
EverMind
EverMind builds EverOS, a memory layer for AI agents. It turns stateless language models into systems that retain context across sessions, days and platforms, offered to developer teams as Apache 2.0 open source or a managed cloud service.
What is EverMind?
EverMind presents itself as an AI research company, and its main product, EverOS, as a Memory OS for AI Agents. It addresses a specific failure: language models restart from zero at every session, and context windows stop at 128K-200K tokens. The company also takes aim at classic RAG, which returns similar fragments without deeper understanding, the example given being that a stored note like My daughter loves robotics never surfaces when a gift recommendation is needed. Three building blocks carry the product: multimodal retrieval (mRAG), a self-evolving agent memory that records trajectories as Cases and distils repeated patterns into reusable Skills, and the Memory Bank, where stored memories can be inspected and edited. The underlying engine is called EverCore; a second product, EverMe, offers a visual digital twin. EverMind publishes benchmark figures it says are reproducible, LoCoMo 93.05%, LongMemEval 83.00% and HaluMem 93.04%, alongside sub-200 ms retrieval latency and token use around a tenth of a full context. Two arXiv papers back the approach: EverMemOS, built on MemCells and MemScenes, and HyperMem, a hypergraph memory. Five scenarios are shown, from multi-agent systems and personalised companions to enterprise knowledge bases, support intelligence and wearables. The stack runs as managed EverOS Cloud or self-hosted under Apache 2.0, with code on GitHub and models on Hugging Face.
What it does
- Hold on to context well past the 128K-200K token ceiling of a model's context window
- Ingest and retrieve multimodal material through a single API: PDFs, images, Word documents, spreadsheets, presentations, emails, HTML pages, text files and URLs
- Distil agent trajectories into Cases, then into reusable Skills
- Share memory across several agents and several users, with user, group and agent memory
- Inspect, manage and edit stored memories through the Memory Bank interface
- Track how facts evolve over time with temporal knowledge tracking
When to use EverMind / When not to
A quick filter to help you decide if EverMind is the right fit.
When to use EverMind
- Teams building multi-turn or multi-agent AI systems that need to carry context from one session to the next
- Developers who want to self-host their memory stack under Apache 2.0, with Docker and their own data on their own machines
- Builders of personalised AI companions that have to remember a user over months, not minutes
- Companies assembling an enterprise knowledge base or customer support intelligence on top of their own documents
- Hardware teams working on wearables, one of the five scenarios the site puts forward
When not to use EverMind
- Anyone shopping for a language model: the company states plainly that it does not build brains, only memory
- Non-technical end users looking for a finished application, since EverOS is an API and a self-hosted stack
- Under-18s, who are excluded by the terms of service (II.1)
- Buyers who must have a budget approved before testing anything, as no price is published
- Anyone planning to use the product to develop or train a competing offering, which the terms forbid (III.1.d)
How to use EverMind
A typical end-to-end flow, from setup to results.
