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EverOS Cloud is the memory engine for developers. Give every AI product you build (an agent, an assistant, or any AI app) a persistent, self-evolving memory, so a stateless LLM becomes something that remembers and grows, without you operating any memory infrastructure. Today’s models are capable but forgetful: each session resets what they learned about the user, the task, and the mistakes they already made. Stuffing the context window is slow and expensive, and a bolted-on vector store only retrieves, it never learns. EverOS turns conversations, tasks, and domain knowledge into persistent, retrievable, evolving memory, so your product gets more useful the more it is used.

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What EverOS gives your product

Self-evolving memory

Records each agent run as a Case and distills recurring Cases into reusable Skills, so your agents get better at a task the more they run it.

User + Agent memory

Two tracks: User Memory (who the user is) and Agent Memory (what the agent has learned). Scope by owner, type, and user / agent / app / project / session.

Remembers any format

Native multimodal: text, images, audio, docs, PDF, HTML, spreadsheets, slides, and email become structured, searchable memory.

Precise retrieval

Keyword, vector, hybrid, or agentic search, resolving coarse Episodes down to atomic Facts for verifiable recall instead of fuzzy summaries.

Full lifecycle control

Add, search, edit, and delete. Memory is a governable asset you control, not an append-only black box.

Memory Bank

See exactly what’s stored (profiles, episodes, agent cases and skills) in the console, browsable by owner, type, and time.

Cloud and open source, one API

The same API on managed Cloud and self-hosted (Apache-2.0). Move between them for compliance without rewriting code.

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How EverOS works (a quick glance)

EverOS workflow
  1. Episodic Trace Formation: EverOS monitors the continuous dialogue stream and uses semantic boundary detection to segment interactions into coherent events.
  2. Structured Encoding: For each event, it synthesizes a MemCell containing a concise third-person Episode, atomic Facts, and time-bounded Prospections.
  3. Semantic Consolidation: Instead of flat storage, MemCells are dynamically clustered into thematic MemScenes (e.g., “Health”, “Work”) to capture high-level contexts.
  4. Profile Evolution: The system continuously distills these scenes to update a structured User Profile, separating stable traits from temporary states.
  5. Reconstructive Recollection: Your agent retrieves context via hybrid search (BM25/vector/RRF) or Agentic Retrieval. EverOS navigates MemScenes, filters for time-valid evidence, and iterates until the retrieved context is “necessary and sufficient.”
  6. Grounded Reasoning: The agent uses the returned memories as grounded context to respond and plan, enabling long-horizon reasoning without replaying weeks or months of history in the prompt.

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Proven on public benchmarks

  • LoCoMo 93.05% · LongMemEval 83.00% · HaluMem recall 90.04% — memory accuracy and anti-hallucination
  • +234.8% peak task-success lift from the self-evolving skill engine
  • ~450ms P95 latency · >90% fewer tokens than full-context (about 10× lower cost)

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Core memory operations

Add Memory

Extract memory from messages and interactions, across text and files.

Get Memory

Precise memory filtering based on owner, type, and metadata fields.

Search Memory

Context-aware retrieval (keyword, vector, hybrid, agentic) across every format.

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