Best AI Memory for Custom Agent Products (2026)

The quick answer

If your agent product needs a memory layer that ships with a working SDK, per-user isolation, and an embeddable wiki UI your users can browse: Ricord. If you're writing the agent runtime from scratch and want memory built into the framework, not bolted on: Letta. If your differentiation is retrieval quality and you want to own the code: Mem0 OSS. The full matrix is below.

Why this question matters for agent builders

The memory layer you embed today is the one your users will blame for a year. Three things make this decision higher stakes than it looks:

1. Lock-in is real.Each layer's data model is different. Migrating live users between memory backends is not the kind of weekend you want.
2. Cost scales per user, not per request.The pricing page lies about month one. Model the bill at 10k MAU and 100k MAU before signing — the curves diverge fast.
3. End-user perception is the actual product."The agent remembered me" is the magic moment every agent product is selling. The memory layer either delivers that or it doesn't — there's no middle.

That changes the criteria. SDK ergonomics, per-user isolation, conflict resolution at ingest, and cost at user scale matter more than the install ease that mattered for personal-use roundups.

The decision matrix

Nine criteria, six options. "Roll your own" is Postgres + pgvector + a worker — included because it's what most teams reflexively reach for, and the comparison is illuminating.

Slot-by-slot — which fits your build

If you want to ship the memory feature this sprint, not next quarter

Ricordcovers this. REST API, TypeScript + Python SDKs, per-user scoping that's a single parameter on every call. The differentiator for agent products is the embeddable wiki view — your end-users can see what the agent has learned about them, in their own language. That's a feature that doesn't exist on other layers.

If you're writing the agent runtime from scratch

Lettais the cleanest fit. Memory is built into the agent runtime, not added as a service. State, conversation history, and persistence are all in the same data model. The trade is operational burden — Letta self-hosts, so you own the database, the queue, the model access. If you're going to be doing that work anyway, Letta saves you a coordination boundary.

If retrieval quality is your competitive moat

Mem0 OSS is the right pick. Apache 2.0, vector-first, the codebase forks cleanly. Plan for the production-grade work (entity resolution, conflict resolution, hard delete, sub-second recall under load) as real engineering quarters, not weekends. Worth it if retrieval is the thing that makes your agent different. Not worth it otherwise. When OSS wins →

If your agent captures more than just chat (browser, audio)

Supermemoryships a Chrome extension and Pipecat audio/video integration. If your agent product is actually a meeting assistant or a browser companion, Supermemory's multi-source ingest matches the shape of your problem better than the chat-only memory layers.

If you're deep in the LangChain stack already

LangChain memory modulesare technically free and integrate with zero glue. They're also framework-bound, no hard delete, no conflict resolution, and the memory primitives are deliberately minimal — you ship a working chatbot, but you don't ship a memory layer worthy of that name. Fine for prototyping. Migrate before you have paying users.

If you're an experienced infra team that loves owning code

Postgres + pgvector + a worker works. So does the next year of your engineering time. Be honest about whether memory infra is where you want that quarter to go. If yes, build it. If no, pick from the rows above.

Why Ricord wins for most agent builders

Three reasons, in the order agent builders mention them after migrating:

1. The embeddable wiki UI.Your end-users get a feature no competing agent ships — a browsable record of what the agent learned about them. That's a retention play, not just an infra decision.
2. Per-memory pricing. 10k users with uneven usage is much cheaper on per-memory than per-active-user. The cost curve stays linear with activity instead of with signups.
3. Hard delete that's actually hard.GDPR compliance for your end-users isn't a checkbox — it's an audit. Ricord deletes everywhere, including derived graph edges and wiki pages. Most competitors hard-delete the primary record and leave orphans.

// Embed in your agent runtime (TypeScript)
import { RicordClient } from "ricord";
const ricord = new RicordClient({ apiKey: process.env.RICORD_API_KEY });

// Per-user scope is a single param
await ricord.memories.create({
  user_id: "<your-end-user-id>",
  content: "User chose Postgres for the new service",
});

// Recall on the next turn
const hits = await ricord.memories.recall({
  user_id: "<your-end-user-id>",
  query: "database choice",
});

Getting started

Pick the slot that matches your build. If it's Ricord: the snippet above is enough to start. If it's Letta: fork the GitHub repo and follow the agent-runtime quickstart. If it's Mem0 OSS: plan the quarters.

The signal that you picked right: by the time you have 100 active end-users, your support inbox doesn't have "the agent forgot me" in it. If it does, your memory layer is the wrong layer.