Mem0 vs Letta (MemGPT): LLM Memory Frameworks Compared

Mem0 is the better choice when you want to bolt memory onto an app you already have; Letta is better when you are building a stateful agent from the start. Mem0 is a model-agnostic memory layer that sits between your application and any LLM and recalls facts across sessions. Letta — the framework formerly known as MemGPT — is a full agent runtime where memory management and persistence are part of the platform. Neither is universally better; the right one is the layer your problem lives at. (Both move fast — verify current capabilities and pricing against each project's docs before committing.)

A note on how this comparison works. We The Flywheel is vendor-agnostic. Neither Mem0 nor Letta paid for placement or influenced its score. This is an architecture-level comparison of two fast-moving open-source projects: we describe what each is designed to do and where it fits, and we flag explicitly where a capability claim should be re-checked against the vendor's own documentation before you build on it. The full framework is documented and public, and the scoring approach is detailed in How We Score Tools.

How do Mem0 and Letta score head-to-head?

We scored both projects across six dimensions that define an LLM memory system: the memory model itself, how it self-hosts, how it integrates with the rest of a stack, how mature and stable the project is, how it is priced, and how much it takes to learn. Each dimension is scored out of 10. The composite is the unweighted mean, because the right weighting depends on your job — a team adding memory to an existing app should weight integration and learning curve heavily; a team building a new agent should weight the memory model and runtime maturity.

The composite gap is narrow on purpose, and the scores are editorial judgments about fit, not benchmark outputs — both projects are young enough that a published version can change the picture within a release or two. Mem0 edges ahead on the unweighted mean because integration breadth and a gentle learning curve are where a drop-in layer is supposed to win. Letta leads on the two dimensions that matter if memory is the architecture: the memory model and the runtime around it. A team building a new agent from scratch flips the result by weighting those two. Read the per-dimension sections before trusting the headline number, and re-verify anything version-specific against the docs.

How is the memory model different?

The memory model is the root of every other difference. Mem0 is built as a memory layer: it sits between your application and an LLM, extracts salient facts from conversations, stores them, and surfaces the relevant ones back into context on later turns — so your agent appears to remember across sessions without you managing that loop by hand. The selling point is that it is additive to a stack you already have.

Letta inherits MemGPT's central idea: treat the LLM's limited context window like memory in an operating system, paging information in and out so an agent can maintain long-running state beyond what fits in a single prompt. In Letta, memory is part of the agent runtime — the platform manages self-editing memory and persistence as first-class concerns rather than as a bolt-on. One model optimizes for dropping memory into an existing app; the other optimizes for building an agent whose behavior is defined by managed state. Choose the layer first. The framework second. (The exact mechanisms each ships today have evolved — confirm the current design in each project's documentation rather than from older write-ups.)

Which integrates more easily into an existing stack?

This is where Mem0's design intent shows. It is positioned to be model-agnostic and to slot into a range of LLM providers and agent frameworks, so a team that has already chosen its model and orchestration can add persistent memory with a relatively small change. For the common job — "we have an app, we want it to remember" — that lower integration cost is the practical advantage.

Letta is the opposite trade. Because memory lives inside its agent runtime, you get more out of the box if you build on Letta from the start, but you are adopting a platform and its agent model rather than dropping a library into your existing loop. That is the right trade when memory is central to the system and the wrong one when you just want to augment something you already run. The specific list of supported providers and frameworks on either side changes as the projects develop, so do not assume a given integration exists — check the current compatibility matrix in the docs before you build against it.

Self-host versus managed: who controls the data?

Both projects follow the now-standard open-core shape: an open-source path you can run yourself and a managed option that removes operational overhead. The reason this dimension matters for a memory system specifically is that the data being persisted is your users' conversation history and the facts extracted from it — among the most sensitive data an AI app holds.

Self-hosting keeps that memory store on infrastructure you control and avoids per-request platform costs, at the price of running and maintaining the service. The managed option trades that control for less operational work. For regulated or sensitive data, the self-host route is usually why teams reach for open frameworks here rather than a closed memory API. The exact boundary — what is gated behind the hosted tier, what limits apply to the open version — is a detail that shifts on both sides, so verify the present split on each vendor's pricing and documentation pages before you commit.

How mature are they, and what do they cost?

Both are young, fast-moving projects, and that is the honest headline on maturity. Letta carries the research lineage of MemGPT and presents as an agent runtime; Mem0 presents as a memory service with broad integration ambitions. Neither should be treated as a settled, stable platform the way a decade-old database would be — expect APIs and capabilities to move between releases, and pin versions in production.

On price, both follow the open-source-plus-managed pattern: the self-hosted path's cost is mostly the infrastructure and engineering time to run it, while the managed tiers carry their own pricing that can include per-request or usage-based components. We are deliberately not quoting specific dollar figures here because pricing in this category is repriced often and varies by tier and usage — treat any number you find as provisional and confirm it on the vendor's current pricing page before you budget. The durable point is the shape: free-to-run if you self-host, usage-priced if you don't.

