Haki vs Mem0, honestly

Haki and Mem0 both give AI agents long-term memory: they watch conversations, extract facts, and inject the relevant ones back into context on later requests. We build Haki, so read this with that in mind — and to keep ourselves honest, the section where Mem0 is better comes first.

Last updated: August 2026.

Where Mem0 is genuinely ahead

Ecosystem. Mem0 is the most-adopted project in this category — around 62,000 GitHub stars as of August 2026, integrations across most agent frameworks, and first-party support in places like the AWS agent tooling. If you need a framework plugin that already exists today, Mem0 probably has it and we may not.

A mature hosted platform. If you want managed — dashboards, a hosted API, no infrastructure — Mem0's platform has been operating longer, with more surface, than our cloud.

Community gravity. More tutorials, more Stack Overflow answers, more people who have hit your problem before. That's worth real money in debugging time, and no comparison table should pretend otherwise.

If those three points decide it for you, use Mem0. The rest of this page is about the cases where they don't.

The architectural difference in one scenario

A user tells your agent in March: "send my invoices in French." In May: "actually, English."

What happens next is the whole comparison.

Mem0's model stores memories as searchable text entries. An LLM-driven update step decides whether a new statement adds, updates, or deletes an entry. When it works, the old memory is replaced. When it doesn't — the update step is itself a model call, and model calls miss — both entries exist side by side, and retrieval returns whichever scores higher for the query at hand. There is no per-memory validity interval, no explicit "this replaced that" link, and users have filed issues about audit timestamps arriving as NULL.

Haki's model is a ledger. Facts are versioned rows with explicit status: the March fact becomes superseded, the May fact is active, and the lineage between them is stored. A superseded fact is structurally excluded from "current" retrieval — not outranked, excluded. When two active facts genuinely contradict and no date can arbitrate, both are served flagged contested with their dates, rather than one being picked silently. And every context packet ships with a trace ID: which facts were included, which were excluded, and the stated reason for each.

The difference matters most on the boring failure: the agent that confidently applies a preference the user revoked two months ago. With a flat store, that's a ranking accident waiting to happen. With a ledger, it's a state machine violation — the kind of thing a test can catch, and ours do.

Self-hosting: read the fine print on both

Both projects are open source and both can run on your machines. Two details are worth checking on any memory system, ours included:

Where extraction runs. Turning conversation into facts takes an LLM in both systems. The question is whether you can point that call at an endpoint you control. Both allow it; the practical difference is defaults and dependencies — Haki runs as one docker compose up on Postgres 16 + pgvector with embeddings computed locally (no network call in the retrieval path), and extraction is an async, provider-configurable step that never blocks a response.

What enforces isolation. In Haki, tenant separation is PostgreSQL row-level security — the database refuses cross-project reads even if application code forgets a filter, and there's a test that proves it. If you're evaluating any memory layer for multi-tenant use, ask what happens when the application-layer filter is missing. "Nothing gets returned" should be the database's answer, not the code's promise.

What deletion means. Haki's forget cascades to facts, embeddings, source events, and traces, and writes a timestamped receipt. If erasure requests are part of your compliance reality, test this path on whatever you choose — it's the one that's hardest to retrofit.

Feature comparison

On benchmarks

You'll notice no accuracy scores on this page. That's deliberate: memory benchmark numbers are currently not comparable across vendors — the same system scores tens of points apart depending on whose judge prompt and harness runs it, and several published claims have failed independent reproduction. We publish our own numbers with the full methodology, the judge, per-question output, and the baseline reproduced under the standard protocol, in our reliability report — and we'd genuinely encourage you to hold every vendor, us included, to that bar.

When to pick which

Pick Mem0 if you want the largest ecosystem, a mature managed platform, or a framework integration that exists today.

Pick Haki if memory correctness is the product requirement: you need to know what changed, what's current, and why the agent said that — or you're in an environment where data can't transit a third party, deletion must be provable, and isolation has to be enforced below the application.

Either way, run a two-session test before committing: state a preference, contradict it, open a fresh session, and ask. Then ask the system to show you why it answered what it answered. The second question is the one that separates the category.