Zep vs Mem0: Which AI Memory Layer Should You Choose?

When you start building AI agents that need to remember things across sessions, you quickly hit the same wall.

The context window resets.
The user repeats themselves.
The agent has no idea who it is talking to.

Two tools come up repeatedly in this space:
Zep and Mem0.
Both solve the stateless agent problem.
Both handle long-term memory for LLM applications.

But they come from different architectural philosophies, serve different developer profiles, and make different trade-offs around performance, hosting, and ease of integration.

This comparison breaks down how each actually works, where each fits best, and what you should look for depending on what you are building.

What Mem0 Is

Mem0 is the memory layer for AI agents, designed to make context persist across conversations without stuffing raw chat history back into the prompt.

Its new token-efficient memory algorithm uses single-pass ADD-only extraction: important facts are extracted once, stored as new memories, and never overwritten or deleted by the extraction pipeline. This preserves history while reducing the cost and complexity of memory management. Retrieval then combines semantic similarity, keyword matching, and entity matching to surface the most relevant memories when an agent needs context.

The result is a compact, production-oriented memory system that performs well on long-context benchmarks while using far fewer tokens than full-context approaches. Mem0 reports 91.6 on LoCoMo, 93.4 on LongMemEval, and 64.1 / 48.6 on BEAM 1M / 10M, averaging under 7,000 tokens per retrieval call.

Mem0 is available as a managed platform, open-source Python / Node package, self-hosted stack, agent harness like OpenClaw and Hermes Agent, and a local-first memory server for MCP-compatible clients like Claude Desktop, Cursor, Windsurf, and others.

What Zep Is

Zep is a separate open-source memory store for LLM applications, built by a different team. It centers on conversation-history ingestion: Zep takes in raw turns, runs entity and fact extraction in the background, and makes everything searchable via vector embeddings. Their distinguishing piece is Graphiti, a temporal knowledge graph engine that tracks how facts evolve over time.

Zep ships as Zep CE (community, self-hosted, open-source) and Zep Cloud (managed), with Python and TypeScript SDKs and a REST API.

This is a real product with real engineering behind it. The comparison below is on published numbers, not vibes.

Architecture

Zep's architecture is history-first. Raw conversation turns land in the store, entity extraction runs over them, and the result is a growing archive of structured-plus-unstructured memory. The temporal graph sits on top.

Mem0's architecture is distillation-first. Raw turns are not the unit of storage, extracted facts are. The pipeline is designed so that five turns of chit-chat produce zero new memories, and one turn with a load-bearing fact produces exactly one memory. The conflict detector keeps the store from bloating as the same user keeps talking.

In practice, both systems retrieve compact context at query time. The architectural difference shows up most in how the store grows over months of use, and in how each handles contradictions.

Benchmark Head-to-Head

TL;DR

• On LongMemEval overall accuracy: Mem0 93.4 vs Zep 71.2 (GPT-4o). +22.2 points.

• On LoCoMo overall accuracy: Mem0 91.6 (April 2026) vs Zep 80.32% @ 189ms (their best single-retrieval config) / up to 83% at higher config cost. LoCoMo has an active methodology dispute between the two teams, details below.

• On DMR: Zep publishes 94.8; Mem0 has not published DMR results. DMR is an older, shorter-horizon eval than LongMemEval or LoCoMo.

• On BEAM: Mem0 publishes 1M (64.1) and 10M (48.6) scale results. We have not found Zep-published BEAM numbers.

• On tokens per retrieval: Zep ~1.6K on LongMemEval and 27–2,297 across LoCoMo configs; Mem0 ~6.9K across LoCoMo, LongMemEval, and BEAM. Zep is tighter on raw context size; Mem0 trades more context for higher absolute accuracy.

• On latency vs full-context: both are ~90% faster. The interesting gap is accuracy, not latency.

Full numbers, methodology notes, and the trade-off discussion below.LongMemEval

LongMemEval is a 500-question multi-session memory evaluation. Both teams publish numbers on it, which makes it the cleanest comparison surface.

Methodology notes worth calling out:

• Zep reports LongMemEval primarily as improvement deltas over full-context baselines. Mem0 reports absolute task-level scores. The formats aren't always 1:1 comparable at the sub-category level.

