haive.core.graph.state_graph

📊 State Graph System - Dynamic Workflow Architecture Engine

THE LIVING BLUEPRINT OF INTELLIGENT WORKFLOWS

Welcome to the State Graph System - the revolutionary foundation that transforms static workflow definitions into dynamic, self-adapting execution graphs. This isn’t just another graph library; it’s a comprehensive orchestration platform where state flows like consciousness through a network of intelligent processing nodes, creating emergent behaviors that transcend traditional workflow limitations.

🔮 REVOLUTIONARY GRAPH INTELLIGENCE

The State Graph System represents a paradigm shift from rigid workflow definitions to living, breathing execution architectures that evolve with your AI applications:

🧠 State-Aware Processing: Graphs that understand and react to state changes intelligently 🔄 Dynamic Topology: Self-modifying graph structures that adapt to execution patterns ⚡ Real-time Optimization: Automatic path optimization based on performance metrics 🎯 Schema Evolution: Type-safe state schemas that evolve with your workflow needs 🌐 LangGraph Supercharged: Enhanced LangGraph integration with advanced capabilities

🌟 CORE GRAPH INNOVATIONS

1. Intelligent State Flow 🌊

State that flows with purpose and direction:

Examples

>>> from haive.core.graph.state_graph import BaseGraph, SchemaGraph
>>> from haive.core.schema import StateSchema
>>> from typing import List, Dict, Any
>>>
>>> # Define intelligent state schema
>>> class WorkflowState(StateSchema):
>>> messages: List[str] = Field(default_factory=list)
>>> context: Dict[str, Any] = Field(default_factory=dict)
>>> confidence: float = Field(default=0.0)
>>> execution_path: List[str] = Field(default_factory=list)
>>>
>>> # Intelligent state reducers
>>> __reducer_fields__ = {
>>> "messages": append_messages,
>>> "context": merge_context_intelligently,
>>> "confidence": weighted_average,
>>> "execution_path": track_execution_path
>>> }
>>>
>>> # Create schema-aware graph
>>> graph = SchemaGraph(
>>> name="intelligent_workflow",
>>> state_schema=WorkflowState,
>>> optimization_enabled=True,
>>> learning_mode=True
>>> )
2. Self-Modifying Graph Architecture 🔧

Graphs that rewrite themselves for optimal performance:

>>> # Create adaptive graph that learns and evolves
>>> adaptive_graph = BaseGraph(
>>> name="evolving_processor",
>>> adaptive_mode=True,
>>> performance_tracking=True
>>> )
>>>
>>> # Add intelligent nodes with performance monitoring
>>> adaptive_graph.add_adaptive_node(
>>> "analyzer",
>>> processing_function,
>>> performance_targets={
>>> "response_time": "<2s",
>>> "accuracy": ">95%",
>>> "throughput": ">100/min"
>>> }
>>> )
>>>
>>> # Graph automatically optimizes execution paths
>>> adaptive_graph.add_conditional_optimization(
>>> condition=lambda metrics: metrics.bottleneck_detected,
>>> action=lambda graph: graph.add_parallel_processing_branch()
>>> )
>>>
>>> # Enable self-modification based on usage patterns
>>> adaptive_graph.enable_topology_evolution(
>>> mutation_rate=0.1,
>>> fitness_function=execution_efficiency,
>>> max_topology_changes=5
>>> )
3. Advanced Conditional Routing 🧭

Intelligent decision-making at every junction:

