Mem0 vs Zep: AI memory compared

Mem0 is a vector-based memory layer: it uses an LLM to extract facts from your conversations, embeds them, and retrieves the relevant ones by semantic similarity. Its core is open source (Apache 2.0) with a managed platform on top. Reach for it when you want a simple, self-hostable fact store you can drop into an agent.

Zep models memory as a temporal knowledge graph: entities, the relationships between them, and, crucially, when each fact was true. That makes point-in-time recall a first-class capability. Reach for it when a user's role, plan, or preference changes over time and you need to reason about what was true at a given moment, not just what is true now.

The axis both share is that they store and retrieve. Mem0 retrieves flat facts; Zep retrieves a graph of facts with validity windows. Neither is wrong, but neither, on its own, makes the memory change as the user does. That is the gap the Large Cognition Model (LCM) is built to close.

A flat vector store answers "what do I know about this?" well, and cheaply. It struggles the moment two facts contradict each other or one supersedes another. There is no structure that says this replaced that, on this date. Mem0 mitigates this with extraction and de-duplication, but the underlying shape is a bag of facts.

A temporal knowledge graph answers a harder question: "what was true, and when?" Zep's edges carry validity windows, so a user who was on the free plan in March and the pro plan in June produces two true-at-the-time facts rather than one overwritten one. That is powerful for audit, personalization, and long-running agents, at the cost of more moving parts to model and maintain.

CognitiveX takes the structure further. Memories live in four tiers: semantic (facts), episodic (events), procedural (how-tos), and foundational (identity). A cognition engine runs on top of them: pattern detection promotes a recurring episode into a stated preference, salience ranks recall so the important memories surface first, and overnight dream consolidation compresses and re-relates the graph while you sleep. The LLM is the last step that renders language; the structure is the product.

Pricing changes often, so treat specifics as direction, not gospel. Check each vendor's current page before you commit. Structurally, though, the three meter differently, and the shape matters more than the sticker price.

• Mem0 offers a free tier and usage-based paid plans, metered on memory operations and retrievals. Self-hosting the open-source core shifts cost to your own infrastructure.
• Zep meters its cloud platform on usage as well (messages processed and graph operations), reflecting the heavier graph build. The Graphiti graph engine underneath is open source.
• CognitiveX charges recall credits by depth and makes storing memories free. A foundational recall costs 1 credit, a standard recall 3, a deep recall 10, so you pay for how hard the system thinks, not for remembering. Plans run free (100 credits/mo, 1k memories) → Awakened $20/mo (500k credits, 1M memories) → Conscious $200/mo (15M credits, unlimited), with metered pay-as-you-go.

• Pick Mem0 for the fastest path to "my agent remembers facts," especially if you want to self-host and keep the dependency surface small. Deeper take: the Mem0 alternative.
• Pick Zep when time is part of the question: auditability, evolving user state, or reasoning over what was true at a past moment. Deeper take: the Zep alternative.
• Pick CognitiveX when storing and retrieving is not the finish line, when you want a memory that consolidates, detects patterns, and gets to know the user across sessions. It plugs in over MCP and keeps the LLM swappable, because in the LCM the memory is the model.

Want the wider field? See the full CognitiveX vs Mem0 vs Zep vs Letta vs Cognee comparison.

What is the difference between Mem0 and Zep?

Mem0 stores extracted facts in a vector index and retrieves them by similarity. Zep stores a temporal knowledge graph (entities, relationships, and when each was true), so it can answer point-in-time questions. Mem0 favors simplicity and self-hosting; Zep favors time- and relationship-aware recall.

Is Mem0 or Zep better for an AI agent?

Use Mem0 for lightweight, drop-in fact recall. Use Zep when the validity window of facts matters and your agent must reason about a user whose state changes over time.

How is CognitiveX different?

Both Mem0 and Zep store and retrieve. CognitiveX adds the step they skip, where the memory consolidates and learns. Recurring episodes become semantic preferences, patterns get detected, recall is ranked by salience, and an overnight dream pass re-relates the whole graph. That is the Large Cognition Model.

Try the memory that learns.

CognitiveX plugs in over MCP, keeps your LLM swappable, and makes storing memories free. Start building, or try it inside iCog, the consumer app built on the LCM.

Start building →Try iCog →