Best Practices

This guide covers proven patterns and recommendations for building reliable, maintainable, and scalable Fibonacci workflows in production.

Workflow Design

Keep Workflows Focused

Each workflow should do one thing well:

# ✅ Good - focused workflows
customer_classifier = Workflow(name="classify-customer-inquiry")
response_generator = Workflow(name="generate-support-response")
escalation_handler = Workflow(name="handle-escalation")

# ❌ Avoid - monolithic workflow doing everything
support_system = Workflow(name="complete-customer-support-system")

Use Meaningful Names

# ✅ Good - descriptive names
workflow = Workflow(name="quarterly-sales-report-generator")

sentiment_node = LLMNode(
    id="analyze_customer_sentiment",
    ...
)

# ❌ Avoid - vague names
workflow = Workflow(name="processor")
node = LLMNode(id="node1", ...)

Document Your Workflows

workflow = Workflow(
    name="customer-churn-predictor",
    description="""
    Analyzes customer behavior patterns to predict churn risk.
    
    Inputs:
    - customer_id: Customer identifier
    - timeframe: Analysis period (default: 90 days)
    
    Outputs:
    - risk_score: 0-100 churn probability
    - factors: Contributing factors
    - recommendations: Retention actions
    
    Owner: data-science@company.com
    Last updated: 2025-01-15
    """
)

Node Patterns

Chain of Responsibility

Break complex tasks into specialized nodes:

from fibonacci import Workflow, LLMNode

workflow = Workflow(name="content-pipeline")

# Step 1: Generate outline
outliner = LLMNode(
    id="create_outline",
    model="claude-sonnet-4-5-20250929",
    prompt="Create an outline for: {{input.topic}}"
)

# Step 2: Write draft
writer = LLMNode(
    id="write_draft",
    model="claude-sonnet-4-5-20250929",
    prompt="Write content following this outline: {{create_outline}}",
    dependencies=["create_outline"]
)

# Step 3: Edit and polish
editor = LLMNode(
    id="edit_content",
    model="claude-sonnet-4-5-20250929",
    prompt="Edit for clarity and style: {{write_draft}}",
    dependencies=["write_draft"]
)

# Step 4: Final review
reviewer = LLMNode(
    id="final_review",
    model="claude-opus-4-5-20251101",  # Premium model for final check
    prompt="Final quality review: {{edit_content}}",
    dependencies=["edit_content"]
)

Parallel Processing

Maximize throughput with parallel execution:

workflow = Workflow(name="multi-analysis")

# These run in parallel (no dependencies)
sentiment = LLMNode(
    id="sentiment",
    model="claude-haiku-4-5-20251001",
    prompt="Sentiment: {{input.text}}"
)

entities = LLMNode(
    id="entities",
    model="claude-haiku-4-5-20251001",
    prompt="Extract entities: {{input.text}}"
)

summary = LLMNode(
    id="summary",
    model="claude-haiku-4-5-20251001",
    prompt="Summarize: {{input.text}}"
)

# Combine results
combine = LLMNode(
    id="combine",
    model="claude-sonnet-4-5-20250929",
    prompt="""
    Combine these analyses:
    Sentiment: {{sentiment}}
    Entities: {{entities}}
    Summary: {{summary}}
    """,
    dependencies=["sentiment", "entities", "summary"]
)

Critic Pattern for Quality

Use CriticNode for iterative improvement:

from fibonacci import LLMNode, CriticNode

writer = LLMNode(
    id="writer",
    model="claude-sonnet-4-5-20250929",
    prompt="Write a professional email: {{input.request}}"
)

critic = CriticNode(
    id="quality_critic",
    model="claude-sonnet-4-5-20250929",
    target_node="writer",
    criteria=[
        "Professional tone throughout",
        "Clear and concise language",
        "Proper grammar and spelling",
        "Appropriate length (not too long)",
        "Clear call to action"
    ],
    min_score=8,
    max_iterations=3,
    improvement_prompt="""
    Improve the email based on this feedback:
    {{feedback}}
    
    Original email:
    {{writer}}
    """
)

Prompt Engineering

Use Clear Structure

analyzer = LLMNode(
    id="analyzer",
    model="claude-sonnet-4-5-20250929",
    prompt="""You are analyzing customer feedback for a software company.

