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Mem0 vs Zep — AI Agent Memory: Vector-First vs Temporal Knowledge Graph in 2026

Mem0 and Zep are the two most-installed memory layers for AI agents in 2026, but they make opposite architectural bets. Mem0 is a fully open-source vector-first memory framework with optional graph memory, ideal for conversational agents and broad ecosystem coverage. Zep is a commercial platform built on Graphiti, a temporal knowledge graph where every fact has a validity window — the right choice when your agent must reason about state that changes over time. This comparison covers benchmarks, temporal reasoning, self-hosting, pricing, and ecosystem fit.

analyzed by Raşit Akyol April 20, 2026 updated September 5, 2026

Mem0 review

Verdict

Mem0 captures the top spot by providing a highly adaptable memory layer that dynamically extracts, updates, and recalls user and session context. Its hybrid approach—combining vector similarity with structured graph representations—enables nuanced long-term personalization with minimal latency. Compared to Zep's heavier server requirements, Mem0's developer-friendly API and modular deployment model make integrating persistent agent memory significantly more approachable. Our pick: Mem0.


Quick Comparison

Mem0winner

Pricing
Open-source core (Apache-2.0) with $0 self-hosting. Mem0 Cloud Free includes 10k memories and 1k retrieval calls/month. Starter is $19/mo for 50k memories and 5k retrievals. Pro ($99-$249/mo) unlocks Graph Memory (Mem0ᵍ), memory consolidation, and 500k memories with 50k retrievals. Enterprise provides custom pricing for VPC/on-premise deployment, SOC 2/HIPAA compliance, SAML SSO, and 24/7 SLA.
Pricing Model
Freemium
Platforms
Python, API, Self-hosted, Cloud
Open Source
Yes
Telemetry
Clean
Status
Active
Editorial Pick
—
Last Verified
Sep 6, 2026
Description
Mem0 is an open-source intelligent memory layer for AI agents with 51K+ GitHub stars providing persistent, adaptive memory across sessions. It manages working, short-term, and long-term memory types, enabling personalized AI experiences that improve over time. Features automatic memory extraction from conversations, semantic search over stored memories, multi-format support, and integration with 100+ frameworks. Simple API for adding memory to any LLM-powered application or agent.

Zep

Pricing
Zep provides a managed long-term memory cloud with a free tier (10,000 credits/mo), a Flex plan starting at $104/mo (50,000 credits/mo, billed annually), and custom Enterprise/BYOC pricing with SOC 2 and HIPAA compliance.
Pricing Model
Freemium
Platforms
Cloud SaaS, Python/TS/Go SDKs, MCP server
Open Source
Yes
Telemetry
Clean
Status
Active
Editorial Pick
—
Last Verified
Aug 26, 2026
Description
Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.

What Sets Them Apart

Mem0 and Zep are the two most-installed memory layers for AI agents in 2026, and both promise the same outcome: an agent that remembers what you said last week without re-passing the whole history every turn. Under the hood they make nearly opposite bets. Mem0 is a vector-first memory layer with an optional knowledge graph bolted on, shipped as a fully open-source Apache 2.0 stack. Zep is a commercial platform built around Graphiti, a temporal knowledge graph engine where every fact has a validity window — when it was true, and when it was recorded. The gap between "retrieve what sounds relevant" and "retrieve what is currently true" is the whole comparison.

Mem0 and Zep at a Glance

Mem0 is a memory framework for AI agents that combines a vector store with optional graph memory, shipped as an SDK in Python, Node, and Go, and offered as both a managed cloud and a fully self-hostable open-source stack. The mental model is simple: every message is scored, important facts get extracted and stored, and each subsequent query retrieves the top-k most semantically relevant memories. It integrates cleanly with LangChain, LlamaIndex, and the major vector DBs, and it is the default first choice for teams that want memory to "just work" in a prototype.

Zep is a context engineering platform whose engine is Graphiti, an open-source temporal knowledge graph. Instead of storing memories as embeddings, Zep extracts entities and relationships from conversations, stores them as nodes and edges, and tags each fact with a valid_from / valid_until window. When a user changes a shipping address, Zep marks the old address invalid at a timestamp and only the current address surfaces on subsequent queries. The platform is commercial (Zep Cloud), and the self-hosted Community Edition was deprecated in April 2025, with further feature retirements continuing into 2026.

