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In this guide, you’ll build a complete workflow that:
  1. Reads data from a Google Sheet
  2. Analyzes the data with AI
  3. Generates a summary report

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Prerequisites

Make sure you’ve completed the Quickstart and have your API key configured.

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Step 1: Create the Workflow

Start by creating a new Python file:
sales_report.py
from fibonacci import Workflow, LLMNode, ToolNode
from dotenv import load_dotenv
import os

load_dotenv()

# Create the workflow
wf = Workflow(
    name="Sales Report Generator",
    description="Reads sales data and generates an analysis report",
    tags=["sales", "reporting", "weekly"]
)

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Step 2: Add the Data Source Node

Use a ToolNode to read data from Google Sheets: 
# Node 1: Read sales data from Google Sheets
read_data = ToolNode(
    id="read_sales",
    name="Read Sales Data",
    tool="google_sheets_read",
    params={
        "spreadsheet_id": "{{input.sheet_id}}",
        "range": "{{input.range}}"
    }
)

wf.add_node(read_data)
The {{input.sheet_id}} syntax is a template variable. It will be replaced with actual values when you run the workflow.

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Step 3: Add the Analysis Node

Use an LLMNode to analyze the data: 
# Node 2: Analyze the sales data
analyze = LLMNode(
    id="analyze_data",
    name="Analyze Sales Data",
    instruction="""
    Analyze this sales data and provide:
    
    1. **Total Revenue**: Sum of all sales
    2. **Top Performers**: Top 3 salespeople by revenue
    3. **Trends**: Week-over-week comparison
    4. **Concerns**: Any red flags or areas needing attention
    
    Sales Data:
    {{read_sales}}
    
    Format your response with clear sections and bullet points.
    """,
    dependencies=["read_sales"],  # This node depends on read_sales
    model="claude-sonnet-4-5-20250929",
    max_tokens=2000
)

wf.add_node(analyze)

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Step 4: Add the Summary Node

Add another LLM node to create an executive summary: 
# Node 3: Generate executive summary
summary = LLMNode(
    id="executive_summary",
    name="Executive Summary",
    instruction="""
    Based on this analysis, create a brief executive summary (3-4 sentences) 
    suitable for a busy executive. Focus on the most important insights and 
    any actions needed.
    
    Analysis:
    {{analyze_data}}
    """,
    dependencies=["analyze_data"]
)

wf.add_node(summary)

​
Step 5: Complete Workflow Code

Here’s the complete workflow:
sales_report.py
from fibonacci import Workflow, LLMNode, ToolNode
from dotenv import load_dotenv
import os

load_dotenv()

# Create the workflow
wf = Workflow(
    name="Sales Report Generator",
    description="Reads sales data and generates an analysis report",
    tags=["sales", "reporting", "weekly"]
)

# Node 1: Read data
read_data = ToolNode(
    id="read_sales",
    name="Read Sales Data",
    tool="google_sheets_read",
    params={
        "spreadsheet_id": "{{input.sheet_id}}",
        "range": "{{input.range}}"
    }
)

# Node 2: Analyze
analyze = LLMNode(
    id="analyze_data",
    name="Analyze Sales Data",
    instruction="""
    Analyze this sales data and provide:
    
    1. **Total Revenue**: Sum of all sales
    2. **Top Performers**: Top 3 salespeople by revenue
    3. **Trends**: Week-over-week comparison
    4. **Concerns**: Any red flags or areas needing attention
    
    Sales Data:
    {{read_sales}}
    """,
    dependencies=["read_sales"]
)

# Node 3: Summary
summary = LLMNode(
    id="executive_summary",
    name="Executive Summary",
    instruction="""
    Create a brief executive summary (3-4 sentences) based on:
    
    {{analyze_data}}
    """,
    dependencies=["analyze_data"]
)

# Add all nodes
wf.add_nodes([read_data, analyze, summary])

if __name__ == "__main__":
    # Validate the workflow
    wf.validate()
    print("✅ Workflow validated!")
    
