If you are building an agent that needs to remember things between sessions, you have probably hit the same wall everyone hits: the model's context window is not memory. It resets. So you start looking for a memory layer, and three names come up often — Zep, Mem0, and AgentPrizm (that's us). This piece is an honest attempt to lay them side by side.
A word on method, because it matters here. Every claim below about Zep or Mem0 was checked against their own official sources — their docs, sites, and GitHub repositories — and linked so you can verify it yourself. Where a fact was not clearly stated on an official page, we left it out rather than guess. Products change, especially pricing, so treat the specifics as "true when we checked, confirm before you buy." We are not neutral about our own product, but we have tried hard to be fair about everyone else's.
The short version
All three give an agent persistent memory you reach over an API. They differ most in how they model memory and how they expect you to deploy.
- Zep models memory as a temporal knowledge graph and targets enterprise scale, with managed-cloud and bring-your-own-cloud options.
- Mem0 is an open-source memory layer with a hosted platform on top, strong framework integrations, and a local-first MCP option called OpenMemory.
- AgentPrizm is a hosted service built around six explicit memory types and governance primitives, reachable by REST or MCP.
Now the detail.
Zep
Zep describes itself as a "context engineering platform" that gives agents enterprise-grade memory (getzep.com). The core idea is a temporal knowledge graph: in Zep's words, memory is "a temporal Context Graph — the unit of agent memory. Nodes are entities, edges are facts/relationships," and the graph "updates dynamically as new data arrives, invalidating outdated facts while preserving history" (Zep docs: concepts). When a new fact contradicts an old one, Zep records when the old fact became invalid on that fact's edge — so the history is kept, not overwritten.
Under the hood Zep builds on Graphiti, its open-source temporal knowledge-graph framework; the GitHub README notes that "each fact includes valid_at and invalid_at dates, allowing agents to understand how relationships, preferences, and context have evolved" (Zep on GitHub). Worth being precise here: Graphiti is open source, but the Zep product is positioned as a managed cloud service. The README states the Community Edition "is no longer supported and has been deprecated" and recommends Zep Cloud, the managed offering (Zep on GitHub). So if "self-host the whole thing" is on your checklist, read their current docs carefully — the open piece and the product are not the same thing.
On the buyer's checklist, Zep's site claims the most enterprise governance of the three. Its homepage lists SOC 2 Type II and HIPAA BAA compliance, three deployment models (managed cloud, cloud with your own keys, and bring-your-own-cloud within your VPC), provenance tracking, retention policies, audit logs, and sub-200ms retrieval "tested up to 100M+ graphs" (getzep.com). It offers SDKs for Python, TypeScript, and Go (pip install zep-cloud, npm install @getzep/zep-cloud, go get github.com/getzep/zep-go/v2) (Zep on GitHub).
If your problem is "lots of evolving facts about lots of entities, with regulated-industry compliance requirements," Zep is clearly built for that. That is a genuine strength, and it shapes the price.
Mem0
Mem0 calls itself "Drop-in memory infrastructure for AI agents and apps," whose pitch is that systems can "continuously learn from past user interactions" instead of leaning on the context window alone (mem0.ai). Its biggest distinguishing trait is that it is open source with a hosted platform on top. The docs split cleanly into "Mem0 Platform" (managed) and "Open Source" (self-hosted for full data control) (docs.mem0.ai), and the homepage points you at the GitHub repo.
For developers, Mem0's strongest card is ecosystem reach. The docs advertise integrations with "LangChain, CrewAI, Vercel AI SDK, and 20+ partner frameworks," a Python SDK (pip install mem0ai), Node.js support, and a REST API reference (docs.mem0.ai). The homepage cites 90,000+ developers and Y Combinator backing (mem0.ai).
Mem0 also ships OpenMemory, an MCP server it describes as "a private, local-first memory layer" that works with "Cursor, VS Code, Claude, and every MCP-compatible coding agent," running on your own infrastructure via Docker (Mem0: Introducing OpenMemory MCP; mem0ai/mem0-mcp on GitHub). If you want a coding-agent memory you run locally with no data leaving your machine, that is a real and specific offering.
On governance, Mem0's homepage lists SOC 2 (Type 1) and HIPAA compliance, BYOK support, and full audit logging — "every read and write logged" (mem0.ai). For a buyer who values the optionality of open source plus a managed tier, Mem0 fits naturally.
AgentPrizm
We will keep this honest, including about where we are smaller.
AgentPrizm is a hosted memory layer. Agents reach it two ways: a REST API under /api/v1/agent/*, and an MCP server that runs over both stdio (local subprocess) and remote HTTP (connect with just a URL and a key, no install). Both transports expose the same tool surface.
