Memory infrastructure for production AI

Give your AI the context it should never forget.

MemoryOS turns conversations into governed, prompt-ready memory across sessions, agents, and services. Extraction, retrieval, conflict resolution, provenance, consent, and domain schemas — in one API.

Python SDKTypeScript SDKREST APIMCP Server
app.memoryo.dev/select-domain
MemoryOS domain selection
Customer Support schema selected

Storage is the easy part.

Production memory must decide what is durable, what changed, which source to trust, what the user approved, and what context the next agent actually needs.

Session amnesia

A returning user has to repeat goals, preferences, issues, and history every time a new session starts.

Multi-agent drift

Support, onboarding, recommendations, and copilots each build a different picture of the same person.

Conflicting truth

New facts contradict old facts, but raw vector retrieval returns both and leaves the model to guess.

Domain schema registry

One memory platform, multiple production engines.

Start with universal memory. Switch on a domain schema when your AI needs industry-specific extraction, retrieval, and safety behavior.

General Engine
EdTech Schema
Support Schema
Memory Passport
SDK + API
MCP Server
StoredPrefers concise explanations
01memory.acceptedsemantic retrieval
02memory.acceptedquality gate
03memory.acceptedconflict handling
04memory.acceptedimportance decay

Works for any AI product

Universal memory for facts, goals, preferences, and procedures.

Use the general engine when you want durable user context without choosing an industry schema first.

LIVE MEMORY CONTEXTengine: general
Prefers concise explanations
retrievedrankedfresh
Building a B2B SaaS for Indian SMBs
retrievedrankedfresh
Uses FastAPI, Postgres, and Docker
retrievedrankedfresh
context → prompt → response

Governed retrieval

Context arrives ready for the next model call.

Relevant memories are ranked, source-aware, and compact enough to use without handing the model an unfiltered history.

Four stages. One reliable context layer.

Keep your model, tools, and agent framework. MemoryOS handles the memory lifecycle around them.

01

Add

Conversations, tickets, lessons, tool events, and corrections.

02

Extract

Durable facts, preferences, goals, procedures, and domain signals.

03

Resolve

Authority, recency, conflicts, provenance, and version history.

04

Retrieve

Compact, prompt-ready context for the next model call.

$pip install memoryos
import os
from memoryos import Memory

client = Memory(api_key=os.environ["MEMORYOS_API_KEY"])

client.add(
    messages=[{"role": "user", "content": "I prefer concise answers."}],
    external_user_id="customer-123",
    agent_id="support-bot",
)

result = client.get(
    query="How should I answer this user?",
    external_user_id="customer-123",
)
prompt_addition = result.system_prompt_addition if result.has_context else ""
MemoryOS product tour
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Your workspace is ready

Your workspace is ready

One control surface for memory, users, quality, and usage.

Memory lab

Test the memory decision, not a fake chatbot.

Explore extraction, conflict handling, and consent in one compact console. It is simulated, so no account or API key is required.

memoryos://playground

Input signal

Remember memory

POST /v1/memory

This demo visualizes the decision path. Production requests use your workspace key and tenant boundary.

Memory response

Relevant context, ready for the model

Preference

Short replies for debugging

8.2

Preference

Detailed learning steps

7.6

Fact

Mostly codes in Python

8.8

Production foundations

Reliable after the demo.

MemoryOS provides the controls teams usually discover only after their first memory prototype reaches real users.

Quality gates

Block duplicate, low-signal, over-budget, or rate-limited writes before they pollute memory.

Conflict resolution

Use authority, source truth, recency, and explicit review paths instead of storing contradictions forever.

Provenance

Track which service wrote a memory, what evidence produced it, and why retrieval trusts it.

Lifecycle controls

Reinforce useful memory, archive stale facts, preserve versions, and handle corrections without losing history.

Graceful degradation

Keep your product responsive when quotas or dependencies force a partial memory experience.

Tenant isolation

Keep customer memory scoped to the right tenant, user, agent, and permission boundary.

Inspect what MemoryOS stores and why.

Use the dashboard for API keys, users, usage, schemas, quality logs, and domain-specific views.

Create a workspace

Start with your real product

Give your next AI interaction the context the last one earned.

Try the simulated playground, follow the quickstart, or talk with us about production onboarding.