- Decide which route fits: the managed EverOS Cloud service, or the open-source stack on your own infrastructure
- For the cloud route, sign up on everos.evermind.ai, where there is no infrastructure to run yourself
- For self-hosting, step 01, clone the EverOS repository from GitHub
- Step 02, start the Docker services
- Step 03, check that the services are up and running
- Step 04, install uv
- Step 05, install the project dependencies
- Step 06, set the environment variables
- Follow the quick-start section of the GitHub README for the full walkthrough
- Connect your agent through the API, following the documentation at docs.evermind.ai, then ingest your files and URLs through the single ingestion API and manage what is stored from the Memory Bank
Pros & Cons
Pros
- Apache 2.0 open source with a fully self-hostable stack, or a managed cloud service: the choice stays with you, and there is no forced lock-in
- Benchmark figures are published and presented as reproducible by third parties
- Two research papers are referenced on arXiv, so the design can be read rather than taken on trust
- Multimodal ingestion goes through a single API instead of one pipeline per format
- Memory can be shared between several agents and several users
- The Memory Bank makes stored memories visible, manageable and editable rather than opaque
- No training on non-public user content, outside the exceptions the documents list
Cons
- No pricing page at all: no amount, no tier and no currency are published anywhere on the site
- No postal address and no registration number appear on the pages collected
- The marketing site is very thin, amounting to a home page, terms, privacy policy, FAQ, careers and a few product pages
- The everos.evermind.ai interface is rendered entirely in JavaScript, so nothing is readable without an account
- No security certification is displayed, neither SOC 2 nor ISO 27001, and no subprocessor list is published, the policy giving only categories of recipients
- Footer social links to Bluesky, Threads, Mastodon, X, TikTok and Bilibili all lead to the same LinkedIn page, and the privacy page mailto links point to legal@miromind.ai while the displayed text reads legal@Evermind.ai
- Self-hosting assumes Docker, uv and environment variables, and the liability cap is low: the greater of six months of fees paid and 100 USD
Pricing & Plans
No price is published. The pages collected carry no pricing page, no amount, no tier and no currency, so the entry cost of the commercial service cannot be stated here. What is free is the open-source edition: the full EverOS stack is released under the Apache 2.0 licence and can be self-hosted, the user bearing only the cost of their own infrastructure. EverOS Cloud is presented as a paid managed service, described as a fully managed cloud solution with automatic scaling and maintenance and enterprise support included. The terms of service confirm the commercial framework without ever quantifying it: paid accounts require billing information, a valid payment method and applicable taxes (V), certain services are paid in advance by purchasing service credits governed by separate Service Credit Terms (V.2), and any price increase carries at least 30 days' notice (V.3). A prospective buyer therefore has to contact the publisher to learn what the service costs.
- a fully managed cloud solution with automatic scaling and maintenance and enterprise support included
- sold without any published price
- the complete memory stack on your own infrastructure
- with context management
- mRAG and offline memory
- under the Apache 2.0 licence
Data, GDPR & hosting
A consolidated view of how EverMind handles your data.
GDPR overview
Neither GDPR nor any equivalent wording appears anywhere on the pages collected: the company makes no compliance claim, and no EU representative under Article 27 is identified. The privacy policy of 20 January 2026 (IX) does grant rights that mirror the regulation, namely access, erasure, rectification, portability, restriction and objection, withdrawal of consent, and complaint to your national data protection authority, exercised through the account or by writing to legal@evermind.ai. International transfers rest on generic references to appropriate safeguards and legally valid transfer mechanisms, with no mechanism named and no standard contractual clauses cited. Services are stated to be provided from Singapore, and the terms place disputes under Singapore law and the exclusive jurisdiction of Singapore courts. Under-18s are not accepted; data collected from them unintentionally is deleted once reported.
Who owns the data?
The publisher is Evermind AI, Inc., named in both the terms of service and the privacy policy, each effective 20 January 2026. Under those terms you keep all rights to your Inputs, and the publisher grants you whatever rights it may hold in the Outputs generated from them. That grant is conditional by construction: it transfers only rights the company actually has, and Outputs are not guaranteed to be unique, so identical Outputs produced for other users give you no claim over them. The home page markets the arrangement as data sovereignty remaining forever yours, and the self-hosted edition is the practical expression of it, since the stored memories then sit on your own infrastructure rather than the publisher's.
Reuse rights
User content feeds the running of the service: the publisher uses it to provide, maintain, develop and improve the products, to comply with the law and to enforce its own rules. On training, the stated principle is that non-public user content is not used to train EverMind's models. The two documents do not list the same exceptions, and the gap is worth knowing. The privacy policy (II.5) names three: content flagged for Trust and Safety review, feedback you explicitly send through the company's channels, and an explicit opt-in. The terms of service (IV.5) name only two, keeping the flagged content and the feedback but dropping the opt-in. Model training is otherwise declared to draw on the public internet, licensed third-party datasets, contributions from users and crowd workers, and internally generated synthetic data. One clause colours everything else: all user content is treated as non-confidential unless agreed otherwise in writing (terms IV.3.c).
