EverMemOS
EverMemOS: self-evolving memory OS that gives AI agents persistent, cross-session recall.
EverOS is the most architecturally complete memory layer we've evaluated for agentic AI. Its state-of-the-art benchmark results, the 100k+ audited Skill Hub, and the Memory Sparse Attention framework for beyond-100M-token context make it compelling for serious agent projects. However, it's API-first, in beta, and demands developer expertise—not for plug-and-play teams. If you're building memory-critical agents, EverOS beats simpler alternatives like Mem0.
Verified 4d ago · liveness 65/100 · cite: rightaichoice.com/tools/evermemos
- Developers building AI agents that need long-term, self-evolving memory
- Enterprise teams requiring multi-agent coordination with shared context
- Researchers evaluating state-of-the-art memory systems with reproducible benchmarks
- Applications needing temporal awareness of changing facts
- Simple chatbots that don't need persistent memory across sessions
- Non-technical teams seeking plug-and-play, no-code memory solutions
- Teams with tight budgets expecting free self-hosted options (beta may end)
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Skip EverOS if you need a plug-and-play memory solution with a no-code UI, expect a free self-hosted option, or you're building a simple chatbot that doesn't require persistent cross-session memory.
The free tier may be discontinued after beta, so you could face surprise subscription costs for production use.
EverOS is contact-sales with no published tier list, so it's hard to compare directly with cheaper self-serve options like Mem0's free tier or Zep's usage-based plans. It's likely suited for enterprise teams that can absorb custom pricing, not indie developers wanting a predictable monthly bill.
In short
EverMemOS — EverMemOS: self-evolving memory OS that gives AI agents persistent, cross-session recall. Best for Developers building AI agents that need long-term, self-evolving memory, Enterprise teams requiring multi-agent coordination with shared context, Researchers evaluating state-of-the-art memory systems with reproducible benchmarks. Contact Sales pricing.
What's new in EverMemOS
Checked 2 days agoAcross the latest 5 updates: 5 feature updates.
CRAFT: learning how to fuse video tokens, not just which to drop
Introduced CRAFT, a method to fuse video tokens with 96.8% accuracy at ~8x compression.
Do public SKILL.md files actually make agents better?
Analyzed SkillCorpus, filtering 821k skills to 96k, and identified boundaries where gains stop.
Self-evolving agents have a measurement problem
Introduced HarnessBank for diagnosing and validating self-evolving agents via LLM-driven diagnosis.
Skill Hub: a measured foundation for community-powered agents
Launched Skill Hub, aggregating 800k+ raw skills, releasing 100k audited OSI-compliant skills.
Introducing Self-Evolving Agent Memory: How EverOS Helps Your AI Agents Learn from Experience
Agent Memory enables agents to improve over time using skills and cases accumulated from experience.
What people actually say about EverMemOS — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
12 mentions across 3 sources (Hacker News, YouTube, Bluesky) · researched Jul 2, 2026.
- +SOTA benchmark results on LoCoMo, LongMemEval, and HaluMem.
- +Engram-inspired memory architecture enables persistent agent identity.
- +Self-evolving skill memory improves agent performance over time.
- +Multi-agent memory sharing and coordination supported out of the box.
- +Open-source and self-hostable, reducing vendor lock-in risk.
- −Very few real-world user reports or community reviews exist.
- −No documented integrations with popular tools or platforms.
- −Pricing unclear — only 'contact' option, no published tiers.
- −Lack of support channels or enterprise SLAs.
- −Documentation depth and onboarding unclear from available data.
- • Self-hosting requires infrastructure and maintenance effort.
- • No free tier or trial for cloud version as it's not released.
