Core Framework Systems

What You’ll Learn

Advanced Framework Internals:

• LangGraph-native state management with selective persistence strategies

• Pydantic-based context objects with intelligent caching and serialization

• Convention-based registry patterns with lazy loading and component discovery

• Domain-specific prompt builders with dependency injection patterns

• Complete message processing pipeline from Gateway entry to response generation

Prerequisites: Completion of Quick Start Patterns and understanding of LangGraph concepts

Target Audience: Framework developers and advanced capability authors building sophisticated agentic systems

Master the sophisticated internal systems that enable reliable, type-safe agent development. These core systems provide the foundation for building production-ready conversational agents with proper state management, data sharing, and component orchestration.

System Architecture

The Core Framework Systems implement a LangGraph-Native, Type-Safe Architecture with five interconnected components:

🏗️ State Management

Selective persistence strategy with performance optimization and comprehensive lifecycle management.

State Management Architecture

🔄 Context Management

Pydantic-based context objects with automatic serialization and intelligent caching.

Context Management System

📋 Registry & Discovery

Convention-based loading with lazy initialization and type-safe component access.

Registry and Discovery

💬 Prompt Customization

Domain-specific prompt builders with dependency injection and debugging tools.

Prompt Customization

🔀 Message & Execution Flow

Complete message processing from Gateway entry to response generation.

Message and Execution Flow

System Integration

These systems work together to provide a cohesive development experience:

Data Flow

How information moves through the framework:

# State provides the foundation
state = StateManager.create_fresh_state(user_input)

# Context enables data sharing
context = ContextManager(state)
pv_data = context.get_context('PV_ADDRESSES', 'beam_current')

# Registry provides component access
registry = get_registry()
capability = registry.get_capability('pv_value_retrieval')

# Message flow coordinates execution
result = await capability.execute(state)

Performance Strategy

Optimization through intelligent design:

# State: Only context persists across conversations
capability_context_data: Dict[str, Dict[str, Dict[str, Any]]]  # Persists
execution_step_results: Dict[str, Any]                        # Resets

# Context: Object caching and efficient serialization
context = ContextManager(state)  # Cached object reconstruction

# Registry: Lazy loading and singleton patterns
initialize_registry()  # One-time component discovery
registry = get_registry()  # Singleton access

Development Patterns

Common implementation patterns:

# Capability with all systems integration
@capability_node  # Registry registration
class MyCapability(BaseCapability):
    @staticmethod
    async def execute(state: AgentState, **kwargs) -> Dict[str, Any]:
        # Context access
        context = ContextManager(state)
        data = context.get_context('INPUT_DATA', 'key')

        # Processing logic
        result = process_data(data)

        # Context storage
        output = OutputData(results=result)
        return StateManager.store_context(
            state, 'OUTPUT_DATA', 'processed', output
        )