• Zep's 1.6K tokens-per-retrieval is lower than Mem0's 6.8K — a real Zep advantage on context size. The trade: Mem0's April 2026 algorithm posts +22 overall accuracy points on the same benchmark.

• Mem0's post doesn't publish a matched per-run full-context baseline for the 93.4, so the overall comparison is on the same benchmark but not under identical harness conditions.

LoCoMo

LoCoMo is a 1,540-question long-term memory benchmark. Both teams publish numbers. This is also where the two teams have most publicly disagreed on methodology.

Zep's published LoCoMo configurations (from their Mem0-alternative page):

Mem0's published LoCoMo results (April 2026 algorithm):

BEAM — long-horizon stress test

BEAM evaluates long-horizon memory at 1M and 10M token scales. Mem0 publishes; we have not found Zep-published BEAM numbers as of April 2026.

The interesting signal: Mem0's token count barely grows from 1M to 10M. Most retrieval systems blow up their context budget to hold accuracy at scale. Mem0 holds flat around 7K.

DMR — Deep Memory Retrieval

Zep reports on Deep Memory Retrieval with a clean win:

Mem0 has not published DMR results. DMR is an older and shorter-horizon evaluation than LongMemEval or LoCoMo, which is why recent memory-layer work (including ours) has shifted to the longer benchmarks. Zep's DMR number is real and Zep-favorable; we're not going to pretend otherwise.

Integration and Developer Experience

Mem0 runs in four lines of code:

from mem0 import Memory
memory = Memory()
memory.add(["I'm building a customer support agent in Python."], user_id="alex")
results = memory.search("What is Alex building?", user_id="alex")

from mem0 import Memory
memory = Memory()
memory.add(["I'm building a customer support agent in Python."], user_id="alex")
results = memory.search("What is Alex building?", user_id="alex")

from mem0 import Memory
memory = Memory()
memory.add(["I'm building a customer support agent in Python."], user_id="alex")
results = memory.search("What is Alex building?", user_id="alex")

user_id and session_id handle scope automatically. It's framework-agnostic — works with LangChain, LlamaIndex, CrewAI, AutoGen, or no framework at all.

Zep requires a running service (self-hosted or Cloud) before you write a line of application code. If you want full infrastructure ownership from day one, that is the right model. If you want to ship the memory layer today and deal with infra later, it's friction.

Hosting and Data Control

When to Choose Each

Choose Zep if:

• You specifically want Graphiti's temporal graph primitives exposed at the infrastructure level

• You're comfortable running a dedicated memory service and want that operational model from day one

• DMR-style short-horizon retrieval is the workload that maps to your use case

Choose Mem0 if:

• You need memory scoped across users, sessions, agents, and organizations with a single API

• You want the highest published accuracy on LongMemEval (93.4) and LoCoMo (91.6)

• You want benchmark coverage that includes 1M and 10M token scales (BEAM)

• You're building across multiple agent frameworks and want framework-agnostic memory

Feature Comparison

The Honest Takeaway

Zep built a real memory system with real engineering behind it, and they publish enough numbers to defend their position. On DMR, on tokens per retrieval, and on latency at tight config budgets, they lead.

On the long-horizon benchmarks the field has moved to, LongMemEval, LoCoMo (April 2026 algorithm), and BEAM, Mem0 posts the highest public numbers we have seen. The headline comparisons: LongMemEval 93.4 vs 71.2, LoCoMo 91.6 vs 80.32–83.

We'd encourage you to read both teams' benchmark posts directly, including Zep's methodology critiques, before picking. If after reading both you want memory accuracy on multi-session, long-horizon workloads, Mem0 is the strongest pick in April 2026. If you specifically need to squeeze tokens below 2K per retrieval and can live with the accuracy trade, Zep is a fair choice.

We will re-run this post any time either side updates their benchmarks.

External references

• Mem0: Token-Efficient Memory Algorithm (April 2026)

• Mem0 Research — updated benchmark results

• Zep: State of the Art in Agent Memory

• Zep: Lies, Damn Lies, and Statistics — Is Mem0 Really SOTA?

• Zep: Mem0 Alternative (published LoCoMo configs)

• Graphiti by Zep — GitHub

• LoCoMo Benchmark Dataset

• LongMemEval