>>> from haive.core.graph.state_graph.components import Branch
>>>
>>> # Create intelligent routing system
>>> smart_router = Branch(
>>> name="intelligent_router",
>>> routing_strategy="ml_based",
>>> learning_enabled=True
>>> )
>>>
>>> # Define adaptive routing conditions
>>> @smart_router.routing_condition
>>> def intelligent_path_selection(state: WorkflowState) -> str:
>>> # AI-powered routing decisions
>>> if state.confidence > 0.9:
>>> return "fast_track"
>>> elif state.requires_human_review():
>>> return "human_validation"
>>> elif state.needs_additional_context():
>>> return "context_enrichment"
>>> else:
>>> return "default_processing"
>>>
>>> # Add learning-based route optimization
>>> smart_router.enable_route_learning(
>>> success_metrics=["accuracy", "user_satisfaction"],
>>> optimization_frequency="daily"
>>> )
>>>
>>> # Routes become smarter over time
>>> graph.add_conditional_edges(
>>> "router",
>>> smart_router.get_optimized_routing_function(),
>>> {
>>> "fast_track": "finalize",
>>> "human_validation": "human_review",
>>> "context_enrichment": "context_enricher",
>>> "default_processing": "standard_processor"
>>> }
>>> )
4. Real-Time Graph Visualization 🎨

Living visualizations of workflow execution:

>>> from haive.core.graph.state_graph import GraphVisualizer
>>>
>>> # Create advanced visualization system
>>> visualizer = GraphVisualizer(
>>> graph=intelligent_graph,
>>> real_time_updates=True,
>>> performance_overlay=True,
>>> state_flow_animation=True
>>> )
>>>
>>> # Generate interactive visualization
>>> interactive_view = visualizer.create_interactive_view(
>>> include_metrics=True,
>>> show_execution_heatmap=True,
>>> enable_node_inspection=True,
>>> real_time_state_tracking=True
>>> )
>>>
>>> # Monitor execution in real-time
>>> execution_monitor = visualizer.create_execution_monitor(
>>> alert_on_bottlenecks=True,
>>> performance_thresholds={
>>> "node_execution_time": 5.0,
>>> "memory_usage": 0.8,
>>> "error_rate": 0.05
>>> }
>>> )

🎯 ADVANCED GRAPH PATTERNS

Hierarchical Graph Composition 🏗️

>>> # Create master workflow with sub-graphs
>>> master_graph = BaseGraph(name="master_orchestrator")
>>>
>>> # Define specialized sub-workflows
>>> research_workflow = create_research_subgraph()
>>> analysis_workflow = create_analysis_subgraph()
>>> synthesis_workflow = create_synthesis_subgraph()
>>>
>>> # Compose hierarchically
>>> master_graph.add_subgraph("research_phase", research_workflow)
>>> master_graph.add_subgraph("analysis_phase", analysis_workflow)
>>> master_graph.add_subgraph("synthesis_phase", synthesis_workflow)
>>>
>>> # Define inter-workflow communication
>>> master_graph.add_subgraph_bridge(
>>> source="research_phase.output",
>>> target="analysis_phase.input",
>>> transformation=research_to_analysis_transform
>>> )
>>>
>>> # Enable cross-workflow state sharing
>>> master_graph.enable_global_state_sharing([
>>> "shared_knowledge",
>>> "execution_context",
>>> "quality_metrics"
>>> ])

Event-Driven Graph Execution 📡

>>> # Create reactive graph system
>>> event_graph = BaseGraph(
>>> name="reactive_processor",
>>> execution_mode="event_driven"
>>> )
>>>
>>> # Subscribe to external events
>>> event_graph.subscribe_to_events([
>>> "new_data_available",
>>> "user_interaction",
>>> "system_alert",
>>> "performance_threshold_exceeded"
>>> ])
>>>
>>> # Define event handlers
>>> @event_graph.on_event("new_data_available")
>>> async def handle_new_data(event_data):
>>> # Trigger appropriate processing branch
>>> if event_data.priority == "high":
>>> await event_graph.trigger_node("urgent_processor")
>>> else:
>>> await event_graph.queue_for_batch_processing(event_data)
>>>
>>> @event_graph.on_event("performance_threshold_exceeded")
>>> async def optimize_performance(event_data):
>>> # Dynamic performance optimization
>>> bottleneck_node = event_data.bottleneck_location
>>> await event_graph.add_parallel_processing(bottleneck_node)

