Everos
Build, evaluate, and integrate long-term memory for self-evolving agents.
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What is Everos?
EverOS is a memory operating system designed to give AI agents persistent, self-evolving long-term memory. It transforms stateless LLMs into intelligent agents that can remember, learn, and evolve over time by extracting memory from messages and multimodal data. The platform supports both multi-user group chat and Human-AI chat scenarios, enabling agents to maintain context across days, sessions, and platforms.
Key features include EverCore (a self-organizing memory OS inspired by biological imprinting), mRAG hybrid retrieval architecture for multimodal data (PDFs, images, Word docs, spreadsheets, emails, URLs), HyperMem hypergraph memory architecture for long-term conversations, Self-Evolving Agent Memory that records agent trajectories as Cases and distills patterns into reusable Skills, and Memory Bank for transparent memory management. EverOS achieves 93.05% on LoCoMo benchmark, 83% on LongMemEval, and 93.04% on HaluMem.
EverOS is built for AI developers building multi-agent systems, personalized AI companions, company knowledge bases, customer support intelligence, wearable hardware AI, and AI coding assistants. It serves enterprise teams needing multi-agent coordination, developers building AI agents with persistent memory, and researchers evaluating memory systems through open benchmarks like EverMemBench and EvoAgentBench.
Everos pricing
Pricing model: Freemium
EverOS offers two deployment options. EverOS Open Source is free under Apache 2.0 license, allowing self-hosting on your own infrastructure with full access to context management, mRAG, and offline memory capabilities. EverOS Cloud is a fully managed cloud service in public beta that provides persistent memory out of the box with zero ops overhead and enterprise-grade reliability, featuring automatic scaling, maintenance, and enterprise support included. Users can register at everos.evermind.ai to create a developer account and start free during the beta period. The cloud service charges are not publicly disclosed and appear to be available upon contact or through the developer portal.
Everos pros
- 93.05% accuracy on LoCoMo long-context benchmark
- Open-source under Apache 2.0 license with no vendor lock-in
- Self-evolving Skills Engine boosts task success by 234.8%
- mRAG multimodal retrieval handles PDFs, images, spreadsheets, emails
- HyperMem hypergraph architecture captures complex entity relationships
- Memory Bank interface lets users inspect and edit generated Skills
- Supports both one-on-one and multi-agent enterprise collaborations
- Sub-200ms retrieval latency for fast memory operations
- Token efficiency at 1/10 of full context window usage
- Native support for multimodal data through single API
- Records agent trajectories as Cases for experience reuse
- Distills repeated patterns into reusable SOPs (Standard Operating Procedures)
- Works with Claude Code, OpenClaw, Hermes, and other execution agents
- Python SDK and REST API for easy integration
- Transparent memory management with clear view of user/group/agent memory
- Temporal knowledge tracking distinguishes current from outdated facts
- EverOS Cloud offers fully managed solution with zero ops overhead
Everos cons
- Cloud service version still in public beta as of 2026
- Requires Python 3.12 and Docker for local deployment
- LLM_API_KEY and VECTORIZE_API_KEY required for memory extraction
- Relatively new project with 666 commits but limited production history
- Documentation primarily focused on developers with technical background
- Self-hosting requires managing your own infrastructure and scaling
- Best performance requires configuring multiple API keys properly
- Limited enterprise support documentation compared to established vendors
- Memory Bank interface still under development for backend integration
- No detailed public pricing tiers disclosed for EverOS Cloud
Frequently asked questions about Everos
What is EverOS and what problem does it solve?
EverOS is the industry's first memory layer designed specifically for self-evolving AI agents. It solves AI's fundamental limitation of memory loss — today's models are 'brilliant amnesiacs' whose knowledge is frozen in time. EverOS equips agents with persistent, proactive, and self-evolving memory, enabling them to remember, learn, and evolve from every interaction. It breaks through context window limits (128K-200K tokens), maintains behavioral consistency, and allows AI to grow from ongoing interactions rather than starting from zero every session.
How does EverOS's self-evolving memory work?
EverOS records agent trajectories as Cases, automatically extracting Task Intent, Approach, Key Insights, and Quality Scores from completed tasks. It then performs semantic clustering to group similar task experiences via vector-based clustering. Finally, it distills clustered experiences into Standard Operating Procedures (SOPs) called Skills. Skills evolve incrementally — successes reinforce steps while failures add trap warnings. This process boosted task success rates by 234.8% for software engineering problems using a 27B model, allowing it to match 397B model performance.