<context>
Product: {{input.product_name}}
Customer tier: {{input.customer_tier}}
</context>

<feedback>
{{input.feedback_text}}
</feedback>

<instructions>
1. Identify the main issue or request
2. Assess the urgency (low/medium/high/critical)
3. Categorize: bug, feature_request, question, complaint, praise
4. Suggest an appropriate response approach
</instructions>

<output_format>
Return JSON:
{
  "main_issue": "...",
  "urgency": "...",
  "category": "...",
  "response_approach": "..."
}
</output_format>"""
)

Include Examples (Few-Shot)

classifier = LLMNode(
    id="intent_classifier",
    model="claude-sonnet-4-5-20250929",
    prompt="""Classify the customer intent.

<examples>
Input: "I can't log into my account"
Output: {"intent": "technical_support", "urgency": "high"}

Input: "Do you offer annual billing?"
Output: {"intent": "billing_question", "urgency": "low"}

Input: "This is unacceptable, I want a refund!"
Output: {"intent": "complaint", "urgency": "high"}

Input: "Love the new feature!"
Output: {"intent": "feedback_positive", "urgency": "low"}
</examples>

<input>
{{input.message}}
</input>

Classify this input following the same format as the examples."""
)

Specify Output Format

# Structured JSON output
structured_node = LLMNode(
    id="structured",
    model="claude-sonnet-4-5-20250929",
    prompt="Analyze: {{input.text}}",
    output_format="json",
    output_schema={
        "type": "object",
        "required": ["sentiment", "confidence", "keywords"],
        "properties": {
            "sentiment": {"enum": ["positive", "negative", "neutral"]},
            "confidence": {"type": "number", "minimum": 0, "maximum": 1},
            "keywords": {"type": "array", "items": {"type": "string"}}
        }
    }
)

Error Handling

Always Set Timeouts

# Node-level timeout
node = LLMNode(
    id="analyzer",
    model="claude-opus-4-5-20251101",
    prompt="...",
    timeout=60  # 60 seconds
)

# Workflow-level timeout
result = workflow.execute(
    inputs=data,
    timeout=300  # 5 minutes for entire workflow
)

Implement Graceful Degradation

from fibonacci import Workflow, LLMNode

workflow = Workflow(name="resilient-analysis")

# Premium analysis with expensive model
premium = LLMNode(
    id="premium_analysis",
    model="claude-opus-4-5-20251101",
    prompt="Detailed analysis: {{input.text}}",
    timeout=60,
    on_error="standard_analysis"
)

# Standard fallback
standard = LLMNode(
    id="standard_analysis",
    model="claude-sonnet-4-5-20250929",
    prompt="Analysis: {{input.text}}",
    timeout=30,
    on_error="basic_analysis"
)

# Basic fallback
basic = LLMNode(
    id="basic_analysis",
    model="claude-haiku-4-5-20251001",
    prompt="Quick analysis: {{input.text}}",
    timeout=15
)

Use Retry with Backoff

from fibonacci import ToolNode, RetryConfig

api_node = ToolNode(
    id="external_api",
    tool="http.request",
    inputs={"url": "https://api.example.com"},
    retry=RetryConfig(
        max_attempts=5,
        delay=1.0,
        backoff="exponential",
        max_delay=30.0,
        jitter=True,
        retry_on=["timeout", "5xx", "rate_limit"]
    )
)

Performance Optimization

Choose the Right Model

Use Case	Recommended Model
Simple classification	claude-haiku-4-5-20251001
General tasks	claude-sonnet-4-5-20250929
Complex reasoning	claude-opus-4-5-20251101
High volume, low cost	claude-haiku-4-5-20251001

# Use Haiku for simple, high-volume tasks
classifier = LLMNode(
    id="quick_classify",
    model="claude-haiku-4-5-20251001",  # Fast and cost-effective
    prompt="Classify as spam/not_spam: {{input.text}}"
)

# Use Opus for complex analysis
deep_analyzer = LLMNode(
    id="deep_analysis",
    model="claude-opus-4-5-20251101",  # Most capable
    prompt="Provide comprehensive analysis: {{input.text}}"
)