    # Deploy
    api_key = os.getenv("FIBONACCI_API_KEY")
    workflow_id = wf.deploy(api_key=api_key)
    print(f"✅ Deployed! ID: {workflow_id}")
    
    # Execute
    result = wf.run(input_data={
        "sheet_id": "1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms",  # Sample Sheet
        "range": "Sales!A1:E10"
    })
    
    # Print results
    print(f"\n📊 Status: {result.status}")
    print(f"⏱️  Duration: {result.duration_seconds:.2f}s")
    print(f"💰 Cost: ${result.total_cost:.4f}")
    
    print("\n" + "="*60)
    print("📈 ANALYSIS")
    print("="*60)
    print(result.output_data["analyze_data"])
    
    print("\n" + "="*60)
    print("📋 EXECUTIVE SUMMARY")
    print("="*60)
    print(result.output_data["executive_summary"])

​
Step 6: Run Your Workflow

Execute the workflow: 
python sales_report.py
Expected output: 
✅ Workflow validated!
✅ Deployed! ID: wf_abc123xyz

📊 Status: completed
⏱️  Duration: 4.32s
💰 Cost: $0.0023

============================================================
📈 ANALYSIS
============================================================
## Total Revenue
$1,234,567 for the period

## Top Performers
1. Sarah Johnson - $245,000
2. Mike Chen - $198,000
3. Lisa Park - $187,000

## Trends
- 12% increase from last week
- Strong performance in Enterprise segment

## Concerns
- SMB segment down 8%
- Two reps below quota

============================================================
📋 EXECUTIVE SUMMARY
============================================================
Sales exceeded targets by 12% this week, driven by strong Enterprise 
performance from Sarah Johnson and Mike Chen. However, the SMB segment 
declined 8%, requiring immediate attention. Recommend a team meeting 
to address underperforming segments.

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Understanding the Workflow

Workflow Diagram

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How Nodes Connect

  1. read_sales runs first (no dependencies)
  2. analyze_data waits for read_sales to complete
  3. executive_summary waits for analyze_data to complete

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Template Variables

  • {{input.sheet_id}} - Replaced with input data at runtime
  • {{read_sales}} - Replaced with the output of the read_sales node
  • {{analyze_data}} - Replaced with the output of the analyze_data node

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Adding Conditional Logic

Want to send an alert only if sales drop? Add a conditional node: 
from fibonacci import ConditionalNode

# Check if we need an alert
check_alert = ConditionalNode(
    id="check_alert",
    name="Check if Alert Needed",
    left_value="{{analyze_data}}",
    operator="contains",
    right_value="down",
    true_branch=["send_alert"],
    false_branch=[],
    dependencies=["analyze_data"]
)

# Send Slack alert (only runs if condition is true)
send_alert = ToolNode(
    id="send_alert",
    name="Send Slack Alert",
    tool="slack_send_message",
    params={
        "channel": "#sales-alerts",
        "message": "⚠️ Sales Alert!\n\n{{executive_summary}}"
    },
    dependencies=["check_alert", "executive_summary"]
)

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Exporting to YAML

You can also define workflows in YAML: 
# Export your workflow to YAML
wf.to_yaml("sales_report.yaml")
This creates:
sales_report.yaml
name: Sales Report Generator
description: Reads sales data and generates an analysis report
version: 1
is_active: true
tags:
  - sales
  - reporting
  - weekly

nodes:
  - id: read_sales
    type: tool
    name: Read Sales Data
    tool_name: google_sheets_read
    tool_params:
      spreadsheet_id: "{{input.sheet_id}}"
      range: "{{input.range}}"

  - id: analyze_data
    type: llm
    name: Analyze Sales Data
    instruction: |
      Analyze this sales data and provide:
      1. **Total Revenue**: Sum of all sales
      2. **Top Performers**: Top 3 salespeople by revenue
      ...
    dependencies:
      - read_sales

  - id: executive_summary
    type: llm
    name: Executive Summary
    instruction: |
      Create a brief executive summary...
    dependencies:
      - analyze_data

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Next Steps

Node Types Deep Dive

Learn about all available node types

Tool Integrations

Connect to 100+ tools and services

Conditional Logic

Build dynamic workflows with branching

Memory & State

Persist data across workflow runs
Great job! You’ve built a real workflow with multiple nodes, dependencies, and AI analysis.