Memory for |
Agents
MemU powers autonomous AI agents with persistent, evolving memory.
Continuously predict user intentions, act proactively, and work for you — even while you sleep.
Agent Memory Files
User Intention Prediction
Real-Time Intention Analysis
Current User: Sarah Chen
Session: Active for 12 minutes
Confidence: 94%
#### Predicted Intentions
- Primary: Seeking enterprise pricing info
- Confidence: 94%
- Evidence: Visited pricing page 3x, hovering on "Enterprise" tab
- Suggested Action: Proactively offer custom quote
- Secondary: Evaluating team onboarding
- Confidence: 78%
- Evidence: Downloaded team setup guide, searched "bulk invite"
- Suggested Action: Share team admin tutorial
- Emerging: Considering API integration
- Confidence: 62%
- Evidence: Brief visit to API docs, technical background
- Suggested Action: Prepare integration examples
Intention History (Last 7 Days)
| Date | Intention | Accuracy | Action Taken |
|------|-----------|----------|--------------|
| Jan 27 | Billing question | ✅ Correct | Proactive help |
| Jan 25 | Feature discovery | ✅ Correct | Guided tour |
| Jan 23 | Churn risk | ✅ Prevented | Retention offer |
| Jan 20 | Support need | ✅ Correct | Instant assist |
Behavioral Signals Detected
- Hesitation Pattern: Pausing on pricing toggle
- Comparison Mode: Switching between plan tiers
- Decision Readiness: 73% likely to convert today
- Preferred Channel: Email (based on past)
Proactive Recommendations
- [ ] Send personalized enterprise comparison
- [ ] Offer live demo scheduling
- [ ] Prepare ROI calculator link
Pending Tasks
High Priority
- [ ] Follow up with Sarah Chen (Due: 2h)
- Context: Asked about enterprise pricing yesterday
- Action: Send personalized quote based on team size (50 users)
- Memory: Prefers email over calls
- [ ] Resolve billing inquiry (Due: 4h)
- User: Mike Johnson (ID: usr_7k2m8x)
- Issue: Invoice discrepancy for January
- Context: Long-time customer, 3-year history
- [ ] Onboard new user (Due: Today)
- User: Alex Rivera
- Stage: Completed signup, needs first-run guidance
- Preference: Technical background, skip basics
Scheduled
- [ ] Send weekly digest to power users (Tomorrow 9AM)
- [ ] Check in with at-risk accounts (Tomorrow 2PM)
- [ ] Prepare Q1 usage report for enterprise clients
Proactive Opportunities
- [ ] Suggest upgrade to Pro plan for 3 qualifying users
- [ ] Share new feature announcement with beta testers
- [ ] Reconnect with dormant users (7+ days inactive)
Completed Tasks
Today (Jan 28)
- [x] Churn prevention intervention ✨
- User: Jennifer Walsh
- Action: Offered personalized 20% discount
- Result: User retained, renewed for 12 months
- Revenue saved: $2,400 ARR
- [x] Support ticket resolved
- Ticket: #4892 - Password reset issue
- Resolution time: 3 minutes
- User satisfaction: 5/5 stars
- Note: Updated knowledge base with solution
- [x] Proactive feature recommendation
- User: David Kim (Power user)
- Suggested: API integration based on usage pattern
- Outcome: User activated API, +40% engagement
- [x] Cross-session context recall
- Connected 5-day-old conversation seamlessly
- User impressed: "You remembered everything!"
This Week
- [x] 47 support tickets resolved (avg 4.2 min)
- [x] 12 churn preventions successful
- [x] 28 proactive recommendations sent
- [x] 156 context recalls across sessions
Impact Metrics
- Customer satisfaction: 4.8/5
- First response time: 0.3 seconds
- Resolution rate: 94%
Learning from Failures
Recent Issues (Under Review)
Case #1: Missed Escalation Signal
- User: Thomas Brown
- What happened: Didn't detect frustration in message tone
- Root cause: Sarcasm misinterpreted as positive
- Learning: Added sarcasm detection pattern
- Status: Model updated, similar cases now flagged
Case #2: Outdated Information Provided
- User: Rachel Green
- What happened: Gave old pricing (pre-update)
- Root cause: Memory not refreshed after price change
- Learning: Added trigger for pricing memory refresh
- Status: Resolved, pricing sync automated
Case #3: Slow Response on Complex Query
- User: Enterprise client
- What happened: 8 second response (target: <2s)
- Root cause: Multi-database lookup inefficiency
- Learning: Optimized query path, added caching
- Status: Response now <1.5s for similar queries
Improvement Actions
- [ ] Review edge cases weekly
- [ ] Update sentiment detection model
- [ ] Add fallback for slow database responses
- [ ] Implement proactive health checks
Success Stories
Top Wins This Month
🌟 Enterprise Deal Saved
- Client: TechCorp Industries
- Situation: Considering competitor switch
- Agent Action:
- Detected risk from support ticket patterns
- Proactively escalated to success team
- Prepared personalized retention offer
- Outcome: 3-year renewal, $180K ARR
- Key Memory: Remembered CEO's product feedback from 6 months ago
🌟 Viral Support Moment
- User: Influencer with 50K followers
- Situation: Complex integration issue at midnight
- Agent Action:
- Resolved in 4 minutes (24/7 availability)
- Provided step-by-step video guide
- Followed up proactively next morning
- Outcome: User posted positive review, 12 signups attributed
🌟 Proactive Upsell Win
- Users: 8 power users identified
- Pattern Detected: Hitting usage limits regularly
- Agent Action: Personalized upgrade suggestions
- Outcome: 6 upgrades, +$4,320 MRR
Success Metrics
- Revenue influenced: $47,200 this month
- NPS improvement: +12 points
- Proactive saves: 23 at-risk accounts
- User testimonials: 18 new this month
A Three-Layer Memory Engine for Autonomous AI Agents
MemU's cloud-native memory engine enables proactive 24/7 agents with persistent, self-evolving memory. No manual annotation, no complex pipelines — just intelligent memory that grows with your agents and empowers them to act autonomously.
Integrate into your LLM apps
# Install memU SDK
pip install memu-py
# Initialize and use
from memu import MemuClient
import os
memu_client = MemuClient(
api_key=os.getenv("MEMU_API_KEY")
)
memu_client.memorize_conversation(
conversation=conversation,
user_name="User",
agent_name="Assistant"
)Two Powerful APIs for Proactive Agent Memory
Build autonomous agents that remember, learn, and act proactively. Choose the integration level that fits your architecture — from fully managed responses to granular memory control.
Response API
One API call for fully autonomous responses. Your agent retrieves memories, generates context-aware replies, and stores new learnings automatically. Perfect for 24/7 agents that need to learn while they work.
Memory API
Full control over your agent's memory. Store strategic insights, retrieve proactive triggers, and build agents that anticipate user needs before they ask.
Enterprise-grade AI solutions for your business needs
Powerful tools and dedicated support to scale your AI applications with confidence
Commercial License
Full proprietary features, commercial usage rights, and white-labeling options for your enterprise needs
Custom Development
SSO/RBAC integration and dedicated algorithm team for scenario-specific optimization
Intelligence & Analytics
User behavior analysis, real-time monitoring, and automated agent optimization tools
Premium Support
24/7 dedicated support team, custom SLAs, and professional implementation services
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Contact our enterprise team (contact@nevamind.ai) to discuss your specific requirements and get a custom solution.
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Benchmarking 
MemU achieves 92.09% average accuracy in Locomo dataset across all reasoning tasks, significantly outperforming competitors.
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