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# Six AI memory engines built for developers, not operators
- URL: https://intelligence.artificialrealitymedia.com/ai-memory-infrastructure-for-client-data/
- Published: 2026-08-06T09:00:00.000Z
- Updated: 2026-08-06T09:00:00.000Z
- Description: Mem0, Cognee, Zep, Letta, Supermemory and Basic Memory hold private client memory, and each expects a developer. What they do, and what to verify first.
- Author: Galina Fendikevich
- Tags: Cross industry, #For AI Buyers, #AI tools, #Import 2026-09-03 04:53

The companies actually building temporal, structured memory are real, funded and mostly open source — and not one of them ships something a business owner can buy and use. Five Season 1 operators asked Artificial Reality for a private client memory layer. These six are the closest engines that exist, and every one of them expects a developer.

*Identified by Artificial Reality as apparently relevant to that request. Not ranked, not independently tested, and not named or endorsed by any operator. Funding and product details were verified as of publication and change frequently — check the current state before purchasing.*

## [Mem0](https://mem0.ai/?ref=intelligence.artificialrealitymedia.com)

A persistent memory API for AI applications that extracts and stores durable facts about a person across sessions. Apache 2.0 licensed and fully self-hostable, which satisfies the privacy requirement at the infrastructure level. Raised $24M cumulative, most recently a $20M Series A led by Basis Set Ventures announced October 2025\. Published pricing runs from a free tier through $249 per month for graph memory, with on-premise on the enterprise tier.

**Verify:** it is an API. There is no interface where a person types a guest preference and gets it back.

## [Cognee](https://www.cognee.ai/?ref=intelligence.artificialrealitymedia.com)

An open-source memory engine that turns scattered data from many sources into a knowledge graph an assistant can query and refine over time. It is the strongest answer here to "never leaves my walls": Apache 2.0, with the full stack running locally on local models and no cloud dependency. Raised a $7.5M seed led by Pebblebed announced February 2026 — the most recent funding event among the infrastructure companies in this guide.

**Verify:** cloud pricing is not published, and the customer list is enterprise and research rather than small business.

## [Zep](https://www.getzep.com/?ref=intelligence.artificialrealitymedia.com)

Enterprise memory built on a temporal knowledge graph that tracks how facts about a person change over time and invalidates stale ones. That is the requirement almost nothing else meets: preferences that expire rather than accumulate. Its graph engine, Graphiti, is open source. Zep itself is cloud, with deployment into your own private cloud on the enterprise tier, and published pricing starts around $1,250 per year.

**Verify carefully:** Artificial Reality could not confirm any funding round with both an amount and a date from a credible source, and public records list the company as very small. The most technically sophisticated option here is also the thinnest capitalised, which is a real risk for a system holding your client history.

## [Letta](https://www.letta.com/?ref=intelligence.artificialrealitymedia.com)

A Berkeley research-lab spinout building stateful agents with self-editing long-term memory. Apache 2.0 and self-hostable. Raised a $10M seed led by Felicis, announced September 2024.

**Verify:** two flags. The last public funding event is nearly two years old, and the company now presents as a research lab whose shipping flagship is a coding agent — active development has moved away from general memory infrastructure.

## [Supermemory](https://supermemory.ai/?ref=intelligence.artificialrealitymedia.com)

A memory API for AI applications, from a young founding team. Raised a pre-seed led by Susa Ventures announced October 2025; the company states $3M and third-party reporting says $2.6M.

**Verify:** self-hosting is gated to the $399 per month tier and fully air-gapped deployment to enterprise, so the privacy requirement is expensive here rather than free.

## [Basic Memory](https://basicmemory.com/?ref=intelligence.artificialrealitymedia.com)

Builds a persistent knowledge graph from AI conversations and stores it as plain markdown files on your own machine, integrated with [Obsidian](https://obsidian.md/?ref=intelligence.artificialrealitymedia.com) and exposed to [Claude](https://claude.ai/?ref=intelligence.artificialrealitymedia.com). It is in this guide because it is Sam Polstein's hand-built arrangement, productised. Local use is free and open source; cloud starts around $15 per seat per month.

**Verify:** Artificial Reality found no institutional funding, so treat it as an indie project. It also requires setting up an MCP server — not hiring a developer, but not buying a product either.

## AI tools named in this report

| Tool     | Named by                    | Verdict                                          | Used for                                         |
| -------- | --------------------------- | ------------------------------------------------ | ------------------------------------------------ |
| Obsidian | Sam Polstein, HAUS          | Worked                                           | Markdown client folders an AI assistant reads    |
| Zep      | Artificial Reality analysis | Closest to temporal memory; funding unverifiable | Facts that expire when they stop being true      |
| Cognee   | Artificial Reality analysis | Category example                                 | Fully local knowledge graph, no cloud dependency |
| Mem0     | Artificial Reality analysis | Category example                                 | Self-hostable memory API with published pricing  |

*Tools named by operators on the record, plus companies identified by Artificial Reality as apparently relevant. Inclusion is reporting, not endorsement.*

## What to do with this list

Read it as a supply map, not a shortlist. Every hard technical requirement the five operators stated already has a working implementation somewhere above: facts that expire, local-first deployment, unstructured ingestion, an assistant that can read the record. The technology is not the obstacle.

If you employ or can hire an engineer, start with the one that matches your binding constraint — Cognee if nothing may leave the building, Zep if stale preferences are the problem, Mem0 if you want published pricing and a permissive licence. If you do not, stop here and try markdown files in a folder first.

## Where this comes from

S1E8: Why 99% of AI products fail — the full interview with Galina Fendikevich. Listen or watch: [YouTube](https://www.youtube.com/watch?v=WLzJ7U6inpw&ref=intelligence.artificialrealitymedia.com), [Spotify](https://open.spotify.com/episode/1DI6RkvekF4NF7HfpuzEqm?ref=intelligence.artificialrealitymedia.com) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/why-99-of-ai-products-fail-the-gap-between/id6780699025?i=1000780319385&ref=intelligence.artificialrealitymedia.com).