Mem0 vs Zep vs Letta vs Cognee: AI Agent Memory Compared (2026)

Every team that ships an AI agent eventually hits the same wall: the agent forgets. A customer tells your support bot their order number in message three, and by message nine it asks again. The fix is a memory layer — a system that decides what to store, how to retrieve it, and when old facts stop being true. By mid-2026 this stopped being a research curiosity and became a buying decision, with four serious contenders fighting for the slot in your stack: Mem0, Zep, Letta, and Cognee.

I run six AI-powered products in production — SmartExam AI Generator, DiabeCheck Food Scanner, BizChat Revenue Assistant, DocSumm AI Summarizer, ServiceBot AI Helpdesk, and ContentForge AI Studio. Three of them (BizChat, ServiceBot, and SmartExam's tutoring mode) need persistent memory across sessions, and I've had to make this exact decision with real money on the line. This article is the comparison I wish existed when I started: benchmark numbers, pricing math, and the architectural tradeoffs that actually bite you in production.

What an "agent memory layer" actually does

Before the comparison, it helps to be precise about the job. A memory layer sits between your conversation history and your LLM context window, and it does three things:

• Extraction — deciding which facts from a raw conversation are worth keeping. "The user's name is Maria and she's allergic to penicillin" is worth storing; "thanks, that's helpful" is not.
• Retrieval — pulling the right subset of stored facts back into context at the right moment, without flooding the window or blowing your token budget.
• Conflict resolution — handling the fact that truth changes over time. A user who lived in New York last year now lives in London. A naive store keeps both and confuses the agent.

The four tools approach these three jobs from genuinely different angles, and that's why there's no single winner. Let me take them one at a time.

Mem0 — the default for most teams

Mem0 is a passive memory layer. You pass it a conversation, its extraction pipeline decides what facts to store across user, session, and agent scopes, and you query it later. It combines vector, graph, and key-value storage under one API, and it has the largest community of the four — over 47,000 GitHub stars as of mid-2026.

The benchmark story is where Mem0 gets interesting. On the LOCOMO benchmark (1,540 questions spanning single-hop, multi-hop, open-domain, and temporal recall), the base Mem0 scores 66.9% accuracy with a 0.71s median latency and roughly 1,800 tokens per conversation. The graph-enhanced variant, Mem0g, pushes that to 68.4%. More striking: Mem0's 2026 token-efficient algorithm reports 92.5 on LoCoMo and 94.4 on LongMemEval while averaging under 7,000 tokens per retrieval call — a claimed 91% drop in p95 latency and a 90% drop in token consumption versus naive full-context approaches.

That token number matters more than the accuracy headline. When I integrated a memory layer into BizChat (our revenue-assistant product), the dominant cost wasn't the storage — it was the tokens we paid to stuff retrieved memory into every single LLM call. At ~1,800 tokens per conversation versus a competitor pushing six figures, the monthly bill difference was not subtle. I'll come back to that.

Pricing: Mem0 has a genuinely usable free tier at 10,000 requests/month, a Starter plan at $19/month, and a Pro plan at $249/month that adds graph memory. Self-hosting is available if you'd rather run it on your own Postgres + vector store.

My take: Mem0 is the right default. If you don't have a specific reason to choose something else, start here. The free tier is large enough to prototype seriously, the multi-store flexibility means you won't outgrow it quickly, and the community size means your weird edge-case question has probably already been answered on GitHub.

Zep — when "when" matters

Zep is built around a temporal knowledge graph. Instead of storing facts as timestamped snapshots, it stores fact validity windows — each fact knows when it became true and when it stopped being true. This is the cleanest answer to the conflict-resolution problem of the four tools.

On LongMemEval using GPT-4o, Zep scores 63.8% versus Mem0's 49.0% — a 15-point gap driven almost entirely by that temporal graph. If your agent reasons about sequences of events ("what medication was the patient on before the switch?"), Zep's model fits the problem in a way the others don't.

But there's a real cost, and it's the reason I didn't pick Zep for ServiceBot. The Mem0 research paper measured Zep's memory footprint at over 600,000 tokens per conversation versus 1,764 for Mem0 — and noted that immediate post-ingestion retrieval often failed, with correct answers only appearing hours later as the graph finished processing. Zep's reported p50 total latency of 1.292s is fine on its own, but the ingestion lag means Zep is not the tool for an agent that needs to recall something the user said 30 seconds ago. It's built for durable, slowly-accumulated knowledge, not hot working memory.

Pricing: Zep runs on a credit system. The Flex plan starts at $25/month for 20,000 credits, scaling to Flex Plus at $475/month for 300,000 credits. Budget carefully — credit-based pricing makes it harder to predict your monthly cost than Mem0's flat tiers.

My take: Choose Zep when temporal reasoning is the core of your product — compliance audit trails, medical history, anything where "what was true when" is a first-class question. Don't choose it for real-time chat where the user expects the agent to remember something instantly.

Letta — memory as part of the agent runtime

Letta (formerly MemGPT) takes a different philosophical stance: memory shouldn't be a passive layer you query, it should be part of the agent itself. Letta treats memory like an operating system — the main context is RAM, archival memory is disk, and the agent autonomously manages its own memory allocation, deciding what to page in and out.