Data retention & training
Hosting summary
What the publisher declares: the privacy policy of 20 January 2026 (V.2) states that the services are generally provided from Singapore. It reserves the right to transfer personal data outside your own jurisdiction, to an affiliate or to a third party abroad, and warns that local protection laws may differ and be less protective than your own. Transfers are said to rest on appropriate safeguards and legally valid transfer mechanisms, but not one is named: no standard contractual clauses, no adequacy decision. Recipients appear as categories only, covering hosting and infrastructure, compliance and audit, research and data processing, payment, and customer support. No subprocessor is named, no precise hosting country is given beyond Singapore, and no cloud region is declared. One network observation, offered as context rather than as a declaration: the home page resolves to an anycast node of AS16509, Amazon.com, in Amsterdam, which is a CDN point of presence and says nothing about where data is stored. Self-hosting changes the question entirely, since the open-source stack then runs on your own infrastructure, under your own jurisdiction.
Things to keep in mind
Risks and trade-offs to weigh before adopting EverMind.
- The mailto links on the privacy page point to legal@miromind.ai, three times in the archived HTML, while the address displayed in the text is legal@Evermind.ai: a rights request sent by clicking the link leaves for a different domain, so type the address by hand
- The two legal documents disagree on training: the privacy policy lists three exceptions, including an opt-in, where the terms of service list only two, so read both before assuming what happens to your content
- All user content is deemed non-confidential unless agreed otherwise in writing (terms IV.3.c), and any feedback you send becomes the publisher's property outright (terms VII)
- No price is published although the terms provide for paid accounts and prepaid service credits, so the cost is discovered after contact rather than before
- An account may be suspended or closed without notice or explanation (terms II.3), and the services may be discontinued without notice (terms X.3): keep an exit plan for memories your agents depend on
- Liability is capped at the greater of the fees paid over the last six months and 100 USD, which is low next to the value of a memory store you have built up
- The publisher is identifiable by name alone, with no postal address and no registration number; the Inc. suffix suggests a US company while the services are provided from Singapore under Singapore law, and no security certification is displayed, a Trust Center existing at trust.evermind.ai but not collected here
Setup & Integrations
Technical difficulty
Two very different levels. The cloud route is straightforward: create an account on everos.evermind.ai and use the service, with what the company calls zero ops overhead. Self-hosting is developer work, in six documented steps: clone the repository, start the Docker services, check that they are running, install uv, install the dependencies, set the environment variables. Either way, putting EverOS inside an application means calling the API and following the documentation at docs.evermind.ai. Comfort with Docker and Python tooling is the real prerequisite, and a non-technical user will not get past the setup.
Deployment
Supported languages
Behind EverMind
Social
Resources
All the official URLs gathered for verification and reference.
Frequently asked questions
What is EverOS for?
Is EverOS open source, and can I self-host it?
Is there an API?
How much does it cost?
Is my content used to train EverMind's models?
Who can sign up, and which law applies?
Should you pick EverMind?
EverMind sells infrastructure, not a finished product: EverOS is a memory layer that developers wire into their own agents, and the company says plainly that it does not build the models. What can be checked stands up well, with an Apache 2.0 licence, a public repository, benchmark figures presented as reproducible and two arXiv papers behind the design. What cannot be checked is everything commercial around it: no price, no postal address, no security certification, no subprocessor list. Evermind AI, Inc. carries a US corporate suffix while the services are stated to be delivered from Singapore under Singapore law; the domain dates from 22 February 2020, with a first web archive capture on 2 December 2021. For a team comfortable with Docker, the open-source route removes most of that uncertainty. For anyone who needs a budget first, the cloud offer remains a conversation to have.
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