On a recent LongMemEval benchmark with GPT-4o, an independent run measured Mem0 at 49.0% accuracy and Zep at 63.8% — a material gap on long-horizon, time-sensitive questions. The LOCOMO benchmark that both companies cite has been publicly disputed (Zep originally claimed 84%, Mem0 reran it at 58.44%, Zep counter-claimed 75.14%), so treat any single headline number with healthy skepticism. The directional finding — graph-with-time beats vector-only on temporal reasoning — is well-supported across independent evaluations.

Temporal Reasoning and Retrieval Quality

The scenario that separates these tools is one you will eventually hit in production: the facts your agent remembers change. A customer’s subscription tier, a support ticket status, a user’s preferences, an account owner. With Mem0’s vector-first default, older facts can and do resurface when they are semantically closer to a query than the updated fact — and "my address" is often embedded near any previous address the user mentioned, regardless of recency.

Zep’s Graphiti bakes temporality in at the schema layer. Every fact is a triple with a validity window, and retrieval filters by "as of now" unless you explicitly ask for historical state. That sounds like an infra detail until you ship an agent into a real business process and watch it confidently quote a tier the customer churned from in Q2. For anything touching CRM data, compliance, or evolving user preferences, temporal queries are not a nice-to-have — they are the product.

Mem0 has closed some of this gap with Graph Memory, an optional Neo4j-backed layer that captures entity relationships and adds time dimensions. It is real and production-viable, but it is a bolt-on to a vector-first design rather than the schema the platform was built around. Zep wins on temporal correctness by default; Mem0 wins on retrieval simplicity and on being good enough for the majority of assistant-style use cases where facts do not materially change.

Self-Hosting, Pricing, and Ecosystem Fit

Self-hosting is the sharpest practical split. Mem0 is Apache 2.0 end-to-end, ships with Docker support, and runs on any Postgres+vector-DB combination — you can own the whole stack with zero license questions. Zep’s open-source Community Edition was deprecated in April 2025, and the recommended path in 2026 is Zep Cloud; Graphiti itself is still open source, but self-hosting a full Zep experience now means running Graphiti plus a compatible graph DB (Neo4j, FalkorDB, or Kuzu) and rebuilding the higher-level context engineering yourself.

On pricing Mem0 offers a generous free tier and a transparent per-agent/per-event managed plan; Zep Cloud is a usage-based commercial product sold primarily to teams already confident they need temporal reasoning. Ecosystem-wise Mem0 integrates with almost every agent framework out of the box (LangChain, LlamaIndex, CrewAI, AutoGen), while Zep leans into a tighter, more opinionated SDK plus direct integrations with the frameworks its temporal guarantees matter most for.

The Bottom Line


FAQ

What is the difference between Mem0's vector-first architecture and Zep's Graphiti temporal knowledge graph?

Mem0 is a hierarchical vector memory layer that extracts facts, embeds them with dense models, and retrieves them via hybrid semantic search. Zep (powered by the Graphiti engine) transforms conversations into a temporal bipartite Knowledge Graph, modeling entities as nodes and facts as timestamped edges.

How do they manage temporal updates and contradiction resolution?

When user facts change over time, Mem0 overwrites older records based on similarity and metadata updates. Zep maintains edge validity intervals (valid_from, valid_to); when a contradiction is detected, it invalidates rather than deletes past facts, preserving temporal history.

What are the latency, compute, and retrieval performance trade-offs?

Mem0 delivers sub-50ms query latency via single-pass vector searches. Zep requires higher compute at ingestion for entity extraction but provides superior retrieval precision for multi-hop relational queries.

How do their deployment and infrastructure requirements compare?

Mem0 is a lightweight Python package that can be embedded directly into application processes and stores vectors in external databases (Qdrant, pgvector). Zep is a standalone Go/Python service with asynchronous queue workers requiring PostgreSQL (pgvector) or Neo4j.

Sources & verification

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Verification dates are editorial checks. Routine CMS saves and automatic updatedAt timestamps do not advance them.