This is the most powerful and the most opinionated of the four. In third-party testing, Letta benchmarks around 83.2%, the highest raw number in this comparison. The catch is that you're not bolting memory onto an existing agent — you're building your agent inside Letta's stateful runtime. If you already have an agent built on LangGraph or the OpenAI Agents SDK, adopting Letta means rearchitecting, not integrating.

Pricing: Letta's API charges $0.00015 per second of tool execution, with self-hosted deployments running $5–10/month on basic managed infrastructure. Enterprise pricing is deliberately opaque and requires a sales conversation.

My take: Letta is the right call when you're building a long-running autonomous agent from scratch and you want memory management to be a native capability rather than an add-on. For retrofitting memory into an agent you've already shipped, the switching cost is usually too high to justify the accuracy gain.

Cognee — the knowledge-graph-first pipeline

Cognee is the youngest of the four and the most developer-infrastructure-flavored. Rather than positioning itself as a managed memory service, Cognee is an open-source "ECL" (Extract, Cognify, Load) pipeline that turns your documents and conversations into a queryable knowledge graph plus vector store. It's closer in spirit to a RAG framework that happens to do memory than to a drop-in API like Mem0.

The appeal is control: Cognee runs on your own infrastructure, integrates with the graph and vector databases you already operate, and gives you direct access to the graph it builds rather than hiding it behind an abstraction. The cost is operational — you own the deployment, the tuning, and the scaling. There's no generous managed free tier doing the heavy lifting for you.

My take: Cognee is for teams who already run their own data infrastructure and want memory to be a transparent, ownable part of it rather than a vendor dependency. If your team's instinct is "we'd rather self-host and understand every layer," Cognee rewards that instinct. If your instinct is "give me an API and let me ship," it will feel like more rope than you wanted.

Head-to-head comparison

How I actually chose — a worked example

For ServiceBot, our AI helpdesk product, the requirement was simple to state and annoying to satisfy: remember a customer's prior tickets and stated preferences across sessions, return that context fast enough that the chat doesn't stall, and don't let the token cost per conversation balloon as the customer's history grows.

Zep was out immediately on the ingestion-lag problem — a helpdesk agent that can't recall what the user said earlier in the same session is worse than no memory at all. Letta was out because ServiceBot was already built; rearchitecting it into Letta's runtime would have been a multi-week project to recover an accuracy gap that didn't matter for short support conversations. Cognee was tempting because we run our own Postgres, but the operational overhead of owning the graph pipeline wasn't worth it for a feature that needed to ship that quarter.

We went with Mem0, self-hosted against our existing Postgres + pgvector setup. The ~1,800-tokens-per-conversation figure is the reason. Across a helpdesk doing thousands of conversations a day, the difference between 1,800 tokens and a six-figure token footprint per conversation is the difference between a memory feature that pays for itself and one that quietly eats your margin. That's the lesson I'd hand to anyone making this call: benchmark accuracy is the headline, but token economics is what shows up on the invoice.

Frequently asked questions

Do I even need a dedicated memory layer, or can I just stuff history into the context window?

For a short, single-session task, just use the context window. You need a memory layer the moment you have (a) conversations that span multiple sessions, (b) histories long enough that full context becomes expensive, or (c) facts that need to survive past a single chat. Below that threshold, a memory layer is over-engineering.

Which one has the lowest total cost of ownership?

For most teams, Mem0's free and $19 tiers win on raw price, but the real TCO driver is token consumption per call, where Mem0's ~1,800 tokens is dramatically cheaper to run than a heavy graph store. Self-hosted Letta or Cognee can be cheaper at very large scale if you already have the infrastructure and the team to operate it.

Can I switch later if I pick wrong?

Partially. Migrating stored memories between systems is painful because each tool models facts differently — a temporal graph doesn't map cleanly onto a key-value store. The cheaper escape hatch is to keep your raw conversation logs in your own database regardless of which memory tool you use, so you can always re-ingest from source if you switch. I do this for all three of our memory-backed products, and it has saved me at least one painful migration.

Is the highest benchmark score the one I should pick?

No. Letta posts the highest raw number (~83.2%) but is the hardest to adopt into an existing agent. The right tool is the one whose architecture matches your problem — temporal for Zep, drop-in for Mem0, native-runtime for Letta, self-hosted for Cognee — not the one with the best leaderboard row.

Bottom line

If you want a recommendation rather than a menu: start with Mem0. It's the best default for the widest range of teams, the free tier lets you prototype seriously, and its token efficiency is the thing that keeps the feature profitable at scale. Reach for Zep when temporal reasoning is genuinely central to your product and you can tolerate ingestion lag. Build on Letta when you're creating a new autonomous agent from scratch and want memory baked into the runtime. Pick Cognee when you'd rather own every layer of the stack than depend on a vendor.

The mistake I see teams make is treating this as a search for the "best" tool. There isn't one. There's a best fit for your latency tolerance, your token budget, your existing architecture, and how much of the stack you want to own. Get clear on those four constraints first, and the choice usually makes itself.