Skip to main content

Comprehensive Graph Demo

This example demonstrates an advanced StateGraph workflow that showcases intelligent query routing, parallel execution, and memory management - a production-ready implementation of complex multi-step processes.

🎯 Core Functionality​

Intelligent Query Routing System:

  • LLM-powered intent classification - Automatically categorizes user queries into: general_qa, short_term_trend, macro_trend, or deep_research
  • Dynamic routing logic - Routes queries to specialized analysis paths based on detected intent
  • Context-aware decision making - Uses conversation history and market context for routing decisions

True Parallel Data Processing:

  • Concurrent data fetching - Simultaneously retrieves data from multiple timeframes (15m, 30m, 1h, 4h, daily, weekly)
  • Real-time market data - Integrates with live cryptocurrency APIs for accurate, current information
  • Performance optimization - Parallel execution significantly reduces total processing time

Advanced Memory Management:

  • Persistent conversation context - Maintains user preferences and analysis history across sessions
  • Intelligent memory updates - Automatically stores learned patterns and market insights
  • State preservation - Saves analysis results and routing decisions for future reference

🚀 Key Features Demonstrated​

  • StateGraph Architecture - Complete implementation of SpoonOS graph system
  • LLM Integration - Advanced prompt engineering and response processing
  • Tool Orchestration - Seamless integration of multiple data sources
  • Error Handling - Robust error recovery and fallback mechanisms
  • Performance Monitoring - Built-in metrics and execution tracking

📋 Prerequisites​

# Required environment variables
export OPENAI_API_KEY="your-openai-api-key"
export TAVILY_API_KEY="your-tavily-api-key"       # Search engine

🏃 Quick Start​

# Navigate to examples directory
cd spoon-cookbook/example

# Install dependencies
pip install -r requirements.txt

# Run the comprehensive graph demo
python comprehensive_graph_demo.py

🔍 What to Observe​

Execution Flow:

  • Watch how the system intelligently routes queries to appropriate analysis paths
  • Observe parallel data fetching across multiple timeframes simultaneously
  • See how memory is loaded and updated throughout the process

Performance Metrics:

  • Monitor execution times for different routing paths
  • Compare sequential vs parallel processing performance
  • Track memory usage and optimization

Advanced Behaviors:

  • See how the LLM makes routing decisions based on query intent
  • Watch real-time data integration from multiple sources
  • Observe how the system maintains context across complex workflows

📁 Source Code & Documentation​

  • GitHub Link: Comprehensive Graph Demo
  • Related Files:
    • spoon-core/examples/comprehensive_graph_demo.py - Core implementation
    • spoon-core/spoon_ai/graph/ - Graph system components
    • docs/core-concepts/graph-system.md - Graph system documentation

🎓 Learning Objectives​

This example teaches you:

  • How to build complex, multi-step workflows using StateGraph
  • Advanced LLM integration patterns and prompt engineering
  • Parallel processing techniques for performance optimization
  • Memory management and state persistence in long-running processes
  • Error handling and recovery in distributed systems

💡 Best Practices Demonstrated​

  • Modular Design - Clean separation of concerns with focused nodes
  • Scalable Architecture - Easy to extend with new analysis types
  • Resource Efficiency - Optimized for both speed and memory usage
  • Maintainable Code - Well-documented and structured implementation

🚀 Key Features Demonstrated​

  • StateGraph Architecture - Complete implementation of SpoonOS graph system
  • LLM Integration - Advanced prompt engineering and response processing
  • Tool Orchestration - Seamless integration of multiple data sources
  • Error Handling - Robust error recovery and fallback mechanisms
  • Performance Monitoring - Built-in metrics and execution tracking

📋 Prerequisites​

# Required environment variables
export OPENAI_API_KEY="your-openai-api-key"
export CRYPTO_API_KEY="your-crypto-api-key"  # For market data

🏃 Quick Start​

# Navigate to examples directory
cd spoon-cookbook/example

# Install dependencies
pip install -r requirements.txt

# Run the comprehensive graph demo
python comprehensive_graph_demo.py

🔍 What to Observe​

Execution Flow:

  • Watch how the system intelligently routes queries to appropriate analysis paths
  • Observe parallel data fetching across multiple timeframes simultaneously
  • See how memory is loaded and updated throughout the process

Performance Metrics:

  • Monitor execution times for different routing paths
  • Compare sequential vs parallel processing performance
  • Track memory usage and optimization

Advanced Behaviors:

  • See how the LLM makes routing decisions based on query intent
  • Watch real-time data integration from multiple sources
  • Observe how the system maintains context across complex workflows

📁 Source Code & Documentation​

  • GitHub Link: Comprehensive Graph Demo
  • Related Files:
    • spoon-core/examples/comprehensive_graph_demo.py - Core implementation
    • spoon-core/spoon_ai/graph/ - Graph system components
    • docs/core-concepts/graph-system.md - Graph system documentation

🎓 Learning Objectives​

This example teaches you:

  • How to build complex, multi-step workflows using StateGraph
  • Advanced LLM integration patterns and prompt engineering
  • Parallel processing techniques for performance optimization
  • Memory management and state persistence in long-running processes
  • Error handling and recovery in distributed systems

💡 Best Practices Demonstrated​

  • Modular Design - Clean separation of concerns with focused nodes
  • Scalable Architecture - Easy to extend with new analysis types
  • Resource Efficiency - Optimized for both speed and memory usage
  • Maintainable Code - Well-documented and structured implementation