Session amnesia
A returning user has to repeat goals, preferences, issues, and history every time a new session starts.
Memory infrastructure for production AI
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.

The memory problem
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.
A returning user has to repeat goals, preferences, issues, and history every time a new session starts.
Support, onboarding, recommendations, and copilots each build a different picture of the same person.
New facts contradict old facts, but raw vector retrieval returns both and leaves the model to guess.
Start with universal memory. Switch on a domain schema when your AI needs industry-specific extraction, retrieval, and safety behavior.
Works for any AI product
Use the general engine when you want durable user context without choosing an industry schema first.
Governed retrieval
Relevant memories are ranked, source-aware, and compact enough to use without handing the model an unfiltered history.
From messages to memory
Keep your model, tools, and agent framework. MemoryOS handles the memory lifecycle around them.
Conversations, tickets, lessons, tool events, and corrections.
Durable facts, preferences, goals, procedures, and domain signals.
Authority, recency, conflicts, provenance, and version history.
Compact, prompt-ready context for the next model call.
pip install memoryosimport 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 ""
Your workspace is ready
One control surface for memory, users, quality, and usage.
Memory lab
Explore extraction, conflict handling, and consent in one compact console. It is simulated, so no account or API key is required.
Input signal
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
Preference
Detailed learning steps
Fact
Mostly codes in Python
Production foundations
MemoryOS provides the controls teams usually discover only after their first memory prototype reaches real users.
Inspect what MemoryOS stores and why.
Use the dashboard for API keys, users, usage, schemas, quality logs, and domain-specific views.
Built around real workflows
Customer Support
Remember open issues, account context, sentiment risk, communication preferences, and resolution history.
Explore use caseEducation
Carry weak topics, exam context, learning style, language comfort, and progress signals into every session.
Explore use caseMemory Passport
Let users approve categories, inspect grants, correct facts, resolve questions, and revoke agent access.
Explore use caseStart with your real product
Try the simulated playground, follow the quickstart, or talk with us about production onboarding.