, so the old one stops competing with the new one on similarity score. An entity graph without temporality still happily returns a correct relationship to an out-of-date fact. If your agent's problem is that retrieved memories are irrelevant, entity linking helps. If the problem is that retrieved memories are stale, you want temporality, and that is Zep's core rather than an upgrade tier.

Which is cheaper at production scale?

It depends on shape rather than volume, and the honest answer is that you have to model it. Mem0's Pro tier is $249 a month for 500,000 add requests and 50,000 retrievals. Zep's Flex Plus is $375 a month for 200,000 credits, with additional credits at $25 per 10,000 in both directions. Those units are not directly comparable — a credit is not an add request — so a spreadsheet built from the pricing pages will mislead you. What you can compare is the step structure. Mem0 jumps $19 to $249 with nothing between, which is brutal for anything mid-size. Zep steps $125 to $375, which is smoother. If your usage lands in the middle of the range, Zep's ladder costs less pain even where Mem0's headline number looks lower.

Can I evaluate both for free before choosing?

Partly, and asymmetrically. Mem0's Hobby tier is a genuine hosted evaluation — sign up, wire it in, run production-shaped traffic for a fortnight, read the two counters. Zep has no free hosted tier, so the equivalent is running Graphiti, the open-source temporal graph core, yourself. That proves the architecture fits but not the managed service around it, and it is an engineering project rather than a signup. A reasonable evaluation plan is therefore: build on Mem0's free tier first, instrument how often your agent retrieves a fact that has since changed, and only if that number is non-trivial spend the $125 to test whether Zep's temporal model fixes it. That sequencing costs you nothing and answers the question in the right order.

Should I just self-host and skip both?

Only if you have the infrastructure people and a clear reason. Both companies ship a real open-source core — Mem0 at over 62,000 GitHub stars, Graphiti at over 20,000 in under a year — so self-hosting is not the trap it often is with open-core products. But the free path and the paid path diverge in capability rather than only in operations. Self-hosted Mem0 gets you storage and retrieval, not managed graph memory, Dream consolidation or analytics. Self-hosted Graphiti gets you the temporal graph engine, not the Context Lake, the Context Graph Engine or the sub-200ms operational guarantees around them. Self-host if data residency is non-negotiable or if your volume would put you deep into the top tiers. Do not self-host to save $19 a month.

Is there a case for using both?

Rarely, and it is usually a migration rather than an architecture. The realistic version is that you start on Mem0's free tier while validating, discover that stale facts are your dominant failure mode, and move to Zep once memory is core to the product and the budget exists. Running both simultaneously means maintaining two ingestion paths and reconciling two views of the same user, which is a lot of complexity for a benefit you can usually get from one of them properly configured. The exception is a large organisation with genuinely separate workloads — a high-volume consumer surface with light memory needs, and a long-running internal agent where fact staleness is expensive. There, picking per-workload is defensible.