Optimize Token Usage

# Be concise in prompts
# ❌ Verbose
verbose = LLMNode(
    id="verbose",
    prompt="""I would like you to please analyze the following text 
    and provide me with a detailed sentiment analysis. The text is 
    provided below for your analysis..."""
)

# ✅ Concise
concise = LLMNode(
    id="concise",
    prompt="Analyze sentiment: {{input.text}}\nReturn: positive/negative/neutral"
)

Cache Expensive Operations

from fibonacci import Workflow
from fibonacci.patterns import cached

workflow = Workflow(name="cached-workflow")

@cached(ttl=3600)  # Cache for 1 hour
def expensive_analysis(text: str):
    return workflow.execute(inputs={"text": text})

# Or use memory-based caching
def get_or_compute(workflow, key: str, compute_fn):
    cached = workflow.memory.get(f"cache:{key}", scope="organization")
    if cached:
        return cached
    
    result = compute_fn()
    workflow.memory.set(
        f"cache:{key}",
        result,
        scope="organization",
        ttl=3600
    )
    return result

Testing

Unit Test Nodes

import pytest
from fibonacci import LLMNode
from fibonacci.testing import MockLLM

def test_sentiment_classifier():
    # Mock the LLM response
    mock = MockLLM(responses={
        "positive": '{"sentiment": "positive", "confidence": 0.95}'
    })
    
    node = LLMNode(
        id="sentiment",
        model=mock,
        prompt="Classify: {{input.text}}"
    )
    
    result = node.execute({"text": "I love this product!"})
    
    assert result["sentiment"] == "positive"
    assert result["confidence"] > 0.9

Integration Test Workflows

def test_support_workflow():
    workflow = Workflow.from_yaml("workflows/support.yaml")
    
    # Test happy path
    result = workflow.execute(
        inputs={
            "message": "How do I reset my password?",
            "customer_id": "test-123"
        }
    )
    
    assert result["category"] == "technical_support"
    assert "password" in result["response"].lower()

def test_error_handling():
    workflow = Workflow.from_yaml("workflows/support.yaml")
    
    # Test with invalid input
    with pytest.raises(ValidationError):
        workflow.execute(inputs={"message": ""})

Use Test Fixtures

import pytest
from fibonacci import Workflow

@pytest.fixture
def sample_workflow():
    return Workflow.from_yaml("test_fixtures/sample.yaml")

@pytest.fixture
def test_inputs():
    return {
        "text": "Sample text for testing",
        "user_id": "test-user"
    }

def test_workflow_execution(sample_workflow, test_inputs):
    result = sample_workflow.execute(inputs=test_inputs)
    assert "output" in result

Deployment

Environment Separation

# fibonacci.yaml
environments:
  development:
    model_default: claude-haiku-4-5-20251001  # Cheaper for dev
    timeout_default: 30
    log_level: debug
    
  staging:
    model_default: claude-sonnet-4-5-20250929
    timeout_default: 60
    log_level: info
    
  production:
    model_default: claude-sonnet-4-5-20250929
    timeout_default: 120
    log_level: warning
    retry_default: 3

Version Your Workflows

name: customer-support
version: "2.1.0"  # Semantic versioning
changelog: |
  2.1.0 - Added escalation handling
  2.0.0 - Breaking: Changed output format
  1.1.0 - Added sentiment analysis
  1.0.0 - Initial release

CI/CD Pipeline

# .github/workflows/deploy.yaml
name: Deploy Workflows

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install fibonacci-sdk
      - run: fibonacci validate workflows/ --strict

  test:
    needs: validate
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - run: pip install fibonacci-sdk pytest
      - run: pytest tests/

  deploy-staging:
    needs: test
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    environment: staging
    steps:
      - uses: actions/checkout@v4
      - run: pip install fibonacci-sdk
      - run: fibonacci deploy --env staging
        env:
          FIBONACCI_API_KEY: ${{ secrets.FIBONACCI_API_KEY }}

  deploy-production:
    needs: deploy-staging
    if: github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    environment: production
    steps:
      - uses: actions/checkout@v4
      - run: pip install fibonacci-sdk
      - run: fibonacci deploy --env production
        env:
          FIBONACCI_API_KEY: ${{ secrets.FIBONACCI_API_KEY_PROD }}

