Glossary

This glossary defines the core terms and concepts used throughout the Semantic Kernel Graph documentation. Each term is defined in simple, accessible language while maintaining technical accuracy.

Core Graph Concepts

Graph: A computational structure composed of nodes connected by edges that defines the flow of execution in a workflow. The graph orchestrates how data flows between different processing steps.

Node: A fundamental unit of computation in a graph that represents a single processing step. Each node can execute functions, make decisions, or perform specific operations.

Edge: A connection between two nodes that defines the path of execution flow. Edges can be conditional (only traversed when specific conditions are met) or unconditional (always traversed).

Execution: The process of running a graph by traversing nodes and edges according to the defined flow, processing data at each step.

State: The current data and context maintained throughout graph execution, including input arguments, intermediate results, and execution metadata.

Node Types and Behavior

FunctionGraphNode: A node that encapsulates and executes a Semantic Kernel function, providing graph-aware behavior around existing ISKFunction instances.

ActionGraphNode: A specialized node that automatically discovers and executes appropriate tools or actions based on the current context and available functions.

ConditionalGraphNode: A node that makes routing decisions based on conditions, enabling dynamic workflow paths and branching logic.

ReActLoopGraphNode: A node that implements the Reasoning → Acting → Observing pattern for iterative problem-solving and decision-making.

ObservationGraphNode: A node that monitors and records execution results, providing visibility into workflow performance and outcomes.

Edge and Routing Concepts

ConditionalEdge: A directional connection between nodes that includes a predicate function to determine whether the edge should be traversed during execution.

Unconditional Edge: An edge that is always traversed, providing a direct path between nodes without conditional logic.

Routing: The process of determining which node to execute next based on the current state and edge conditions.

Dynamic Routing: Advanced routing that makes decisions at runtime based on content analysis, similarity matching, or other contextual factors.

State Management

GraphState: A typed wrapper for KernelArguments that serves as the foundation for graph execution, providing execution tracking and state manipulation capabilities.

KernelArguments: The underlying data container that holds input parameters, intermediate results, and execution context throughout the workflow.

State Serialization: The process of converting graph state to a persistent format for storage, transmission, or recovery purposes.

State Validation: The verification that state data meets required constraints and format requirements before processing.

Execution and Control Flow

GraphExecutor: The main orchestrator for graph execution that manages the execution flow, navigation, and coordination of graph nodes.

Execution Context: The runtime environment that maintains execution state, metrics, and provides coordination services during graph execution.

Execution Path: The sequence of nodes visited during a graph run, representing the actual flow taken through the workflow.

Execution Status: The current state of a graph execution, such as NotStarted, Running, Paused, Completed, or Failed.

Cancellation: The ability to gracefully stop graph execution before completion, respecting cancellation tokens and cleanup procedures.

Checkpointing and Recovery

Checkpoint: A saved snapshot of graph execution state at a specific point, enabling recovery and resumption of workflows.

Checkpointing: The process of creating, storing, and managing execution checkpoints for resilience and recovery purposes.

Recovery: The ability to restore graph execution from a previous checkpoint, continuing workflow processing from the saved state.

State Persistence: The long-term storage of graph state and checkpoints, typically using memory services or external storage systems.

Checkpoint Validation: The verification that saved checkpoints are complete, consistent, and can be successfully restored.

Streaming and Real-time Execution

Streaming Execution: Real-time graph execution that provides immediate feedback and event streams as nodes complete processing.

Event Stream: A real-time sequence of execution events that provides visibility into graph processing as it happens.

GraphExecutionEvent: A structured notification about a specific occurrence during graph execution, such as node start, completion, or errors.

Backpressure: The mechanism for controlling the rate of event production to match consumer processing capabilities.

Real-time Monitoring: Live observation of graph execution progress, performance metrics, and status updates.

Performance and Observability

Metrics: Quantitative measurements of graph execution performance, including timing, throughput, and resource utilization.

Performance Monitoring: Continuous tracking of execution metrics to identify bottlenecks and optimize workflow performance.

Telemetry: The collection and transmission of execution data for monitoring, debugging, and analysis purposes.

Tracing: Detailed tracking of execution flow through individual nodes and edges for debugging and performance analysis.

Logging: Structured recording of execution events, errors, and diagnostic information for operational visibility.

Error Handling and Resilience

Error Recovery: The ability to detect, handle, and recover from execution failures while maintaining workflow integrity.

Retry Policies: Configurable strategies for automatically retrying failed operations with exponential backoff and circuit breaker patterns.

Circuit Breaker: A pattern that prevents repeated calls to failing services, allowing them time to recover before resuming operations.

Fallback Strategies: Alternative execution paths or default behaviors when primary operations fail or are unavailable.

Error Propagation: The mechanism by which errors are communicated through the graph execution chain for appropriate handling.

Advanced Patterns

Multi-Agent Coordination: The orchestration of multiple specialized agents working together to solve complex problems.

Human-in-the-Loop (HITL): Integration of human decision-making and approval steps within automated workflows.

Parallel Execution: Simultaneous processing of multiple nodes or workflow branches to improve performance and throughput.

Fork-Join Patterns: Workflow structures that split execution into parallel paths and then recombine results.

Template Engine: A system for creating reusable workflow patterns and configurations that can be customized for specific use cases.

Integration and Extensions

Plugin System: A mechanism for dynamically loading and integrating external functionality into graph workflows.

REST Tools: Integration capabilities for calling external APIs and services from within graph nodes.

Memory Integration: Connection to persistent storage systems for maintaining context across multiple workflow executions.

Custom Extensions: User-defined functionality that extends the core graph capabilities with domain-specific logic.

API Integration: Methods for exposing graph workflows as REST endpoints or other external interfaces.

Development and Testing

Debug Mode: Enhanced execution mode that provides detailed logging, breakpoints, and inspection capabilities for development.

Graph Inspection: Tools and APIs for examining graph structure, state, and execution history during development and debugging.

Unit Testing: Testing individual nodes and edges in isolation to verify correct behavior and edge cases.

Integration Testing: Testing complete graph workflows to ensure proper coordination and end-to-end functionality.

Performance Testing: Evaluating graph execution performance under various load conditions and data volumes.

Configuration and Options

Graph Options: Configurable parameters that control graph behavior, performance, and feature availability.

Execution Options: Settings that govern how graphs are executed, including timeouts, concurrency limits, and resource constraints.

Checkpoint Options: Configuration for checkpoint creation, storage, compression, and retention policies.

Streaming Options: Settings that control real-time execution behavior, event buffering, and backpressure handling.

Resource Options: Configuration for managing memory usage, concurrency limits, and system resource allocation.

Security and Data Handling

Data Sanitization: The process of removing or masking sensitive information from logs, metrics, and external communications.

Access Control: Mechanisms for controlling who can execute, modify, or inspect graph workflows and their data.

Audit Logging: Comprehensive recording of all graph operations for compliance, security, and operational purposes.

Encryption: Protection of sensitive data at rest and in transit using cryptographic techniques.

Privacy Controls: Features that help maintain data privacy and comply with regulatory requirements.

Deployment and Operations

Production Deployment: The process of making graph workflows available for live use in production environments.

Monitoring and Alerting: Systems for observing production workflows and notifying operators of issues or performance degradation.

Scaling: The ability to adjust resource allocation and concurrency to handle varying workloads and performance requirements.

High Availability: Design patterns that ensure graph workflows remain operational even when individual components fail.

Disaster Recovery: Procedures and systems for restoring graph operations after major failures or data loss events.