Agent Memory for AI.
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MemU is an agent memory layer for LLM applications,
enabling autonomous, intelligent memory management for AI agents
Higher accuracyFaster retrievalLower cost
1. Create customer memories
2. Add relationship data
3. Query based on context
Documentation
User Profile Analysis
John Smith - Senior Frontend Developer
Basic Information
- Company: TechCorp Inc.
- Role: Lead Frontend Developer (Level: Senior III)
- Location: San Francisco, CA
- Team: Manages 3 junior developers
- Experience: 3.2 years at company
Contact & Preferences
- Email: john.smith@techcorp.com
- Slack: @johnsmith
- Meeting Time: Early morning (9-11 AM PST)
- Communication: Prefers email for formal, Slack for quick questions
Technical Skills
Expert Level:
- JavaScript, TypeScript, React.js, Next.js
- Webpack, Vite, Jest, Cypress
Proficient Level:
- Node.js, PostgreSQL, AWS, Docker
Learning Areas:
- WebAssembly, Micro-frontends, Performance Optimization
Current Projects
- User Authentication Redesign (70% complete)
- React, TypeScript, OAuth 2.0
- Due: Feb 2024
- Performance Optimization Initiative
- Focus: Core Web Vitals improvement
- Expected: 40% faster load times
Work Style
Strengths:
- Detail-oriented and proactive
- Excellent mentor and communicator
- Strong advocate for code quality
Preferences:
- Peak productivity: 9 AM - 1 PM PST
- Thorough code reviews with educational comments
- Data-driven discussions
Performance Metrics
- Code Review Quality: 9.2/10
- On-time Delivery: 95%
- Bug Rate: 0.3/1000 lines (team avg: 1.2)
- Team Satisfaction: 4.8/5
Career Goals
- Short-term: Lead frontend architecture, expand team
- Medium-term: Transition to Engineering Manager
- Long-term: Head of Frontend Engineering
Event Timeline
Recent Activity (Last 7 Days)
2024-01-20 - Critical Support Incident
Issue: Authentication timeout affecting 15% of users
Severity: P1 (Critical)
Resolution: Emergency patch deployed in 1h 15min
Status: ✅ Resolved
2024-01-20 - Q1 Planning Session
Type: Strategic Planning Meeting
Organizer: Sarah Chen
Key Decisions:
- ✅ Feature freeze on Feb 15, 2024
- ✅ Client demo scheduled for Feb 1, 2024
- ✅ Performance optimization sprint (Feb 19-Mar 5)
2024-01-19 - Production Deployment
Version: v2.3.1
Changes: 7 bug fixes, performance improvements, security updates
Status: ✅ Successful deployment
2024-01-19 - Code Review Session
Reviewer: John Smith
PRs Reviewed: 3 pull requests
Total Changes: +847 -234 lines of code
2024-01-19 - Client Strategy Call
Client: ABC Corporation
Outcome: ✅ Contract renewal approved for 24 months
Growth: 20% increase in license count (500 → 600 users)
Upcoming Events
2024-01-22 (Monday)
- 09:00: Sprint 24 Daily Standup
- 10:30: New Employee Onboarding (2 new hires)
- 14:00: Client demo preparation meeting
2024-01-23 (Tuesday)
- 09:00: All-hands company meeting
- 11:00: Performance optimization planning session
- 13:00: Lunch & Learn: "Advanced React Patterns"
Event Statistics (Last 30 Days)
- Total Events: 47
- Categories: Development (38%), Meetings (26%), Support (19%)
- Critical Events: 3 (All resolved)
- Average Resolution Time: 2.4 hours
Memory Insights Dashboard
Executive Summary
- Total Memories: 12,847 active entries
- Query Volume: 3,247 searches/day (↑15% from last month)
- System Health: 99.7% uptime, 0.3s avg response time
- User Satisfaction: 4.6/5
Usage Analytics
Memory Categories
- Customer Interactions - 5,781 entries (45%)
- Team Communications - 3,597 entries (28%)
- Project Documentation - 2,312 entries (18%)
- Technical Notes - 1,157 entries (9%)
Peak Usage Hours
- Primary Peak: 10:00 AM - 12:00 PM (847 avg queries/hour)
- Secondary Peak: 2:00 PM - 4:00 PM (623 avg queries/hour)
Performance Metrics
- Average Response Time: 0.34s (Target: <0.5s)
- Success Rate: 94.2% (↑2.1% from last month)
- Cache Hit Rate: 78.3%
Most Searched Topics
- Customer preferences - 1,234 searches
- Project deadlines - 967 searches
- Team member contact info - 789 searches
- Meeting summaries - 656 searches
- Bug resolution status - 543 searches
Top Users by Query Volume
- John Smith - 247 queries/week (React, code reviews)