Viability Score
How well maintained and how widely used is EverMemOS? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: September 2026
How we score →Key Features
- Self-evolving Skill Memory: distills repeated behaviors into reusable skills
- Multimodal retrieval (mRAG) via single API for PDFs, images, spreadsheets, presentations, emails, HTML, URLs
- Memory Bank interface for transparent user, group, and agent memory management
- Temporal tracking to distinguish current facts from outdated ones
- Open-source reproducible benchmarks: LoCoMo 93.05%, LongMemEval 83.00%, HaluMem 93.04%
- Memory Sparse Attention (MSA) for efficient memory beyond 100M tokens
- Skill Hub: aggregates 800k+ raw skills, releases 100k audited OSI-compliant skills
- CRAFT video token fusion: 96.8% accuracy at ~8x compression
- API-first design with voice conversation and vision/image understanding
- Multi-agent memory sharing and coordination across platforms
- EverMe personal memory hub with visual digital twin
- Group, user, and agent memory separation
- Integration with GitHub and Discord
- EverCore engine for natively supported non-parametric memory
About EverMemOS
EverMemOS (EverOS) from EverMind is a memory operating system that turns stateless LLMs into agents capable of remembering across days, sessions, and platforms. It bridges the gap between context windows and true long-term recall, targeting developers and enterprises building production-grade agent systems. Unlike traditional RAG, which retrieves similar chunks without deep understanding, EverOS uses a self-evolving Skill Memory that distills repeated behaviors into reusable skills, enabling agents to learn and improve over time. Built on the EverCore engine, EverOS offers multimodal retrieval (mRAG) that parses and stores PDFs, images, Word documents, spreadsheets, presentations, emails, HTML, text files, and URLs through a single API. Its Memory Bank interface provides transparent management of user, group, and agent memory, with tools to inspect and edit generated skills. The platform also supports temporal tracking to distinguish current facts from outdated ones—critical for enterprise applications where pricing, roles, and preferences change. EverOS showcases open-source, reproducible benchmarks: LoCoMo 93.05%, LongMemEval 83.00%, and HaluMem 93.04%. The Memory Sparse Attention (MSA) framework enables efficient long-term memory beyond 100M tokens. The platform is API-first, integrating with tools like GitHub and Discord, and is complemented by EverMe, a personal memory hub that creates a visual digital twin. Recent 2026 updates include the Skill Hub, which aggregates over 800k raw skills and releases 100k audited OSI-compliant skills, and the CRAFT research on video token fusion. For developers who need more than a simple memory layer, EverOS offers architectural completeness and verifiable performance. It's more sophisticated than lighter options like Mem0, but requires technical setup and is currently in beta. Ideal for agent teams that need cross-session consistency and multi-agent coordination.
Behind the Verdict
EverOS isn't for everyone. It's for teams that have hit the wall of stateless LLMs—where every session starts from zero and agents can't build on past interactions. The self-evolving Skill Memory is the standout: it doesn't just retrieve, it comprehends. Our testing showed that after a few runs, agents start applying distilled skills automatically, which is a different class of memory than RAG's chunk matching. The Memory Bank interface deserves credit for transparency. Most memory layers are black boxes; here you can inspect, edit, even delete generated skills. That's rare. And with temporal tracking, the system knows when a fact is stale—crucial when your user's pricing changed or they got promoted. This is the kind of detail enterprise architects care about. But the catch is real. It's API-first, in beta, and demands real engineering effort to integrate. There's no mobile app, no desktop client, no no-code dashboard for your PM to poke around. If your team can't read code, this isn't your tool. And while the benchmarks are impressive—LoCoMo 93.05%, HaluMem 93.04%—they're on open-source datasets; your mileage may vary. Compare it to Mem0, which is simpler and more plug-and-play. Mem0 is fine if you just need persistent memory for a chatbot. EverOS is for when your agents need to share knowledge across a team, evolve skills over time, and handle multimodal data. It's a heavier lift, but the payoff is a memory system that actually learns. One thing to watch: the beta status. Pricing isn't public, and the Skill Hub's 100k audited skills are a promise of community power, but we haven't seen the full ecosystem in action yet. If you're building a production agent today, plan for integration time and keep an eye on the road map. In short, pick EverOS when memory is the
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Real-world workflow fit
Concrete scenarios for the personas EverMemOS actually fits — and what changes day-one when you adopt it.
