WakeIQX Playground

Experience the 3-layer temporal intelligence architecture with interactive scenarios. Each demo shows how WakeIQX tracks context across time dimensions: Past (WHY), Present (HOW), and Future (WHAT).

🎮 WakeIQX Playground

Experience 3-layer temporal intelligence with interactive scenarios

🔍 Layer 1: Past (WHY)💾 Layer 2: Present (HOW)🔮 Layer 3: Future (WHAT)

🔍

Development Session - Causal Chain

See how Layer 1 (Causality Engine) tracks decision history backwards through time

💾

Memory Tier Evolution

Watch how Layer 2 (Memory Manager) manages context lifecycle over time

🔮

Future Context Prediction

See Layer 3 (Propagation Engine) predict which contexts you'll need next

⚡

Save Context & Reconstruct Reasoning

Create a new context and later understand WHY it was created

🔎

Semantic Search Across Time

Find contexts by keyword and see how they connect across all 3 layers

About These Examples

All scenarios above are based on real MCP tool implementations with:

Actual tool schemas from the WakeIQX codebase
Real 3-layer architecture (Causality, Memory, Propagation)
Authentic responses matching actual tool output format

What You're Seeing:

User Query - Natural language question about context
Tool Invocation - MCP server selects appropriate tool and parameters
Tool Result - Temporal intelligence analysis across layers
WakeIQX Response - Actionable insights with causal chains, memory tiers, and predictions

The 3-Layer Brain Architecture:

┌─────────────────────────────────────────────────────┐
│  Layer 3: PROPAGATION ENGINE (Future - WHAT)        │
│  Predicts which contexts will be needed next         │
└─────────────────────────────────────────────────────┘
                         ↑
┌─────────────────────────────────────────────────────┐
│  Layer 2: MEMORY MANAGER (Present - HOW)            │
│  Manages context relevance and lifecycle            │
└─────────────────────────────────────────────────────┘
                         ↑
┌─────────────────────────────────────────────────────┐
│  Layer 1: CAUSALITY ENGINE (Past - WHY)             │
│  Tracks decision history and reasoning chains       │
└─────────────────────────────────────────────────────┘

Key Features Demonstrated:

🔍 Layer 1: Causality Engine - Backward causal chain reconstruction
💾 Layer 2: Memory Manager - Memory tier evolution (ACTIVE → RECENT → ARCHIVED → EXPIRED)
🔮 Layer 3: Propagation Engine - Multi-factor prediction scoring (40% temporal, 30% causal, 30% frequency)
⚡ Cross-Layer Integration - How all three layers work together
📊 Temporal Analytics - Statistics and insights across time dimensions

Memory Tier System:

Tier	Age	Purpose	Example
🔥 ACTIVE	< 1 hour	Hot, frequently accessed	Current work session
⚡ RECENT	1-24 hours	Warm, recently used	Today's context
📦 ARCHIVED	1-30 days	Cold, aging	Last week's work
❄️ EXPIRED	> 30 days	Pruning candidates	Old contexts

Prediction Scoring Algorithm:

Combined Score = (Temporal × 0.4) + (Causal × 0.3) + (Frequency × 0.3)

Temporal (40%): Recent access patterns and momentum
Causal (30%): Position in causal chain (root causes score higher)
Frequency (30%): Overall popularity and access count

Want to Try the Real Thing?

To use WakeIQX with your own projects:

Install Claude Desktop - Required for MCP integration
Clone the Repo - Open source on GitHub
Follow Setup Guide - 5-minute configuration with Cloudflare D1

Real-World Use Cases

🔍 Development Session Tracking

typescript

// Save context with causality
save_context({
  project: "my-app",
  summary: "Implemented OAuth2 authentication",
  context: "Chose OAuth2 over JWT for third-party integrations",
  causedBy: "ctx_security_discussion_123",
  actionType: "implementation",
  rationale: "Need social login providers"
})

// Later: Why did I choose OAuth2?
reconstruct_reasoning({
  snapshotId: "ctx_oauth_implementation_456"
})
// → See the full causal chain back to original discussion

💾 Memory Management

typescript

// Check memory health
get_memory_stats({
  project: "my-app"
})
// → See tier distribution, identify expired contexts

// Clean up old contexts
prune_expired_contexts({
  limit: 50
})
// → Remove contexts older than 30 days

🔮 Proactive Context Loading

typescript

// Get prediction scores
get_high_value_contexts({
  project: "my-app",
  minScore: 0.6,
  limit: 10
})
// → Pre-fetch likely-needed contexts before starting work

// Update predictions
update_predictions({
  project: "my-app",
  staleThreshold: 3600 // 1 hour
})
// → Recalculate scores for fresh recommendations

⚡ Full Workflow Example

typescript

// Morning: Start work session
const recentContexts = await load_context({
  project: "my-app",
  limit: 5
})

// Identify what you were working on
const causalChain = await build_causal_chain({
  snapshotId: recentContexts[0].id
})

// Get predictions for today's work
const predictions = await get_high_value_contexts({
  project: "my-app",
  minScore: 0.7
})

// Work throughout the day, saving contexts...

// Evening: Check memory health
const stats = await get_memory_stats({
  project: "my-app"
})

Technical Deep Dive

Interested in how the 3-layer architecture works under the hood?

Architecture Overview - Complete system design
Layer 1: Causality Engine - Decision history tracking
Layer 2: Memory Manager - Lifecycle management
Layer 3: Propagation Engine - Prediction scoring
Tools Documentation - All 12 MCP tools
API Reference - Service interfaces and entities

Ready to add temporal intelligence to your AI workflows?

Past → Present → Future | Temporal Intelligence for AI Agents 🐦

Get Started →

WakeIQX Playground ​