Workflow Patterns

To build any example: – press Space to add nodes – drag connections – press Ctrl/⌘+Enter to run – add Preview nodes to inspect intermediate results

Pattern 1: Simple Pipeline

Use Case: Transform input → process → output

Example: Image Enhancement

graph TD image_output["ImageOutput"] image_input["ImageInput"] sharpen["Sharpen"] auto_contrast["AutoContrast"] image_input --> sharpen sharpen --> auto_contrast auto_contrast --> image_output

When to Use:

  • Simple data transformations
  • Single input, single output
  • No conditional logic needed

Pattern 2: Agent-Driven Generation

Use Case: LLM generates content based on input

Example: Image to Story

graph TD image_input["Image"] agent_story["Agent (Story Generator)"] preview_audio["Preview (Audio)"] text_to_speech["TextToSpeech"] image_input --> agent_story agent_story --> text_to_speech text_to_speech --> preview_audio

When to Use:

  • Creative generation tasks
  • Multimodal transformations (image→text→audio)
  • Need semantic understanding

Key Nodes:

  • Agent: General-purpose LLM agent with streaming
  • Summarizer: Specialized for text summarization
  • ListGenerator: Streams list of items

Pattern 3: Streaming with Multiple Previews

Use Case: Show intermediate results during generation

Example: Movie Poster Generator

graph TD strategy_llm["Agent (Strategy)"] strategy_template_prompt["String (Strategy Template)"] strategy_preview["Preview (Strategy)"] strategy_prompt_formatter["FormatText (Strategy)"] movie_title_input["StringInput (Title)"] genre_input["StringInput (Genre)"] audience_input["StringInput (Audience)"] image_preview["Preview (Image)"] prompt_list_generator["ListGenerator"] designer_instructions_prompt["String (Designer Instructions)"] preview_prompts["Preview (Prompts)"] mflux["MFlux"] strategy_llm --> strategy_preview strategy_template_prompt --> strategy_prompt_formatter strategy_prompt_formatter --> strategy_llm strategy_llm --> prompt_list_generator designer_instructions_prompt --> prompt_list_generator audience_input --> strategy_prompt_formatter movie_title_input --> strategy_prompt_formatter genre_input --> strategy_prompt_formatter prompt_list_generator --> preview_prompts prompt_list_generator --> mflux mflux --> image_preview

When to Use:

  • Complex multi-stage generation
  • User needs to see progress
  • Agent planning + execution workflow

Key Concepts:

  • Strategy Phase: Agent plans approach
  • Preview Nodes: Show intermediate results
  • ListGenerator: Streams generated prompts
  • Image Generation: Final output

Pattern 4: RAG (Retrieval-Augmented Generation)

Use Case: Answer questions using documents as context

Example: Chat with Docs

graph TD chat_input["ChatInput"] string_output["StringOutput (Answer)"] format_text["FormatText"] hybrid_search["HybridSearch"] agent["Agent"] chat_input --> format_text chat_input --> hybrid_search hybrid_search --> format_text format_text --> agent agent --> string_output

When to Use:

  • Question-answering over documents
  • Need factual accuracy from specific sources
  • Reduce LLM hallucinations

Key Components:

  1. Search: Query vector database for relevant documents
  2. Format: Inject retrieved context into prompt
  3. Generate: Stream LLM response with context

Related Workflow: Index PDFs

graph TD list_files["ListFiles"] collection["Collection"] load_document["LoadDocumentFile"] extract_text["ExtractText"] index_chunks["IndexTextChunks"] sentence_splitter["SentenceSplitter"] path_to_string["PathToString"] extract_text --> sentence_splitter load_document --> extract_text sentence_splitter --> index_chunks collection --> index_chunks list_files --> load_document list_files --> path_to_string path_to_string --> sentence_splitter

Pattern 5: Database Persistence

Use Case: Store generated data for later retrieval

Example: AI Flashcard Generator with SQLite

graph TD topic_input["StringInput (Topic)"] create_table["CreateTable"] format_prompt["FormatText"] generate_flashcards["DataGenerator"] insert_flashcard["Insert"] query_all["Query"] display_result["Preview"] topic_input --> format_prompt format_prompt --> generate_flashcards generate_flashcards --> insert_flashcard create_table --> query_all create_table --> insert_flashcard query_all --> display_result

