GenAI Mastery Series · Agentic AI · Flowise Walkthrough
Building an AI Storytelling Agent with Flowise — No Code Required
In today’s AI landscape, agents are becoming powerful tools to automate complex tasks — from chatbots to interactive storytelling. Flowise is a no-code AI workflow builder that makes it easy to design, deploy, and manage AI agents for a wide range of applications. This walkthrough builds a fully functional storytelling agent, locally deployable and cloud-ready.
Concepts
What are AI agents in Flowise?
AI agents in Flowise are intelligent modules that can handle tasks autonomously by combining logic, AI models, and external tools. They process inputs, make decisions, and generate tailored outputs — without manual intervention at each step.
In this project we use the Supervisor and Worker node pattern with OpenAI Chat. The supervisor coordinates the overall workflow; worker nodes each own a specific sub-task — here, storytelling and title assignment.
Step 1
Setting up Flowise
Flowise runs as a local Node.js server you access through a browser-based canvas. Two commands are all you need to get started.
npm install flowise
npx flowise run
Once running, open your browser, log in, and click “New Workflow” to open the interactive canvas. You’ll drag, drop, and wire nodes together visually — no boilerplate code.
Step 2 — Workflow Design
Building the agent — node by node
Chat Model Node
ChatOpenAI — The Brain
Drag a ChatOpenAI Node onto the canvas and connect it as the model backend for all worker nodes. Configure GPT-4 with elevated temperature for imaginative outputs.
Worker Node · Role: Storyteller
Storytelling Agent
Add a Worker Node and connect it to the ChatOpenAI node. Set its role as the Storyteller. This node owns the core creative generation task — it receives the theme prompt and writes the full story.
Worker Node · Role: Title Assigner
Title Assigner Agent
Add a second Worker Node downstream of the Storyteller. This node’s sole job is to extract a short, engaging title from the generated story — a focused single-responsibility task.
Format Prompt Values Node
Output Formatter
Use the Format Prompt Values Node to combine the story and title from the two worker nodes into a clean, structured output ready for display.
Chat Output Node
Chat Output — Delivery
Connect the formatted output to the Chat Output Node. This is the interface layer — the final assembled story and title are surfaced here for users to read, copy, or embed.
Step 3
Running the agent
With all nodes configured and wired together, save your workflow — name it something like “AI Storytelling Agent” — and hit Run. Enter a theme prompt or use the default storytelling instructions, and the agent pipeline fires automatically: ChatOpenAI powers the Storyteller worker, its output flows to the Title Assigner, both outputs merge in the Formatter, and the Chat Output displays the result.
temperature values. At 0.7 the stories are coherent but predictable. At 0.95 you get genuinely surprising plot twists — which for kids’ stories is exactly what you want.
Example Output
IBM’s Marshmallow Party
Generated Title
IBM the Robot and the Marshmallow Party
Once upon a time, in a land of giggles and sparkles, there lived a silly little robot named IBM. Now, IBM wasn’t your everyday robot who danced or painted; oh no! He was known as a “computer,” which is a magic box that helps people do all sorts of amazing things!
One sunny day, IBM decided to throw a party for all his robot friends. He said, “Let’s make it the best party ever! I’ll invite my buddy, Printer Pete, and my bestie, Codey the Coder!” But IBM accidentally ordered 1,000 bags of rainbow-colored marshmallows instead of snacks — and chaos ensued!
The party turned into a marshmallow-filled adventure with pillow fights, marshmallow towers, and lots of laughs. In the end, IBM and his friends agreed: whether it’s chips or marshmallows, any party is fun when friends are around!
Interview Prep
Cheat sheet — quick definitions to remember
What is an AI agent?
What is the Supervisor / Worker pattern?
What does temperature control in an LLM?
No-code (Flowise) vs code-first (LangChain) — when to use which?
Why split story generation and title extraction into separate nodes?
What other use cases suit a Flowise multi-agent setup?
What are the main limits of no-code agent builders?