The verdict, by use case

There is no universal winner. There is a right answer for each job. Match yours below.

Pick Mem0 if…

• You already have an app or agent and want to add long-term memory with the smallest change to your existing code.
• Model and framework portability matter — you want a memory layer that does not lock you to one LLM provider.
• Integration speed is the priority and you would rather drop in a memory service than adopt a new agent platform.
• Your team is augmenting a stack it has already chosen, not rearchitecting around memory.

Pick Letta if…

• You are building a stateful agent from the start and want memory, persistence, and the agent loop managed by the runtime.
• Memory is the architecture, not an add-on — the agent's behavior depends on long-running, self-editing state.
• You value the MemGPT lineage and want a platform built around operating-system-style context management.
• You are willing to adopt a platform rather than a library to get more capability out of the box.

Or pair them with the architecture view

These two are not the only way to give an LLM memory, and the framework choice is downstream of the architecture decision. If you are deciding how memory should work in your system before picking a tool — retrieval, embeddings, what to store and recall, and how it fits an agent — start with the architecture, not the framework. See the CTAIO deep dive on LLM memory architecture for the system view, and the broader second-brain pillar for how memory, retrieval, and recall fit together. Once the architecture is clear, the Mem0-versus-Letta question usually answers itself.

The bottom line

Mem0 and Letta are often compared as if they were the same product, but they are the best tools for two different jobs. Mem0 is the memory layer you add to an app you already have; Letta is the agent runtime you build on when memory is the point. The narrow composite edge to Mem0 (7.8 to 7.3) reflects that a drop-in layer wins the integration and learning-curve dimensions more decisively than Letta wins the memory-model and runtime ones — not that it is universally better.

Decide the layer before the framework. If you are augmenting an existing stack, Mem0 is the lower-cost path; if you are architecting an agent around managed state, Letta is the stronger foundation. Because both projects move quickly, treat every capability, version, and pricing detail on this page as a pointer to verify against the vendors' own docs, not a fixed fact. Check the Technology Radar for the latest, compare the field in AI Knowledge Base Tools the Radar Recommends, and read the full methodology for how we arrive at these recommendations.

What is the difference between Mem0 and Letta?

They operate at different layers. Mem0 is a memory layer: a library and managed service you place between your application and an LLM so the model can store and recall facts across conversations, without you rebuilding the agent loop. Letta — the project formerly known as MemGPT — is a full stateful-agent runtime: an agent server where memory management, persistence, and the agent API are first-class parts of the platform rather than an add-on. The practical question is not which has better memory, but which abstraction level your project needs: a drop-in memory service over your existing stack (Mem0) or an agent runtime you build on from the start (Letta). Both are evolving quickly, so confirm current scope against each project's documentation before deciding.

Is Letta the same as MemGPT?

Letta is the continuation of the MemGPT project under a new name. MemGPT was the research system that introduced the idea of giving an LLM operating-system-style memory management — paging information in and out of a limited context window so an agent could maintain long-running state. The team behind it rebranded the open-source project and surrounding company to Letta. If you read older material referencing MemGPT, treat it as the lineage of today's Letta, but check the current Letta documentation for what the platform actually ships now, because the project has changed substantially since the original paper. Verify the exact naming and scope on Letta's own site before citing specifics.

Can I use Mem0 with any LLM or agent framework?

Mem0 is positioned as model-agnostic and designed to integrate with a range of LLM providers and agent frameworks rather than locking you to one model. That is much of its appeal: it aims to add a persistent memory layer to a stack you have already chosen. The exact list of supported providers, frameworks, and integrations changes as the project develops, so do not assume a specific integration exists — check Mem0's current documentation for the supported set before you build against it. The general design intent is portability across models; the specific compatibility matrix is the part that moves.

Should I self-host Mem0 and Letta or use the managed version?

Both projects have historically offered an open-source path you can self-host and a managed/cloud option, which is the usual pattern for this category — but the exact split, limits, and what is gated behind the hosted tier are details that shift, so verify the current offering on each vendor's pricing and documentation pages. As a rule of thumb: self-hosting gives you data control and avoids per-request platform costs at the price of running infrastructure; the managed version trades that control for less operational overhead. For sensitive or regulated data, the self-host option is the reason teams often start with open frameworks here. Confirm the present-day hosted versus self-host boundary before budgeting.

Which is better for a production AI agent, Mem0 or Letta?

It depends on where memory sits in your design. If you are adding long-term memory to an agent or app you have already built — and you want the smallest change to your existing code — Mem0's drop-in memory layer is usually the faster path to production. If memory is central to the agent's behavior and you want a runtime that manages state, persistence, and the agent loop for you, Letta's stateful-agent platform is the stronger foundation to build on. Neither is universally 'more production-ready'; they are production-ready for different jobs. Both are young, fast-moving projects, so validate stability, current features, and pricing against their own documentation before you ship.