When to Use:

  • Need persistent storage
  • Building apps with memory
  • Agent workflows that need to recall past interactions

Key Nodes:

  • CreateTable: Initialize database schema
  • Insert: Add records
  • Query: Retrieve records
  • Update: Modify records
  • Delete: Remove records

Database Flow:

  1. Create table structure
  2. Generate data with agent
  3. Insert into database
  4. Query and display results

Pattern 6: Email & Web Integration

Use Case: Process emails or web content

Example: Summarize Newsletters

graph TD gmail_search["GmailSearch"] email_fields["EmailFields"] summarizer_streaming["SummarizerStreaming"] preview_summary["Preview (Summary)"] preview_body["Preview (Body)"] gmail_search --> email_fields email_fields --> summarizer_streaming summarizer_streaming --> preview_summary email_fields --> preview_body

When to Use:

  • Automate email processing
  • Monitor RSS feeds
  • Extract web content

Key Nodes:

  • GmailSearch: Search Gmail with queries
  • EmailFields: Extract email metadata
  • FetchRSSFeed: Get RSS feed entries
  • GetRequest: Fetch web content

Pattern 7: Realtime Processing

Use Case: Process streaming audio/video in real-time

Example: Realtime Agent

graph TD audio_input["RealtimeAudioInput"] preview_output["Preview"] realtime_agent["RealtimeAgent"] audio_input --> realtime_agent realtime_agent --> preview_output

When to Use:

  • Voice interfaces
  • Live transcription
  • Interactive audio applications

Key Nodes:

  • RealtimeAudioInput: Streaming audio input
  • RealtimeAgent: OpenAI Realtime API with streaming
  • RealtimeWhisper: Live transcription
  • RealtimeTranscription: OpenAI transcription streaming

Pattern 8: Multi-Modal Workflows

Use Case: Convert between different media types

Example: Audio to Image

graph TD stable_diffusion["StableDiffusion"] whisper["Whisper"] audio_input["AudioInput"] image_output["ImageOutput"] whisper --> stable_diffusion audio_input --> whisper stable_diffusion --> image_output

When to Use:

  • Converting between media types
  • Creating rich multimedia experiences
  • Accessibility applications

Common Chains:

  • Audio → Text → Image
  • Image → Text → Audio
  • Video → Audio → Text → Summary

Pattern 9: Advanced Image Processing

Use Case: AI-powered image transformations

Example: Style Transfer

graph TD stable_diffusion_control_net_img2img["StableDiffusionControlNetImg2Img"] image_input_1["ImageInput"] image_input_2["ImageInput"] image_output["ImageOutput"] canny["Canny"] image_to_text["ImageToText"] fit_1["Fit"] fit_2["Fit"] stable_diffusion_control_net_img2img --> image_output canny --> stable_diffusion_control_net_img2img image_to_text --> stable_diffusion_control_net_img2img image_input_2 --> fit_1 image_input_1 --> fit_2 fit_2 --> canny fit_2 --> image_to_text image_input_2 --> stable_diffusion_control_net_img2img fit_2 --> stable_diffusion_control_net_img2img

When to Use:

  • Style transfer between images
  • Controlled image generation
  • Preserving structure while changing style

Key Techniques:

  • ControlNet: Preserve structure with edge detection
  • Image-to-Text: Generate descriptions
  • Img2Img: Transform while maintaining composition

Pattern 10: Data Processing Pipeline

Use Case: Fetch, transform, and visualize data

Example: Data Visualization Pipeline

graph TD preview_1["Preview"] get_request["GetRequest"] import_c_s_v["ImportCSV"] filter["Filter"] chart_generator["ChartGenerator"] preview_2["Preview"] get_request --> import_c_s_v import_c_s_v --> filter filter --> preview_1 filter --> chart_generator chart_generator --> preview_2

When to Use:

  • Fetch external data sources
  • Transform and filter datasets
  • Auto-generate visualizations

Key Nodes:

  • GetRequest: Fetch web resources
  • ImportCSV: Parse CSV data
  • Filter: Transform data
  • ChartGenerator: AI-generated charts with Plotly