- Sarah Chen - 198 queries/week (Team performance, projects)
- Mike Chen - 187 queries/week (Timelines, resources)
AI Insights
Pattern Recognition
- Code reviews spike after Monday standups (78% confidence)
- Team meetings most productive on Tuesdays (92% confidence)
- Performance issues correlate with code reviews (87% confidence)
Predictive Analytics
- Authentication project: 87% likely to complete on time
- Performance optimization: 94% likely to finish early
- John Smith approaching capacity (85% utilization)
Business Impact
- Time Saved: 14.7 hours/week per team member
- ROI: 347% return on investment
- Faster Decision Making: 45% improvement
- Knowledge Transfer: 89% reduction in repetitive questions
Memory Connections Network
Network Overview
- Total Connections: 47,892 active links
- Network Density: 0.73 (highly interconnected)
- Strong Connections (weight >0.8): 12,456 (26%)
- Emerging Connections (last 7 days): 347 new links
Key People Connections
John Smith - Frontend Lead
- Total Connections: 2,847 memories (22% of network)
- Strongest Topics: React (341 connections), TypeScript (298), Code Reviews (267)
- Key Collaborators: Sarah Chen (156 memories), Rachel Green (134), Lisa Wong (98)
- Influence Score: 8.7/10
Sarah Chen - Engineering Manager
- Total Connections: 3,234 memories (25% of network)
- Strongest Topics: Team Management (445), Project Planning (387), Performance Reviews (298)
- Leadership Style: Data-driven (89% decisions backed by metrics)
Mike Chen - Project Manager
- Total Connections: 2,156 memories (17% of network)
- Strongest Topics: Timeline Management (398), Resource Allocation (356), Risk Assessment (289)
- Meeting Frequency: 3.2 meetings/day average
Topic Clusters
Authentication System (Central Hub)
Authentication System (847 connections)├── OAuth Implementation (234 memories)
├── Session Management (198 memories)
├── Password Security (156 memories)
└── User Permissions (123 memories)
React Development Ecosystem
React Component Library (1,234 connections)├── Design System Integration (345 memories)
├── Performance Optimization (298 memories)
├── Testing Infrastructure (267 memories)
└── TypeScript Integration (189 memories)
Auto-Generated Insights
Recent Pattern Discovery (Last 30 Days)
- Performance Issues ↔ Code Reviews (87% confidence)
- Customer Feedback ↔ Feature Development (92% confidence)
- Bug Reports ↔ Testing Procedures (79% confidence)
Cross-Team Collaboration
- Engineering ↔ Product: 2,456 shared memories (8.4/10 score)
- Customer Success ↔ Engineering: 92% P1 issues resolved within SLA
- Design ↔ Development: 91% design specifications met
Network Health
- High Quality connections (0.8-1.0): 67%
- Knowledge Distribution Score: 7.2/10
- Single Points of Failure: 3 identified (mitigation planned)
A Three-Layer Memory Engine Built for Real-World AI Applications
MemU’s cloud-native memory engine delivers long-term, interpretable memory out of the box—no manual annotation, no custom infrastructure, no complex pipelines. It transforms raw multimodal data into structured, queryable memory through a scalable three-layer architecture designed for real application workloads.
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"
)Your All-in-One AI Memory Platform
Experience MemU’s long-term memory capabilities instantly on our cloud platform. Integrate easily with your AI products and see how persistent, self-evolving memories can make your AI more intelligent and responsive.
Response API
Response API combines LLM output, memory retrieval, and context assembly into one request. With Response API, you can build agents that understand users over time without managing separate steps or extra logic.
Memory API
Memory API gives developers full control over long-term memory. Store, retrieve, search, and manage structured memory through flexible endpoints that plug directly into your LLM workflow. Build agents that evolve over time — exactly the way you design them.
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
Ready to scale your AI applications?
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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