Personalized Learning AI
See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent
Advanced Multi-Agent Learning System
BOFU: Production LangGraph with Router → Specialized Agents → Aggregation
1. Select Analysis Scenario
2. Monday's Prompt
3. Run Multi-Agent Workflow
MCP Tool Use Pattern
TOFU: LLM calls learning tools via Model Context Protocol - see real tool execution
Automation Task
User query → LLM decides tool → Executes get_student_performance() → Returns data
Terminal ready. Run simulation to begin.
RAG-Based Learning Search
MOFU: Search 100+ learning resources with vector similarity
Search Query
"Search 500+ Resources for Visual Learner"
Embed query ('visual factoring resources for struggling 8th grader') → Search 500 resource vectors → Retrieve top 5 chunks → Generate personalized recommendations
RAG Vector Search Network
Search Results
Run visualization to see results
Multi-Tool Learning Orchestration
MOFU: Multiple learning tools working together automatically
ROI Analysis
Orchestrator calls: LMS_data_collector(100_students) → Learning_analytics(data) → Gap_detector(analytics) → Resource_recommender(gaps) → Alert_system(critical_cases) - all in parallel where possible
Time to Complete
Cost per Analysis
Data Points Analyzed
Accuracy Rate
Reports per Day
Error Rate
Ready for Production Personalized Learning System?
We'll build your custom system: MCP tools (TOFU) → RAG search (MOFU) → Multi-tool (MOFU) → Multi-agent (BOFU). From demos to deployment in 8 weeks. Start with 100 students, scale to 10,000+.
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