Introduction

MemoryOS is a memory infrastructure layer for AI products. It lets your AI remember the right things about the right users — without your team rebuilding extraction, retrieval, ranking, lifecycle cleanup, conflict handling, domain schemas, permissions, and dashboard controls from scratch.

The problem MemoryOS solves:

Every AI product eventually hits the same wall: your AI forgets everything the moment the conversation ends. Here are three real scenarios where that hurts:

Customer support bot — A customer reported a billing issue last week. They come back today with a follow-up. Your bot asks them to explain the problem all over again. The customer is frustrated. Your agent has no idea who they are.

AI tutor— A student has been struggling with quadratic equations for three sessions. Every new session, the tutor starts from scratch. It never adjusts its teaching style. It never remembers the student is preparing for JEE Main in March.

AI career coach— A user told your assistant their goal is to get a senior engineering role in six months. Three conversations later, the assistant has no memory of that goal and gives generic advice that ignores everything the user already said.

But memory storage is just the beginning. Production AI systems face deeper problems:

Conflicting information — A user says they’re vegetarian in one session, then orders chicken in another. Without conflict resolution, your AI holds contradictory beliefs about the same user simultaneously.

Multi-agent chaos — When multiple agents work in parallel — a planner, an executor, a reviewer — they each build their own fragmented picture of the user. There’s no shared ground truth.

Cross-service amnesia — A user interacts with your support bot, your onboarding flow, and your recommendation engine. Each service operates in isolation. None of them know what the others learned.

MemoryOS solves all of this — a persistent, structured memory layer with built-in conflict resolution, a unified memory store across agents, and cross-service memory sharing, so every part of your AI stack knows what it needs to know

Without MemoryOS vs. with MemoryOS

Support agent — returning customer

Without MemoryOS

Customer: "Hi, I'm following up on my billing issue."

Agent: "Hi there! How can I help you today?
        Could you describe your issue?"

Customer: "I already explained this last week..."

Agent: "I'm sorry, I don't have any record of that.
        Can you start from the beginning?"

→ Customer repeats everything. Trust eroded.

Conflicting information — same user, changing facts

Without MemoryOS

Session 1 — User: "I'm vegetarian, avoid meat in recipes."
Assistant stores: "User is vegetarian."

Session 2 — User: "Suggest a good chicken dish for dinner."
Assistant stores: "User eats chicken."

Next session — Assistant holds both facts simultaneously.
It contradicts itself mid-conversation.

→ No conflict resolution. AI gives inconsistent answers.

Cross-service amnesia — user across multiple services

Without MemoryOS

Support bot:       User complained about slow checkout. Ticket closed.
Onboarding flow:   User skipped the payment setup step.
Recommendation AI: Suggests premium plan upgrade to the same user.

Each service has its own isolated context.
None of them know what the others learned.

→ User gets an upgrade pitch right after a bad support experience.
   Trust destroyed.

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How MemoryOS works (in two lines)

mem.add(messages, external_user_id)   ← after each conversation
mem.get(query,    external_user_id)   ← before the next model call

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Choose your path

If you are building	Start here	What you get
A chatbot, assistant, copilot, coding agent, or general SaaS AI feature	General Engine	Tenant-scoped long-term memory for facts, preferences, goals, procedures, relationships, and expertise
A tutoring, learning, exam-prep, or student-coaching product	EdTech Engine	Student memory for grade, curriculum, weak topics, strong topics, learning style, exams, and review urgency
A customer support bot, support copilot, or agent-assist product	Support Engine	Customer support memory for open issues, issue history, communication preferences, support type, sentiment, and escalation risk
HR, healthcare, agriculture, or another domain not yet available	General Engine	Production-safe generic memory today; migrate to a domain schema when it becomes available
User-controlled memory shared across multiple agents or apps	Memory Passport	Consent-based universal memory using agent keys and user UUI tokens

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General Engine versus domains

Every tenant can use the General Engine. It is the default.

Domain schemas add industry-specific memory on top of the General Engine.

General tenant
  add() -> generic extraction
  get() -> generic system_prompt_addition

Domain tenant
  add() -> generic extraction + domain overlay
  get() -> generic context + domain-aware context

Available production domains:

• General Engine for broad AI memory
• EdTech Schema for learning products
• Support Schema for customer support products

Coming later: HR Tech, HealthTech, AgriTech

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Dashboard

Use the workspace dashboard for setup, domain selection, usage, API keys, users, quality logs, and domain-specific views such as student or support dashboards.

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Start building

Start with:

• Quickstart for the fastest code path
• General Engine for the default memory model
• Domain schemas for EdTech and Support
• Authentication for API key format and identity
• Memory Passport only if you need user-controlled cross-agent memory