How ModelBrain compares
Almost every "best AI memory tool" article on the internet is a vendor ranking itself first. We'd rather you compare on three things that actually predict whether you'll be happy with a memory tool a year from now: where your data physically sits, what happens when you tell it to forget something, and what either project can actually show you about recall quality, not what it claims.
The read below is ours, based on each product's own public documentation as of publish time; it isn't a citation on every row, so check Mem0's docs and Letta's docs directly for anything you're relying on. We're not neutral, since we built ModelBrain, so don't take our word for the "maturity" row either: it's newer than both of these, and hasn't been in production as long.
For a step-by-step version of the Mem0 comparison with a test you can repeat, see ModelBrain vs Mem0 for one person.
ModelBrain vs. Mem0
Shape
A memory layer only, one binary, over MCP. No hosted default.
A memory API and platform, offered self-hosted or as a managed hosted service; check which mode a given doc page assumes.
Where data lives by default
Your own disk. No account exists until you connect ChatGPT or opt into a paid tier.
The hosted tier is the path most people start from, which means an account and a vendor-held copy from the first request unless you deliberately self-host instead.
Forgetting
Three explicit modes (revert, chain, object), not one undifferentiated delete, and deletion integrity is a hard-fail line in our published eval, not a scored metric.
Supports updates and deletion; read their docs for the exact guarantee around whether a superseded memory can resurface in retrieval.
Recall-quality evidence
Published adversarial methodology and a harness that ships with v1, designed to accept any MCP backend. 86% right-answer rate, 100% on prompt-injection resistance.
Publishes its own benchmark comparisons. We'd rather you run one harness against both than trust either vendor's numbers, including ours.
Maturity
In beta now, on macOS, Windows and Linux. Newer than Mem0, without its production history yet.
Shipping today, funded, an established user base and integrations.
ModelBrain vs. Letta (MemGPT)
Shape
A memory layer only. No agent runtime of its own.
A stateful agent framework with memory as one part, plus a hosted service and its own agent abstractions.
Where data lives
Local vault by default, no account, no identity generated at install. Server storage is a separate opt-in tier.
Self-hostable, and also offered as a managed cloud service; check which mode their docs assume for the feature you want.
Delete semantics
forget takes a named mode, revert (default), chain, or object, and deletion integrity is a hard-fail line in our eval.
Memory blocks are editable by the agent itself. Read their docs on who can rewrite a block and what survives that.
Recall-quality evidence
Published adversarial methodology and a harness that ships with v1, designed to accept any MCP backend. 86% right-answer rate, 100% on prompt-injection resistance.
Published research lineage from the MemGPT paper. Compare that to our harness once it ships.
Maturity
In beta now, on macOS, Windows and Linux. Newer than Letta, without its production history yet.
Shipping, funded, sizeable community, years of public history.
Pick one of them if
You want a hosted service you don't have to run yourself, or an agent framework rather than a memory layer, or something with years of production use behind it. Those are real reasons to consider them.
Pick ModelBrain if
You already have an assistant you like and only want it to remember, across every project, without your notes leaving the laptop by default. You care that deletion is predictable and auditable. And you'd rather judge a memory tool on a harness you can run yourself than on a comparison page, including this one.