What's distinctive is the memory model. Instead of one undifferentiated store, AgentPrizm uses six explicit types — fact, lesson, directive, preference, contact, bookmark — because each one fails differently and so deserves different handling. A standing safety rule (directive) should outrank a stylistic habit (preference) at recall time; a lesson should age and get revisited where a fact stays sticky. Recall is hybrid (semantic plus keyword) and returns confidence scores. Memories carry fact-validity windows and supersedes relations, so a corrected fact replaces the old one with a trail rather than leaving two contradictory "facts" both ranking high. Scoping is done with containers — per-project or per-agent scopes — so a lesson from one project doesn't bleed into another. On governance, every memory operation leaves an audit trail, forgetting is a first-class right-to-forget operation, and the design is GDPR-aligned.
Here is the honest framing. AgentPrizm is newer and smaller than the other two. We do not publish third-party benchmarks. We are not going to claim SOC 2 or HIPAA certification, an SLA, or self-hosting — because at the time of writing we don't offer those, and a comparison article is exactly the wrong place to fudge it. If you are a regulated enterprise that needs a signed BAA today, be clear-eyed about that gap. What we offer is a clean, opinionated memory model with governance built in, at a price that starts at zero.
Side by side
Everything in the competitor columns is from the sources linked above; "see docs" means we couldn't confirm a single clean answer on an official page and would rather you check than trust us paraphrasing.
| | Zep | Mem0 | AgentPrizm |
|---|---|---|---|
| Core model | Temporal knowledge graph (entities + facts) | Self-improving memory layer | Six explicit memory types |
| Hosting | Managed cloud; BYOK; BYOC in your VPC (src) | Hosted platform + open-source self-host (src) | Hosted |
| Open source | Graphiti (the graph engine) is OSS; product is managed (src) | Yes, core is OSS (src) | No |
| API | REST + SDKs (Python/TS/Go) (src) | REST + SDKs (Python/Node) (src) | REST /api/v1/agent/* |
| MCP | See docs | OpenMemory MCP server (src) | Yes — stdio + remote HTTP |
| Scoping | Graph / projects | Projects (src) | Containers |
| Temporal facts | valid_at / invalid_at on edges (src) | See docs | Validity windows + supersedes |
| Compliance claims | SOC 2 Type II, HIPAA BAA (src) | SOC 2 Type 1, HIPAA (src) | GDPR-aligned; audit trails; right-to-forget |
| Free tier | Yes (src) | Yes (src) | Hobby, $0 |
| API on free tier | See pricing | See pricing | Yes — every tier |
On pricing
Pricing moves, so here is the shape, not gospel — confirm current numbers on each site before you commit.
Zep prices on a credit model: a free tier with a small monthly credit allowance, then paid plans that bundle credits with per-block overage, plus custom Enterprise (Zep pricing).
Mem0 prices on request volume — buckets of "add" and "retrieve" requests across a free Hobby tier and several paid tiers, up to custom Enterprise, with a startup program offering free Pro access to qualifying startups (Mem0 pricing).
AgentPrizm uses simple monthly plans (not per-seat): Hobby $0, Builder $29/mo, Scale $249/mo (1M-memory cap), and Enterprise (custom). There's a 14-day trial, and the API is available on every tier including the free one — so you can wire up a real integration before paying anything.
The practical takeaway: if your usage is spiky or hard to predict, look closely at how each vendor meters, because credit and request models behave very differently from flat seats under load.
How to choose
Forget which logo is "best" — match the tool to your constraint.
Choose Zep if you need a temporal knowledge graph at enterprise scale and your compliance bar (SOC 2 Type II, HIPAA BAA) is non-negotiable today. Its model is purpose-built for many evolving facts across many entities, and its deployment options reach into your own VPC.
Choose Mem0 if open source matters to you — for auditability, for avoiding lock-in, or for running memory locally via OpenMemory — and you want deep, ready-made integrations with the agent frameworks you already use.
Choose AgentPrizm if you want an opinionated, typed memory model with governance built in (containers, validity windows, audit trails, right-to-forget), reachable by REST or MCP, with the API open on a genuinely free tier so you can prove it out first. Just go in knowing we're the newer, smaller option and we don't carry the enterprise certifications the others advertise.
The good news is that all three let you start for free. The best way to settle a comparison like this is to point your actual agent at each one for an afternoon. For the AgentPrizm side of that experiment, the docs cover the API, MCP setup, types, and container scoping, and pricing has the current plan limits.
Whatever you pick, the lesson is the same one that started this article: the context window is not memory. Picking a real memory layer is the upgrade. Pick the one whose model matches how your agent actually needs to remember.