Customer memory for support agents that cannot afford to guess.
Remember account context, open issues, support history, sentiment risk, and resolution preferences. Your backend still owns real actions like refunds, invoice fixes, and ticket updates.
Open issue: invoice failed after Growth upgrade
ranked, sourced, and retrieval-ready
Prefers direct support replies and refund over replacement
ranked, sourced, and retrieval-ready
Uses Slack and webhook integrations
ranked, sourced, and retrieval-ready
High escalation risk when payment is involved
ranked, sourced, and retrieval-ready
Support problem
Why this needs memory infrastructure.
The hard part is not remembering text. It is deciding what is durable, what is stale, who controls it, and what the next agent should trust.
Customers repeat order, invoice, account, and complaint details in every chat.
Ticket history alone does not tell the agent what context matters right now.
Generic RAG retrieves policy docs but misses customer-specific support memory.
Support agents must not claim a refund or invoice was processed until the real backend tool confirms it.
What you get
Clear context for the next model call.
Open issue first
Current unresolved issues are ranked before old history so agents start in the right place.
Safe retrieval
Support context is retrieved separately from live actions, reducing accidental promises.
Escalation risk
Payment failures, delays, fraud sensitivity, and repeated complaints can be surfaced as risk context.
Less repetition
Prior contact, preferences, and account state carry forward across support sessions.
Prompt-ready context
The retriever returns compact bullets or JSON that your support agent can use immediately.
Multi-vertical
SaaS, ecommerce, banking, travel, telecom, EdTech support, or general support routing.
Integration
Use the SDK first. Add governance when needed.
The same API supports solo apps and production teams. Start with a stable user ID, then add source metadata, service writers, or Memory Passport when your product needs them.
from memoryos import Memory
mem = Memory(api_key="mem_live_xxx")
context = mem.get(
external_user_id="cust_8a72",
query="what matters for this support issue?",
format="bullets",
)
order = tools.get_order("ORD-44821")
# MemoryOS gives context.
# Your backend tools still perform the real action.Make every support conversation start with the right customer context.
Start with support memory in a test workspace, connect your real support tools, and inspect what gets stored before production traffic.