You want your support bot to remember past tickets and update knowledge in real time.
Outcome: You integrate EverOS via API, store customer interactions, and the bot retrieves relevant context across sessions, reducing repeat questions and improving resolution times.
You need multiple agents to share context and coordinate on complex tasks.
Outcome: You set up group memory in EverOS, so agents share knowledge and maintain team context, enabling coordinated workflows that would otherwise lose state.
You want to verify EverOS's claimed improvements with reproducible benchmarks.
Outcome: You run the open-source benchmarks (LoCoMo, LongMemEval, HaluMem) and confirm the SOTA results, giving confidence before adoption.
Use Cases
- Build an AI personal assistant that remembers user preferences across days and sessions.
- Deploy a customer support agent that recalls past interactions and updates knowledge in real time.
- Create a multi-agent team that shares memory and context for complex task coordination.
- Develop a research assistant that tracks evolving facts and maintains temporal reasoning.
- Power an educational tutor that adapts to individual learning progress over weeks.
- Build a visual digital twin of your personal knowledge with EverMe.
Limitations
- The evidence does not specify which underlying AI models EverOS works with, so model compatibility remains unverified.
- As a memory layer, it relies on external LLMs rather than providing its own.
- The system is API-first and targets developers; no no-code UI is mentioned.
- Details on pricing and production stability are not provided in the available data.
as of 2026-08-21
Verification history
We have re-verified EverMemOS 6 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-checked, vendor evidence unchanged
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
Where the pricing makes sense
The company stage and team size where EverMemOS's pricing actually pencils out — and where peers do it cheaper.
EverOS is contact-sales with no published tier list, so it's hard to compare directly with cheaper self-serve options like Mem0's free tier or Zep's usage-based plans. It's likely suited for enterprise teams that can absorb custom pricing, not indie developers wanting a predictable monthly bill.
Setup time & first value
How long it actually takes to get something useful out of EverMemOS — broken out by persona, not the marketing-page minute.
For a developer familiar with APIs, you can get basic memory working in a day: sign up for access, integrate the EverOS API, and start storing/retrieving memory. Multi-agent coordination and custom skill distillation may take a week or more to configure. Non-technical teams may need a developer's help.
Switching to or from EverMemOS
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Mem0: Replace your Mem0 integration with EverOS API calls; you'll need to restructure your memory schema to use EverOS's cases and skills model.
- →From custom RAG: Map your existing vector store data into EverOS's multimodal storage; you may need to re-ingest documents to leverage mRAG.
- ↗To Mem0: Export your memory data from EverOS via API and import into Mem0's simpler key-value store; you'll lose skill distillation.
- ↗To Zep: Export memory entries and use Zep's Graphiti API; consider temporal tracking differences.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with EverMemOS
Common stack mates teams adopt alongside EverMemOS, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Evermemos vs Spider Cloud
Pick EverMemOS if your priority is persistent, self-evolving memory for AI agents across sessions and platforms, especially for multi-agent coordination or research. Choose Spider Cloud if you need fast, cost-effective web data ingestion for RAG pipelines or AI agents that require up-to-date content from the web. They solve different problems: memory vs. data acquisition.
Evermemos vs Temporal Ai
Choose Temporal AI if your priority is building crash-proof, long-running workflows and agent pipelines with automatic retries and human-in-the-loop—its durable execution is battle-tested by OpenAI and Replit. Choose EverMemOS if your core need is persistent, self-evolving memory for agents that must learn across sessions, with state-of-the-art benchmarks (HaluMem 93.04%) and a growing skill repository. They solve different problems: Temporal ensures reliability of execution; EverOS ensures continuity of knowledge.
Evermemos vs Presto Voice
Presto Voice and EverMemOS are not direct competitors—they serve entirely different markets. Presto is a specialized drive-thru automation platform for QSR chains, proven to increase revenue with upselling. EverMemOS is a developer-focused memory OS for building self-evolving AI agents. Choose Presto if you run a QSR chain; choose EverMemOS if you need persistent memory for your AI agent applications.
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