Last updated 08-21-2026
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EverOS
EverOS gives LLM agents persistent, structured memory across sessions, platforms, and agent teams without stuffing entire histories into the prompt. It ingests multimodal inputs (PDFs, images, docs, slides, URLs), organizes them into episodic and semantic memory units, and retrieves only the context each query needs. You can run it on EverOS Cloud or self-host the full stack under Apache 2.0.
Most agent memory tools stop at vector search and chunk retrieval. EverOS runs a full lifecycle: it structures interactions into MemCells, clusters them into MemScenes, and distills repeated agent trajectories into self-evolving Skills shared across your agent team. That procedural layer is rare in memory infra, which usually treats recall as a flat archive rather than evolving know-how.
Developers building multi-agent systems, coding assistants, or long-running chat agents are the core audience. EverOS plugs into Claude Code, Codex, OpenClaw, Hermes, and MCP servers without rewriting your agent loop. Benchmarks on the site cite 93.05% accuracy on LoCoMo, under 500ms p95 retrieval latency, and roughly 7 to 15 times lower token usage than naive RAG.
93.05% accuracy on the LoCoMo long-dialogue memory benchmark
Under 500ms p95 retrieval latency via hybrid mRAG
Self-evolving Skills: agent Cases auto-promote into reusable Skills across teams
One-call multimodal ingestion for PDFs, images, docs, Excel, slides, and URLs
Cloud Free tier includes 50,000 MCU and 100,000 retrieval API calls per month
Apache 2.0 self-hosted option with unlimited Memory Spaces and full source access
Compatible with Claude Code, Codex, OpenClaw, Hermes, and MCP servers
Self-evolving Skills turn repeated agent trajectories into shared procedural memory without manual curation.
Hybrid mRAG delivers 93.05% LoCoMo accuracy with under 500ms p95 latency.
Identical API between cloud and self-hosted Apache 2.0 deployment with Markdown export.
One ingestion call handles PDFs, images, docs, slides, and URLs without extra pipelines.
Cloud Free caps at 3 Memory Spaces and 50,000 MCU per month.
Self-Evolving Skills require the Pro cloud tier or self-hosted setup.
Pro plan pricing shifts to $25 per month after the beta period ends.
What is EverOS?
EverOS is a memory operating system from EverMind that turns fragmented agent interaction histories into structured, evolving memory. LLM agents use it to stay consistent, updatable, and traceable across long sessions without stuffing entire histories into the prompt.
How is EverOS different from vector-store RAG?
EverOS is a lifecycle system, not a flat embed-and-retrieve store. It structures interactions into MemCells, consolidates them into MemScenes, and reconstructs only the necessary context per query. Repeated agent wins also self-promote into shared Skills, which typical RAG pipelines do not handle.
Does EverOS offer a free plan?
Yes. EverOS Cloud Free costs $0 and includes 3 Memory Spaces, 50,000 MCU per month, and 100,000 retrieval API calls per month. The self-hosted Community tier is also free forever under Apache 2.0 with unlimited Memory Spaces on your own compute.
Can I self-host EverOS?
Yes. EverOS ships as open source under Apache 2.0 on GitHub. You run the full memory stack locally with unlimited Memory Spaces and unlimited MCU on your hardware. The API matches EverOS Cloud, so you can switch endpoints without code changes.
Which agent frameworks work with EverOS?
EverOS integrates with Claude Code, Codex, OpenClaw, Hermes, and MCP servers out of the box. It also supports OpenAI and Anthropic SDKs. You drop it into an existing agent loop without rebuilding your orchestration layer.
What file types can EverOS ingest?
EverOS accepts PDFs, images, Markdown docs, Excel spreadsheets, slides, and URLs in a single ingestion call. It parses, chunks, and indexes them as retrievable memory so agents can recall that content at inference time without separate pipelines.