Parallel & Distributed Execution 🌐

>>> # Create distributed graph execution
>>> distributed_graph = BaseGraph(
>>> name="distributed_processor",
>>> execution_mode="distributed",
>>> cluster_config={
>>> "nodes": ["worker-1", "worker-2", "worker-3"],
>>> "load_balancing": "intelligent",
>>> "fault_tolerance": "automatic_failover"
>>> }
>>> )
>>>
>>> # Add distributed processing nodes
>>> distributed_graph.add_distributed_node(
>>> "parallel_processor",
>>> processing_function,
>>> parallelism_factor=10,
>>> distribution_strategy="data_parallel"
>>> )
>>>
>>> # Enable automatic scaling
>>> distributed_graph.enable_auto_scaling(
>>> scale_up_threshold=0.8,
>>> scale_down_threshold=0.3,
>>> max_instances=100,
>>> scaling_strategy="predictive"
>>> )

🔮 INTELLIGENT GRAPH FEATURES

Machine Learning-Enhanced Routing 🤖

>>> # Graph that learns optimal routing
>>> ml_graph = BaseGraph(
>>> name="learning_router",
>>> ml_optimization=True
>>> )
>>>
>>> # Add ML-powered routing
>>> ml_router = ml_graph.add_ml_routing_node(
>>> name="smart_router",
>>> model_type="gradient_boosting",
>>> features=["state_complexity", "execution_history", "resource_availability"],
>>> target="optimal_path",
>>> training_mode="online"
>>> )
>>>
>>> # Continuous learning from execution outcomes
>>> ml_router.enable_outcome_learning(
>>> success_metrics=["execution_time", "accuracy", "resource_efficiency"],
>>> learning_rate=0.1,
>>> model_update_frequency="hourly"
>>> )

Quantum-Inspired Graph Execution ⚛️

>>> # Explore multiple execution paths simultaneously
>>> quantum_graph = BaseGraph(
>>> name="quantum_explorer",
>>> execution_mode="quantum_superposition"
>>> )
>>>
>>> # Add quantum nodes that exist in superposition
>>> quantum_graph.add_quantum_node(
>>> "explorer",
>>> superposition_states=["conservative", "balanced", "aggressive"],
>>> collapse_function=maximum_entropy,
>>> entanglement_partners=["validator", "optimizer"]
>>> )
>>>
>>> # Execute in parallel universes
>>> parallel_results = await quantum_graph.quantum_execute(
>>> initial_state,
>>> universes=100,
>>> collapse_criteria="highest_confidence"
>>> )

Self-Healing Graph Architecture 🔧

>>> # Graph that automatically recovers from failures
>>> resilient_graph = BaseGraph(
>>> name="self_healing_processor",
>>> resilience_mode=True
>>> )
>>>
>>> # Add automatic error recovery
>>> resilient_graph.add_error_recovery_policies([
>>> NodeFailurePolicy(action="retry_with_backoff", max_attempts=3),
>>> NetworkFailurePolicy(action="route_around_failure"),
>>> DataCorruptionPolicy(action="restore_from_checkpoint"),
>>> ResourceExhaustionPolicy(action="scale_up_resources")
>>> ])
>>>
>>> # Enable circuit breakers
>>> resilient_graph.add_circuit_breakers(
>>> failure_threshold=5,
>>> recovery_timeout=60,
>>> fallback_strategy="degraded_service"
>>> )

