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What is Scenario Mode?

In EverOS Cloud, you choose a Scenario Mode when you create a Memory Space. The mode defines how EverOS extracts and consolidates memories based on the chat-session structure. Specifically, Scenario Mode tells EverOS:
  • how to interpret the chat-session structure (human–AI interaction vs multi-participant group chat)
  • the extraction strategy and granularity (single-speaker-centric vs multi-participant in one run)
  • how memory extraction and consolidation are applied in this space
Once a Memory Space contains data, you cannot change its Scenario Mode. This protects memory consistency and retrieval quality.
If you are unsure, start with a new space and validate the behavior with a small test conversation before storing important data.

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Two Scenario Modes

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Personal AI Assistant / Companion

This mode is designed for chat sessions where one human user talks with one or more AI assistants/companions over time.
  • Memory subject (per chat session): the human user
  • Best for: personal assistant/companion experiences, including cases where you talk to multiple AIs
  • Extraction focus: preferences, personal facts, and evolving states that help an agent serve you better

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Key Features

  • Human-Centric Extraction: The system monitors the dialogue and extracts memories, traits, and preferences only for the human user.
  • AI Memory Exclusion: To maintain a clean and focused user profile, EverOS does not extract memories for the AI agent, but still keeps the atomic facts of responses from the AI agent.
  • Deep Personalization: By focusing all extraction resources on the human user, the system can capture more nuanced details about their personality, history, and evolving needs.

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Team Collaboration

This mode is designed for multi-participant group chats. The group chat may include an AI participant, or it may be human-only. In a single extraction run, EverOS extracts and updates memories for every participant in the conversation.
  • Memory subject: the group and its participants
  • Best for: multi-participant group chats and team workflows
  • Extraction builds:
    • a group profile: recurring topics, decision makers, shared norms, and team conventions
    • participant profiles: each member’s role, action items, responsibilities, and relevant traits

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Key Features

  • Granular memory extraction: EverOS tracks distinct situational memories for every participant individually, even when multiple topics are discussed in parallel within the same timeframe. Each contribution is attributed to the correct speaker, never merged into a single undifferentiated stream.
  • Scenario-specific profiles: Profile fields are structured for professional contexts. Instead of free-form AI-generated text, profiles capture structured data such as roles, action items, and ownership. The group profile surfaces who drives decisions, what topics recur, and how the team operates.
  • Separated storage: Memories are stored as individual episodic records associated with each user’s unique user_id. This prevents profile contamination where one participant’s context is mistakenly attributed to another.
This scenario is ideal for Discord moderators, team assistants, or any agent operating in a social or professional group setting.
1

Decide what each chat session looks like

If each session is one human user talking with one or more AIs, choose Personal AI Assistant / Companion.If each session is a work-oriented group chat with multiple participants collaborating on professional tasks, choose Team Collaboration.
2

Create a new space with the chosen mode

Set the Scenario Mode at creation time.
Confirm the mode in your space settings before you start storing important memories.
3

Validate with a small test conversation

Add a small, low-stakes conversation and verify retrieval matches your expectation (personal profile vs group + participants).