Core Concepts & Architecture

Core Concepts & Architecture

Core Concepts

What is a NodeTool Workflow?

A NodeTool workflow is a Directed Acyclic Graph (DAG) where:

  • Nodes represent operations (processing, generation, transformation)
  • Edges represent data flow between nodes
  • Execution follows dependency order automatically
Input → Process → Transform → Output

Key Principles

  1. Data Flows Through Edges: Nodes connect via typed edges (image → image, text → text, etc.)
  2. Asynchronous Execution: Nodes execute when dependencies are satisfied
  3. Streaming by Default: Many nodes support real-time streaming output
  4. Type Safety: Connections enforce type compatibility

Node Types

Type Purpose Examples
Input Nodes Accept parameters StringInput, ImageInput, AudioInput
Processing Nodes Transform data Resize, Filter, ExtractText
Agent Nodes LLM-powered logic Agent, Summarizer, ListGenerator
Output Nodes Return results Output, Preview
Control Nodes Flow control Collect, FormatText
Storage Nodes Persistence CreateTable, Insert, Query

Streaming Architecture

Why Streaming?

NodeTool workflows support streaming execution for:

  • Real-time feedback: See results as they’re generated
  • Lower latency: Start processing before all data arrives
  • Better UX: Progress indicators and incremental results
  • Efficient memory: Process large data in chunks

Streaming Nodes

Nodes that support streaming output:

  • Agent: Streams LLM responses token by token
  • ListGenerator: Streams list items as they’re generated
  • RealtimeAgent: Streams audio + text responses
  • RealtimeWhisper: Streams transcription as audio arrives
  • RealtimeAudioInput: Streams audio from an input source

Data Flow Patterns

Pattern 1: Sequential Pipeline

Input → Process → Transform → Output

Each node waits for previous node to complete.

Pattern 2: Parallel Branches

        → ProcessA → OutputA
Input →
        → ProcessB → OutputB

Multiple branches execute in parallel.

Pattern 3: Streaming Pipeline

Input → StreamingAgent → Collect → Output
         (yields chunks)

Data flows in chunks, enabling real-time updates.

Pattern 4: Fan-In Pattern

SourceA →
         → Combine → Process → Output
SourceB →

Multiple inputs combine before processing.