What is mRAG and how is it different from traditional RAG?
mRAG (Multimodal Retrieval-Augmented Generation) is EverOS's hybrid retrieval strategy that enables cross-modal search across different memory types. Unlike traditional RAG which returns similar text chunks without deep understanding, mRAG fuses dense semantic vectors, sparse keyword matching, and multimodal alignment through a single API. It natively parses and stores PDFs, images, Word documents, spreadsheets, presentations, emails, HTML pages, text files, and URLs, ensuring agents can accurately recall complex, cross-modal context like 'my daughter loves robotics' for gift recommendations.
What benchmarks does EverOS achieve and how do they compare?
EverOS achieves 93.05% on LoCoMo (long-context memory benchmark), 83% on LongMemEval (multi-session conversation evaluation), and 93.04% on HaluMem. Compared to alternatives: Traditional RAG achieves ~48% long-term memory accuracy, Other Memory Infra achieves 66.80%, while EverOS achieves 93.05%. EverOS also has <200ms retrieval latency versus 100-500ms for traditional RAG, and uses 1/10 the tokens of full context window approaches. The open-source benchmarks are reproducible by anyone.
Can I self-host EverOS or is it cloud-only?
EverOS offers both options. EverOS Open Source allows you to run the full memory stack on your own infrastructure with Docker, giving you complete control over context management, mRAG, and offline memory — you can inspect every layer, own your data, and contribute back to the community. EverOS Cloud is a fully managed solution for those who don't want to manage infrastructure, offering persistent memory out of the box with zero ops overhead and enterprise-grade reliability. You can deploy your way with no lock-in.
What types of AI agents and use cases does EverOS support?
EverOS supports Multi-Agent Systems for complex problem-solving through specialized agents, Personalized AI Companions from personal assistants to therapeutic chatbots requiring emotional intelligence, Company Knowledge Base that evolves with your team, Customer Support Intelligence that remembers past issues and preferences, Wearable Hardware processing fragmented interactions into structured patterns, and AI Coding Assistants with universal long-term memory. Use cases include Claude Code plugin, Rokid AI Assistant, Earth Online Memory Game, Missing Child Reunion Platform, and Alzheimer's Memory Assistant.
What is the Memory Bank interface?
Memory Bank is a new transparent memory management interface that gives users a clear view into all stored memories, including user memory, group memory, and agent memory. It provides tools to inspect, manage, and even edit generated Skills. This transparency allows developers to understand what memories are stored, how they're structured, and make adjustments when needed, rather than treating memory as a black box.
How does HyperMem architecture work?
HyperMem is a hypergraph-based hierarchical memory architecture recognized at ACL 2026 that captures high-order associations through hyperedges. Unlike flat vector databases, it uses a hypergraph structure where a single hyperedge can connect multiple nodes, perfectly capturing complex real-world entity relationships. It has topic, event, and fact layers for coarse-to-fine conversation retrieval, addressing cross-time associations and multi-hop reasoning. HyperMem hits 92.73% accuracy on LoCoMo for long-dialogue systems, setting a new standard for conversational coherence and personalization.
How do I get started with EverOS?
For self-hosting: clone the repo with 'git clone https://github.com/EverMind-AI/EverOS.git', cd into EverOS, start Docker services with 'docker compose up -d', install uv with the curl command, run 'uv sync', copy env.template to .env and set LLM_API_KEY and VECTORIZE_API_KEY, then start the server with 'uv run python src/run.py'. The server runs at http://localhost:1995. For EverOS Cloud, visit everos.evermind.ai to create a developer account and start free during public beta. Python SDK and REST API are available for integration.
What is EverOS's license and how can I contribute?
EverOS is licensed under Apache 2.0, making it fully open-source with no restrictive terms. Contributions are welcome across architecture methods, benchmark coverage, use-case write-ups, documentation, and bug fixes. You can browse Issues to find entry points, then open a PR when ready. The project welcomes all kinds of contributions including code, documentation, benchmark reports, and integration examples. Contributors can connect with maintainers @elliotchen200 on X or @cyfyifanchen on GitHub for project updates and collaboration opportunities.