Monitoring

Track Key Metrics

from fibonacci import Workflow
from fibonacci.metrics import MetricsCollector

metrics = MetricsCollector(
    backend="datadog",  # or "prometheus", "cloudwatch"
    config={"api_key": "..."}
)

workflow = Workflow(
    name="monitored-workflow",
    metrics=metrics
)

# Tracked automatically:
# - fibonacci.workflow.duration
# - fibonacci.workflow.success_rate
# - fibonacci.node.duration
# - fibonacci.node.token_usage
# - fibonacci.errors.count

Set Up Alerts

# alerts.yaml
alerts:
  - name: high-error-rate
    condition: fibonacci.errors.count > 10
    window: 5m
    severity: critical
    notify: ["pagerduty", "slack:#alerts"]
    
  - name: slow-response
    condition: fibonacci.workflow.duration.p95 > 30s
    window: 15m
    severity: warning
    notify: ["slack:#monitoring"]
    
  - name: high-token-usage
    condition: fibonacci.node.token_usage > 100000
    window: 1h
    severity: info
    notify: ["email:team@company.com"]

Checklist

Design

Workflows are focused and single-purpose
Names are descriptive and consistent
Documentation is complete
Dependencies are clearly defined

Reliability

Timeouts set on all nodes and workflows
Retry configuration with backoff
Fallback handlers for critical paths
Input validation schemas defined

Performance

Appropriate models selected for each task
Parallel execution where possible
Caching for expensive operations
Token usage optimized

Testing

Unit tests for individual nodes
Integration tests for workflows
Edge cases and error scenarios covered
Test data fixtures maintained

Deployment

Environment separation configured
Semantic versioning applied
CI/CD pipeline set up
Rollback procedures documented

Monitoring

Key metrics tracked
Alerts configured
Logging enabled
Dashboards created

Next Steps

Security Guide

Secure your workflows

Error Handling

Handle failures gracefully

Governance

Getting Started

Core Concepts

Advanced Guides

Workflow Design

Keep Workflows Focused

Use Meaningful Names

Document Your Workflows

Node Patterns

Chain of Responsibility

Parallel Processing

Critic Pattern for Quality

Prompt Engineering

Use Clear Structure

Include Examples (Few-Shot)

Specify Output Format

Error Handling

Always Set Timeouts

Implement Graceful Degradation

Use Retry with Backoff

Performance Optimization

Choose the Right Model

Optimize Token Usage

Cache Expensive Operations

Testing

Unit Test Nodes

Integration Test Workflows

Use Test Fixtures

Deployment

Environment Separation

Version Your Workflows

CI/CD Pipeline

Monitoring

Track Key Metrics

Set Up Alerts

Checklist

Next Steps

Security Guide

Error Handling

​Workflow Design

​Keep Workflows Focused

​Use Meaningful Names

​Document Your Workflows

​Node Patterns

​Chain of Responsibility

​Parallel Processing

​Critic Pattern for Quality

​Prompt Engineering

​Use Clear Structure

​Include Examples (Few-Shot)

​Specify Output Format

​Error Handling

​Always Set Timeouts

​Implement Graceful Degradation

​Use Retry with Backoff

​Performance Optimization

​Choose the Right Model

​Optimize Token Usage

​Cache Expensive Operations

​Testing

​Unit Test Nodes

​Integration Test Workflows

​Use Test Fixtures

​Deployment

​Environment Separation

​Version Your Workflows

​CI/CD Pipeline

​Monitoring

​Track Key Metrics

​Set Up Alerts

​Checklist

​Next Steps

Security Guide

Error Handling

Workflow Design

Keep Workflows Focused

Use Meaningful Names

Document Your Workflows

Node Patterns

Chain of Responsibility

Parallel Processing

Critic Pattern for Quality

Prompt Engineering

Use Clear Structure

Include Examples (Few-Shot)

Specify Output Format

Error Handling

Always Set Timeouts

Implement Graceful Degradation

Use Retry with Backoff

Performance Optimization

Choose the Right Model

Optimize Token Usage

Cache Expensive Operations

Testing

Unit Test Nodes

Integration Test Workflows

Use Test Fixtures

Deployment

Environment Separation

Version Your Workflows

CI/CD Pipeline

Monitoring

Track Key Metrics

Set Up Alerts

Checklist

Next Steps