Mem0 vs Zep (Graphiti): AI Agent Memory Compared (2026)

Mem0 vs Zep (Graphiti): AI Agent Memory Compared (2026)

Mem0 vs Zep: AI Agent Memory Compared (2026)

If you're building AI agents that need persistent memory, Mem0 and Zep are two of the most established options in the space — and they represent genuinely different philosophies about how agent memory should work. Mem0 combines a vector store with an optional knowledge graph. Zep builds everything around Graphiti, a temporal knowledge graph engine where time is a first-class dimension.

Both are production-ready. Both have large communities. Neither is universally better. This guide breaks down the architectural trade-offs, retrieval quality, pricing, and self-hosting reality so you can pick the right one for your use case.

Mem0 vs Zep: Quick Comparison

Mem0Zep / Graphiti
GitHub Stars~48K~24K
LicenseApache 2.0Graphiti open source; Zep CE deprecated
Backed byY Combinator ($24M Series A)Zep Inc.
ArchitectureVector DB + Knowledge Graph (dual-store)Temporal knowledge graph (Graphiti)
Graph SupportPro tier only ($249/mo)Core to all tiers
Temporal ModelingNo native temporal modelFirst-class validity windows on all edges
Benchmark (LongMemEval)49.0% (independent eval)63.8% (GPT-4o)
SDKsPython, JavaScriptPython, TypeScript, Go
Self-HostingFully self-hostable (Apache 2.0)Via raw Graphiti only (manage Neo4j yourself)
Managed CloudYesYes (Zep Cloud)
ComplianceSOC 2, HIPAASOC 2 Type 2, HIPAA (cloud)
Free Tier10K memories1K credits
Paid Plans$19/mo Standard, $249/mo Pro$25/mo Flex (20K credits), Enterprise custom

Agent Memory Architecture: Mem0 vs Zep

Mem0: Vector + Knowledge Graph

Mem0 uses a dual-store architecture: a vector database handles semantic search, and a knowledge graph captures entity relationships. When you add a memory, Mem0 embeds it into the vector store and extracts entities and relationships for the graph layer.

The important caveat: graph features are gated behind the Pro tier at $249/month. On the free and $19/month Standard plans, you only get vector-based semantic search. That means your agent can find memories that are semantically similar to a query, but it can't traverse entity relationships or answer multi-hop questions. For simple personalization — user preferences, past interaction context — semantic search works well. For anything involving connected entities or structured reasoning, you need Pro.

On Pro, the graph layer adds genuine value. You can trace relationships between entities, answer questions that require connecting multiple memories, and build a richer model of what your agent knows. The jump from $19 to $249 is significant, though, and many teams find themselves in a frustrating middle ground where they need some graph capability but can't justify the full Pro cost.

Zep: Temporal Knowledge Graph (Graphiti)

Zep takes a graph-first approach. Its engine, Graphiti, builds a temporal knowledge graph where every fact carries explicit time metadata. When your agent records an episode — a conversation, event, or observation — Graphiti decomposes it into entities (nodes), relationships (edges), and temporal attributes. Every edge gets a validity window: when the fact became true, when it was superseded, and the confidence level of the assessment.

The underlying graph database (Neo4j, FalkorDB, or Kuzu) handles traversal. Zep Cloud abstracts the infrastructure. If you self-host via Graphiti directly, you manage the graph database yourself — which is a meaningful operational commitment.

As the survey paper "Memory in the Age of AI Agents" documents, temporal knowledge graphs and dual-store architectures represent two of the primary paradigms emerging in agent memory systems. Mem0 bets on breadth (vector + optional graph). Zep bets on depth (graph-native with time as a core dimension). Both represent fundamentally different approaches from traditional RAG architectures, and both bets have real trade-offs.

Retrieval Quality: Mem0 vs Zep Benchmarks

An independent evaluation using the LongMemEval benchmark — which tests long-term memory retrieval across temporal, multi-hop, and knowledge-update query types — measured Mem0 at 49.0% and Zep at 63.8% (using GPT-4o).

The 15-point gap reflects a real architectural difference. Zep's graph-native structure excels at queries that require traversing relationships and reasoning about time. Mem0's vector-only retrieval on Standard tiers struggles with multi-hop queries and anything requiring structural reasoning about entity connections.

Where Mem0 holds its own: straightforward semantic retrieval. "What are the user's notification preferences?" or "Summarize past interactions with this customer" — these are bread-and-butter personalization queries where vector similarity is the right tool. Mem0's semantic search is mature and reliable for these cases.

Where Zep pulls ahead: queries that require structural reasoning. "How has the customer's sentiment changed over the past quarter?" or "What decisions were made about the API after the security review?" — these require graph traversal, and that's Zep's home turf.

Neither system runs more than one or two retrieval strategies simultaneously. Mem0 relies on semantic search (plus graph on Pro). Zep relies on graph traversal with semantic augmentation. Both can be outpaced by queries that don't align with their primary retrieval path.

Temporal Reasoning: Zep's Strongest Advantage

This is Zep's clearest advantage. Every edge in Zep's knowledge graph carries explicit temporal metadata: valid_from, valid_to, and invalid_at markers. This lets Zep answer questions that most memory systems struggle with:

  • "What was the customer's address before they moved?"
  • "When did the team switch from Slack to Teams?"
  • "What was the project budget before the Q2 revision?"

These require understanding that facts have lifespans — that something was true during a specific window and then stopped being true. Zep models these transitions natively.

