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Prerequisites: Quick Start complete, pip install mellea, Ollama running locally.
Concept overview: Generative functions explains the design and trade-offs.
@generative is the idiomatic way to define type-safe LLM functions in Mellea. You write a function signature with type hints and a docstring — Mellea generates the implementation, calls the backend, and parses the output into the declared return type.

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Basic @generative

from typing import Literal
from mellea import generative, start_session

@generative
def classify_sentiment(text: str) -> Literal["positive", "negative"]:
    """Classify the sentiment of the input text as 'positive' or 'negative'."""

m = start_session()
sentiment = classify_sentiment(m, text="I love this!")
print(sentiment)
# Output will vary — LLM responses depend on model and temperature.
# Expected: "positive"
The function body is empty (or ...). The decorator generates a prompt from the signature and docstring, calls the backend, and returns a value of the declared type. The first argument is always the MelleaSession. Literal types constrain the model to output one of the allowed values.

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Pydantic structured output

Return complex structured objects using Pydantic models: 
from pydantic import BaseModel
from mellea import generative, start_session

class Thought(BaseModel):
    step_name: str
    step_content: str

class ChainOfThought(BaseModel):
    chain_name: str
    step_by_step_solution: list[Thought]

@generative
def solve_step_by_step(question: str) -> ChainOfThought:
    """Generate a chain-of-thought solution for the question,
    decomposing reasoning into named, detailed steps."""

m = start_session()
response = solve_step_by_step(m, question="If I have $50 and spend $12, how much is left?")
for step in response.step_by_step_solution:
    print(f"{step.step_name}: {step.step_content}")
# Output will vary — LLM responses depend on model and temperature.
The model output is automatically parsed and validated against the Pydantic schema. If parsing fails, the IVR loop retries.

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Pre- and post-conditions

Add runtime constraints with precondition_requirements (checked before generation) and requirements (checked after). Both accept the same requirement types as instruct(): 
from typing import Literal
from mellea import generative, start_session
from mellea.stdlib.sampling import RejectionSamplingStrategy

@generative
def classify_sentiment(text: str) -> Literal["positive", "negative", "unknown"]:
    """Classify the sentiment of the text."""

m = start_session()
result = classify_sentiment(
    m,
    text="I love this!",
    precondition_requirements=["the text argument should be fewer than 100 words"],
    requirements=["avoid classifying as unknown"],
    strategy=RejectionSamplingStrategy(),
)
print(result)
# Output will vary — LLM responses depend on model and temperature.
If a precondition fails, PreconditionException is raised immediately — the model is never called: 
from mellea import generative, start_session
from mellea.core import Requirement
from mellea.stdlib.components.genslot import PreconditionException
from mellea.stdlib.requirements import simple_validate
from typing import Literal

@generative
def classify_sentiment(text: str) -> Literal["positive", "negative"]:
    """Classify the sentiment of the text."""

m = start_session()
try:
    result = classify_sentiment(
        m,
        text="I love this!",
        precondition_requirements=[
            Requirement(
                "text must be a single word",
                validation_fn=simple_validate(
                    lambda x: (len(x.split()) == 1, "Input has more than one word.")
                ),
            )
        ],
    )
except PreconditionException as e:
    print(f"Precondition failed: {e}")
    for val_result in e.validation:
        print(f"  - {val_result.reason}")

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Composing generative functions

Chain multiple @generative functions to build typed pipelines. The output of one call becomes the input to the next: 
from typing import Literal
from mellea import generative, start_session

@generative
def summarize_meeting(transcript: str) -> str:
    """Summarize the key points of the meeting transcript."""

@generative
def contains_actionable_risks(summary: str) -> Literal["yes", "no"]:
    """Determine whether the summary references business risks."""

@generative
def generate_risk_mitigation(summary: str) -> str:
    """Generate risk mitigation recommendations based on the summary."""

transcript = "..."  # your meeting transcript

m = start_session()
summary = summarize_meeting(m, transcript=transcript)
if contains_actionable_risks(m, summary=summary) == "yes":
    mitigation = generate_risk_mitigation(m, summary=summary)
    print(mitigation)
# Output will vary — LLM responses depend on model and temperature.
Each call is an independent LLM invocation. The typed interface enforces that each step receives and produces valid data, making pipelines easier to test and debug.

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Chain-of-thought reasoning

Advanced: This section shows a performance-oriented pattern for math and reasoning tasks.
The Pydantic structured output pattern works well for explicit chain-of-thought (CoT) reasoning. Separating the reasoning step from the answer extraction step can significantly improve accuracy on tasks like GSM8K. 
from pydantic import BaseModel
from mellea import generative, start_session

class Thought(BaseModel):
    step_name: str
    step_content: str

class ChainOfThought(BaseModel):
    chain_name: str
    step_by_step_solution: list[Thought]

@generative
def compute_chain_of_thought(question: str) -> ChainOfThought:
    """Generate a comprehensive chain-of-thought solution for the question,
    tracking cumulative state at every step."""

@generative
def extract_final_answer(question: str, chain_of_thought: ChainOfThought) -> int:
    """Extract the final numeric answer from the chain-of-thought solution."""

m = start_session()
question = "If I have $50 and spend $12, how much is left?"
cot = compute_chain_of_thought(m, question=question)
answer = extract_final_answer(m, question=question, chain_of_thought=cot)
print(answer)
# Output will vary — LLM responses depend on model and temperature.
# Expected: 38
The structured Thought titles can be surfaced in a UI for observability into the model’s reasoning process.
See also: Generative Functions | Enforce Structured Output | Write Custom Verifiers