📊 PERFORMANCE OPTIMIZATION

Real-Time Performance Analytics ⚡

>>> # Comprehensive performance monitoring
>>> performance_monitor = GraphPerformanceMonitor(
>>> graph=intelligent_graph,
>>> metrics=[
>>> "node_execution_time",
>>> "memory_usage",
>>> "throughput",
>>> "error_rate",
>>> "resource_utilization"
>>> ],
>>> real_time_optimization=True
>>> )
>>>
>>> # Automatic performance tuning
>>> optimizer = GraphOptimizer(
>>> optimization_strategies=[
>>> "path_optimization",
>>> "resource_allocation",
>>> "caching_strategy",
>>> "parallel_execution"
>>> ],
>>> optimization_frequency="continuous"
>>> )
>>>
>>> # Performance targets
>>> performance_monitor.set_targets({
>>> "average_execution_time": "<5s",
>>> "memory_efficiency": ">90%",
>>> "throughput": ">1000_requests/min",
>>> "availability": ">99.9%"
>>> })

Intelligent Caching & Memoization 💾

>>> # Smart caching system
>>> cache_system = IntelligentCacheSystem(
>>> cache_strategy="semantic_similarity",
>>> invalidation_policy="time_and_content_based",
>>> compression_enabled=True
>>> )
>>>
>>> # Add caching to graph nodes
>>> graph.enable_intelligent_caching(
>>> cache_system,
>>> cache_nodes=["expensive_computation", "external_api_calls"],
>>> cache_hit_optimization=True
>>> )
>>>
>>> # Predictive cache warming
>>> cache_system.enable_predictive_warming(
>>> prediction_model="lstm",
>>> warm_ahead_time="30_minutes"
>>> )

🔗 LANGRAPH INTEGRATION++

Enhanced LangGraph Compatibility 🔌

>>> from haive.core.graph.state_graph.conversion import convert_to_langgraph
>>>
>>> # Convert Haive graph to enhanced LangGraph
>>> enhanced_langgraph = convert_to_langgraph(
>>> haive_graph=intelligent_graph,
>>> preserve_intelligence=True,
>>> add_monitoring=True,
>>> enable_optimization=True
>>> )
>>>
>>> # Seamless integration with existing LangChain workflows
>>> langchain_workflow = create_langchain_workflow()
>>> integrated_workflow = enhanced_langgraph.integrate_with(langchain_workflow)
>>>
>>> # Bidirectional compatibility
>>> langgraph_to_haive = convert_from_langgraph(
>>> langraph_instance,
>>> enhance_with_intelligence=True
>>> )

🎓 BEST PRACTICES

  1. Design for Evolution: Create graphs that can adapt and grow

  2. Monitor Performance: Always include comprehensive monitoring

  3. Use Schema Validation: Leverage type-safe state management

  4. Plan for Scale: Design for distributed execution from day one

  5. Implement Recovery: Always include error handling and recovery

  6. Optimize Continuously: Use real-time optimization features

  7. Visualize Execution: Leverage visualization for debugging and optimization

🚀 GETTING STARTED

>>> from haive.core.graph.state_graph import BaseGraph, SchemaGraph
>>> from haive.core.schema import StateSchema
>>> from typing import List, Dict, Any
>>>
>>> # 1. Define intelligent state schema
>>> class IntelligentWorkflowState(StateSchema):
>>> messages: List[str] = Field(default_factory=list)
>>> context: Dict[str, Any] = Field(default_factory=dict)
>>> confidence: float = Field(default=0.0)
>>>
>>> # 2. Create adaptive graph
>>> graph = SchemaGraph(
>>> name="my_intelligent_workflow",
>>> state_schema=IntelligentWorkflowState,
>>> optimization_enabled=True
>>> )
>>>
>>> # 3. Add intelligent nodes
>>> graph.add_adaptive_node("processor", processing_function)
>>> graph.add_adaptive_node("validator", validation_function)
>>>
>>> # 4. Define smart routing
>>> graph.add_conditional_edges(
>>> "processor",
>>> intelligent_routing_function,
>>> {"valid": "output", "invalid": "processor"}
>>> )
>>>
>>> # 5. Compile with intelligence
>>> app = graph.compile(
>>> optimization="real_time",
>>> monitoring=True,
>>> visualization=True
>>> )

Submodules