Mem0 vs Zep vs Letta: AI Agent Memory Frameworks Compared

Mem0, Zep, and Letta are the three open-source projects that define AI agent memory for developers. They solve the same headline problem, giving an agent recall beyond its context window, and they solve it in genuinely different ways.

This comparison is about which architecture fits which job, and about the boundary where a developer framework stops being the right tool at all.

At a glance

Mem0: extraction-first memory

Mem0 sits between your application and the model, watching conversation and extracting the parts worth keeping. Rather than storing raw transcripts, it distils them into discrete memories and retrieves the relevant ones on later turns.

Architecturally it runs a dual store: a vector database for semantic similarity, and a knowledge graph for entities and relationships. That combination is deliberate, because similarity search alone cannot answer questions about relations, and a graph alone is a poor fit for open-ended recall.

Where it fits. Consumer and prosumer products where the value is that the assistant remembers you: preferences, past requests, ongoing projects. The extraction-first design means you get useful memory without designing a schema.

Honest limitations. Automatic extraction is lossy by nature, and what it decides to keep is not always what you would have chosen. Its centre of gravity is the individual user rather than an organization, which is a design choice rather than a flaw, but it matters if you were hoping for shared team memory.

Zep: memory that knows when things were true

Zep is built on Graphiti, its open-source temporal knowledge graph, and the temporal part is the whole argument.

In a conventional store, when a fact changes you have a problem. The account owner was Dana, now it is Sam. Store both and the agent sees a contradiction. Overwrite and you have destroyed the ability to answer anything historical.

A temporal graph records that the first fact was valid until a date and the second is valid from it. Queries about now resolve correctly, queries about last quarter also resolve correctly, and nothing has to be deleted.

The Zep paper on arXiv reports outperforming MemGPT on the Deep Memory Retrieval benchmark, and Zep's own materials report figures on LongMemEval alongside substantial latency reductions. Those latter numbers are vendor-published, so treat them as a claim to verify on your own workload rather than a settled result. The architecture argument stands on its own regardless.

Where it fits. Domains where facts have a lifecycle: customer state, org structure, project ownership, anything an enterprise tracks. It is also the most governance-oriented of the three.

Honest limitations. A temporal graph is more machinery than many applications need. If your agent's memory is "the user prefers metric units," the succession model is overhead. Extraction quality still gates everything, as it does for any graph system.

Letta: the agent manages its own memory

Letta, formerly MemGPT, comes from a different premise. The MemGPT paper framed the limited context window as an operating-systems problem: give the agent tools to page information in and out of its own context, and let it decide what to hold.

So in Letta the agent edits its own memory blocks. It decides what is important enough to keep resident, what to archive, and when to go fetch something back. Memory is an agent behaviour rather than an external service the application calls.

Where it fits. Long-running autonomous agents that operate over extended horizons and need to manage their own state. It is the most conceptually elegant of the three and the most agent-native.

Honest limitations. Handing memory management to the model means memory quality inherits model judgement, which is variable. It is also the most per-agent of the three, and the hardest to reason about when you want a shared picture across many agents.

How to choose

A practical note on evaluation: benchmark numbers in this category are mostly published by the vendors whose systems they favour, and the benchmarks themselves are young. The comparison that will actually decide your choice is running two of them against a week of your own traffic and reading what each one chose to remember. Extraction quality is the variable that dominates, and it is workload-specific.

Where a developer framework stops being the answer

All three are libraries you wire into an application. You decide what gets written, you own the store, and the memory belongs to that application.

That is the correct shape for building a product. It is the wrong shape for an enterprise, for a reason that is structural rather than a matter of features.

Per-agent memory does not compose. A company running twenty agents each with its own memory has twenty partial pictures that never reconcile. The support agent knows what support learned. The sales agent knows what sales learned. Nothing connects the churn signal in a support thread to the renewal that sales is forecasting, because no layer holds both.

Organizational memory is a different thing:

• Sourced from systems, not just chat. Meetings, tickets, documents, and messages, not only what a user typed at one agent.
• Shared across people and agents, so there is one picture rather than many.
• Permission-aware by inheritance, so access rules from the source systems travel with each fact. Without this, a memory layer becomes a route around access control.
• Continuously maintained, pruning and retiring facts rather than accumulating contradictions.

Coworker AI works at that layer. It connects to 50+ tools, maintains organizational memory across them, and runs agents that act on what they find rather than only retrieving it. It also exposes that memory over MCP, so the context is available inside the AI tools your team already uses. Plans are Pro at $29.99 per user per month, Max at $149.99, and Enterprise pricing on request.

These are not mutually exclusive. If you are building a product, use a framework. If you are trying to make your company's AI understand your company, that is a different layer.

Book a demo to see the difference in practice.

Frequently asked questions

What is the main difference between Mem0 and Zep?

Mem0 is extraction-first, distilling conversation into facts held in a hybrid vector and graph store, and it is oriented around remembering individual users. Zep is built on a temporal knowledge graph, so facts carry validity periods and can supersede one another, which suits domains where information changes and history matters.

Is Letta the same as MemGPT?

Letta is the productized continuation of MemGPT. The MemGPT paper introduced the idea of an agent managing its own context like an operating system pages memory, and Letta is that concept built into a framework with a managed option.

Are Mem0, Zep, and Letta open source?

All three have open-source cores and commercial managed offerings. Zep's temporal graph, Graphiti, is open source while Zep itself is offered as a managed service. Check each project's current licence before committing, since licensing in this category has changed before.

Which agent memory framework is best?

There is no single best. Mem0 is the fastest path to working per-user memory, Zep is the strongest where facts change over time and governance matters, and Letta is the most agent-native for long-running autonomous work. The deciding test is which one remembers the right things from a week of your own traffic.

Can these frameworks give an agent memory of my whole company?

Not on their own. They store what a given application or agent observes. Company-wide memory requires ingesting from systems of record rather than conversations, sharing one picture across agents and people, and inheriting permissions from the sources. That is a platform concern rather than a library concern.

Do I need a knowledge graph for agent memory?

Only if your users ask structured questions. Similarity search over vectors handles "find something like this" well and fails at counting, enumeration, negation, and multi-hop traversal. If those questions matter, you need graph structure. If they do not, a vector store is simpler.

Related reading

• What Is AI Agent Memory? Types and Architectures
• What Is MCP (Model Context Protocol)?
• What Is an LLM Gateway?
• LLM Agent Architecture
• AI Agent Orchestration Platforms
• Agent Workflows Explained
• Enterprise AI Agents
• 16 Ways to Use AI For Knowledge Management
• 8 Best AI Agent Builders for Enterprise Teams