GenAI Mastery Series · Agentic AI · Flowise Walkthrough
Building an AI Storytelling Agent with Flowise — No Code Required
In today’s AI landscape, agents are becoming powerful tools to automate complex tasks — from chatbots to interactive storytelling. Flowise is a no-code AI workflow builder that makes it easy to design, deploy, and manage AI agents for a wide range of applications. This walkthrough builds a fully functional storytelling agent, locally deployable and cloud-ready.
Concepts
What are AI agents in Flowise?
AI agents in Flowise are intelligent modules that can handle tasks autonomously by combining logic, AI models, and external tools. They process inputs, make decisions, and generate tailored outputs — without manual intervention at each step.
In this project we use the Supervisor and Worker node pattern with OpenAI Chat. The supervisor coordinates the overall workflow; worker nodes each own a specific sub-task — here, storytelling and title assignment.
Step 1
Setting up Flowise
Flowise runs as a local Node.js server you access through a browser-based canvas. Two commands are all you need to get started.
npm install flowise
npx flowise run
Once running, open your browser, log in, and click “New Workflow” to open the interactive canvas. You’ll drag, drop, and wire nodes together visually — no boilerplate code.
Step 2 — Workflow Design
Building the agent — node by node
Chat Model Node
ChatOpenAI — The Brain
Drag a ChatOpenAI Node onto the canvas and connect it as the model backend for all worker nodes. Configure GPT-4 with elevated temperature for imaginative outputs.
Worker Node · Role: Storyteller
Storytelling Agent
Add a Worker Node and connect it to the ChatOpenAI node. Set its role as the Storyteller. This node owns the core creative generation task — it receives the theme prompt and writes the full story.
Worker Node · Role: Title Assigner
Title Assigner Agent
Add a second Worker Node downstream of the Storyteller. This node’s sole job is to extract a short, engaging title from the generated story — a focused single-responsibility task.
Format Prompt Values Node
Output Formatter
Use the Format Prompt Values Node to combine the story and title from the two worker nodes into a clean, structured output ready for display.
Chat Output Node
Chat Output — Delivery
Connect the formatted output to the Chat Output Node. This is the interface layer — the final assembled story and title are surfaced here for users to read, copy, or embed.
Step 3
Running the agent
With all nodes configured and wired together, save your workflow — name it something like “AI Storytelling Agent” — and hit Run. Enter a theme prompt or use the default storytelling instructions, and the agent pipeline fires automatically: ChatOpenAI powers the Storyteller worker, its output flows to the Title Assigner, both outputs merge in the Formatter, and the Chat Output displays the result.
temperature values. At 0.7 the stories are coherent but predictable. At 0.95 you get genuinely surprising plot twists — which for kids’ stories is exactly what you want.
Example Output
IBM’s Marshmallow Party
Generated Title
IBM the Robot and the Marshmallow Party
Once upon a time, in a land of giggles and sparkles, there lived a silly little robot named IBM. Now, IBM wasn’t your everyday robot who danced or painted; oh no! He was known as a “computer,” which is a magic box that helps people do all sorts of amazing things!
One sunny day, IBM decided to throw a party for all his robot friends. He said, “Let’s make it the best party ever! I’ll invite my buddy, Printer Pete, and my bestie, Codey the Coder!” But IBM accidentally ordered 1,000 bags of rainbow-colored marshmallows instead of snacks — and chaos ensued!
The party turned into a marshmallow-filled adventure with pillow fights, marshmallow towers, and lots of laughs. In the end, IBM and his friends agreed: whether it’s chips or marshmallows, any party is fun when friends are around!
Interview Prep
Cheat sheet — quick definitions to remember
What is an AI agent?
What is the Supervisor / Worker pattern?
What does temperature control in an LLM?
No-code (Flowise) vs code-first (LangChain) — when to use which?
Why split story generation and title extraction into separate nodes?
What other use cases suit a Flowise multi-agent setup?
What are the main limits of no-code agent builders?