Mem0 has no native temporal model. Memories are timestamped at creation, but there's no concept of fact validity windows or temporal supersession. You can filter by creation date, but you can't ask "What was true on this date?" in the way Zep supports. If your use case involves compliance workflows, audit trails, or tracking how entities evolve over time, Zep's temporal depth is hard to replicate with Mem0's architecture.

Self-Hosting Agent Memory: Mem0 vs Zep

Mem0: Fully Self-Hostable

Mem0 is Apache 2.0 licensed and fully self-hostable. You can run the entire stack — vector store, graph layer, API — on your own infrastructure. The community is large (~48K GitHub stars), documentation is solid, and the self-hosted experience is well-supported. For teams with strict data residency requirements or those who want to avoid vendor lock-in, Mem0's open-source story is strong.

Zep: Community Edition Deprecated

Zep's self-hosting story is more complicated. The Zep Community Edition has been deprecated. If you want to self-host today, your path is to use Graphiti directly — which means standing up and managing a Neo4j (or FalkorDB/Kuzu) instance yourself, handling schema migrations, and operating the graph database in production. That's a real operational burden.

Zep Cloud abstracts all of this away, and for many teams that's the right answer. But if self-hosting is a hard requirement, Mem0 has a clear advantage. Graphiti is open source and capable, but "run Graphiti + manage Neo4j" is a different proposition than "run Mem0 with Docker."

For a broader view of the self-hosting landscape, see our Mem0 alternatives and Zep alternatives guides.

Agent Memory Pricing: Mem0 vs Zep

Mem0

TierPriceWhat You Get
Free$010K memories, semantic search only
Standard$19/moHigher limits, semantic search only
Pro$249/moKnowledge graph, priority support, SOC 2/HIPAA

The key pricing tension: graph features are Pro-only. If you need entity relationships and structured reasoning, you're paying $249/month — a 13x jump from Standard.

Zep

TierPriceWhat You Get
Free$01K credits
Flex$25/mo20K credits, full feature access
EnterpriseCustomCustom limits, dedicated support, SLAs

Zep's credit-based model means all features are available at every tier — temporal modeling, graph traversal, the full Graphiti engine. The constraint is volume, not capability. For teams that need graph features but have moderate volume, Zep's $25/month Flex tier undercuts Mem0's $249/month Pro significantly.

When to Choose Mem0

Mem0 is the stronger choice when:

  • Personalization is your primary use case. User preferences, past interactions, behavioral patterns — Mem0's semantic search handles these well without needing graph features.
  • Community and ecosystem matter. With ~48K GitHub stars and YC backing, Mem0 has the largest community, the most integrations, and the most third-party resources of any agent memory framework.
  • Self-hosting is a hard requirement. Mem0's Apache 2.0 license and self-hosted experience are the most mature in the category.
  • You need SOC 2/HIPAA on managed cloud. Mem0's compliance certifications are available on Pro.
  • Budget allows Pro if you need graph. If $249/month is reasonable and you want a dual-store approach, Mem0 Pro is a solid, proven option.

When to Choose Zep

Zep is the stronger choice when:

  • Temporal reasoning is core to your use case. Compliance tracking, audit trails, evolving customer relationships, anything where "What was true at time X?" is a frequent query — Zep's temporal modeling is best-in-class.
  • You want full features at a lower price point. Zep's $25/month Flex tier includes everything — temporal graph, entity resolution, full Graphiti engine. No feature gating.
  • Managed cloud with compliance works for you. Zep Cloud handles Neo4j operations, offers SOC 2 Type 2 and HIPAA, and eliminates the graph database overhead.
  • Your queries are structurally complex. Multi-hop entity traversal, temporal filtering, relationship-based reasoning — these are Zep's architectural strengths.

Worth Considering: Hindsight

If neither Mem0 nor Zep fully fits your requirements, Hindsight is worth evaluating. Both Mem0 and Zep limit retrieval to one or two strategies — semantic search for Mem0 (plus graph on Pro), graph traversal with semantic augmentation for Zep. Hindsight takes a different approach: four parallel retrieval strategies (semantic, BM25 keyword, graph traversal, and temporal reasoning) run simultaneously on every query, with a cross-encoder reranker merging results into a single scored list.

The benchmark difference is substantial. Hindsight scores 91.4% on LongMemEval, compared to Mem0's 49.0% and Zep's 63.8%. The gap is widest on query types that require combining multiple retrieval modalities — temporal + semantic, entity + keyword — where a single-strategy system has to guess which path to take, and Hindsight runs all of them. For a detailed breakdown, see Hindsight vs Mem0 and Hindsight vs Zep.

On the infrastructure side, Hindsight uses embedded PostgreSQL — no external graph database or vector store to manage. All features (graph, temporal, BM25, semantic) are available at every tier, with no Pro-gating. It ships Python, TypeScript, and Go SDKs with MCP-first integration.

Verdict: Mem0 vs Zep for Agent Memory

Mem0 and Zep are both legitimate, production-ready agent memory systems. The choice comes down to what kind of memory your agent needs.

Choose Mem0 if personalization is your core use case, you value the largest open-source community, and you either don't need graph features or can budget for Pro at $249/month. Mem0's self-hosting story is the best in the category.

Choose Zep if temporal reasoning is central to your workload — compliance, audit trails, evolving entity relationships. Zep's temporal knowledge graph is genuinely best-in-class, and the $25/month Flex tier gives you full access without feature gating.

Both systems have real limitations. Mem0's graph-gating creates an awkward pricing cliff. Zep's deprecated Community Edition makes self-hosting harder than it should be. Neither runs more than two retrieval strategies, which leaves both vulnerable to query types that fall outside their primary retrieval path. Evaluate against your actual query patterns, not just feature lists.

Further reading: