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The CriticNode class evaluates the output of a target node against a list of criteria. It is automatically added as a dependent of the target node so it always runs after the node it evaluates.

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Constructor

from fibonacci import CriticNode

node = CriticNode(
    id="evaluate_report",
    name="Evaluate Report Quality",
    target_node="generate_report",
    criteria=["clarity", "completeness", "accuracy"]
)

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Parameters

ParameterTypeDefaultDescription
idstrRequiredUnique node identifier (lowercase letters, numbers, underscores, hyphens)
namestrRequiredHuman-readable node name
target_nodestrRequiredID of the node whose output will be evaluated
criterialist[str]["quality", "correctness", "completeness"]Evaluation criteria labels
dependencieslist[str]autotarget_node is automatically added; add extras here if needed
enable_retryboolFalseRetry the critic node on failure
max_retriesint3Maximum retry attempts (used when enable_retry=True)
retry_delayfloat1.0Initial delay in seconds between retries
The target_node is automatically added to dependencies. You do not need to list it manually.

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Basic Usage

from fibonacci import Workflow, LLMNode, CriticNode

wf = Workflow(name="quality-assured")

# Content generator
writer = LLMNode(
    id="writer",
    name="Write Email",
    instruction="Write a professional email about: {{input.topic}}"
)

# Quality evaluator
critic = CriticNode(
    id="critic",
    name="Evaluate Email Quality",
    target_node="writer",
    criteria=[
        "professional tone throughout",
        "clear and concise language",
        "proper grammar and spelling",
        "appropriate greeting and closing"
    ]
)

wf.add_nodes([writer, critic])

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Default Criteria

When you omit criteria, the node uses three built-in labels: 
# These two are equivalent
critic_a = CriticNode(
    id="critic_a",
    name="Default Critic",
    target_node="writer"
)

critic_b = CriticNode(
    id="critic_b",
    name="Explicit Critic",
    target_node="writer",
    criteria=["quality", "correctness", "completeness"]
)

​
Custom Criteria

Provide specific, measurable criteria for your use case:

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Content Writing

content_critic = CriticNode(
    id="content_critic",
    name="Content Quality Check",
    target_node="blog_writer",
    criteria=[
        "engaging opening hook",
        "clear value proposition",
        "actionable takeaways",
        "appropriate length for the audience"
    ]
)

​
Code Review

code_critic = CriticNode(
    id="code_critic",
    name="Code Review",
    target_node="code_generator",
    criteria=[
        "syntactically correct",
        "follows best practices",
        "includes error handling",
        "is well-documented"
    ]
)

​
Data Extraction

extraction_critic = CriticNode(
    id="extraction_critic",
    name="Extraction Accuracy Check",
    target_node="extractor",
    criteria=[
        "all required fields extracted",
        "values are in the correct format",
        "no hallucinated information"
    ]
)

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Writing Good Criteria

# ✅ Good — specific, observable
criteria=[
    "response is between 100 and 200 words",
    "includes at least one concrete example",
    "no grammatical errors"
]

# ❌ Vague — hard to evaluate objectively
criteria=[
    "good quality",
    "sounds nice"
]

# For customer-facing content
criteria=[
    "friendly and empathetic tone",
    "directly addresses the user's question",
    "offers a clear next step"
]

# For internal analysis
criteria=[
    "cites data from the source",
    "identifies at least 3 trends",
    "recommends concrete actions"
]

​
Retry Configuration

critic = CriticNode(
    id="critic",
    name="Evaluator",
    target_node="writer"
).with_retry(max_retries=3, delay=1.0)

​
YAML Configuration

nodes:
  - id: quality_check
    name: Quality Check
    type: critic
    target_node: writer
    evaluation_criteria:
      - professional tone
      - clear language
      - grammar and spelling
    dependencies:
      - writer

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Complete Example

from fibonacci import Workflow, LLMNode, CriticNode

wf = Workflow(name="blog-writer")

# Generate blog post
writer = LLMNode(
    id="writer",
    name="Write Blog Post",
    instruction="""
    Write a blog post about {{input.topic}}.

    Target audience: {{input.audience}}
    Desired length: approximately {{input.word_count}} words
    Tone: {{input.tone}}
    """,
    model="claude-sonnet-4-6"
)

# Evaluate quality
critic = CriticNode(
    id="quality_check",
    name="Editorial Quality Check",
    target_node="writer",
    criteria=[
        "engaging from the first paragraph",
        "well-researched and accurate",
        "writing style matches the target audience",
        "close to the requested word count",
        "tone is consistent throughout",
        "includes actionable takeaways",
        "compelling conclusion"
    ]
)

wf.add_nodes([writer, critic])

result = wf.run(input_data={
    "topic": "Introduction to Machine Learning",
    "audience": "business professionals",
    "word_count": 800,
    "tone": "informative yet approachable"
})

print(result.output_data["quality_check"])