Mellea.ai build enterprise AI without guesswork

uv pip install mellea

GitHub

Write simple, effective program in place of “instruction essays”

Unpredictability ManagementProvide structures for failure detection and recovery for LLM outputs.
Robust and ComposableRequirement-driven pipelines deliver rock-solid libraries and apps.
Ready to ScaleSwap engines and models effortlessly. Scale in a single line.
Efficiency in Model UsageGenerative programs can do Big Model Things without Big model hardware.
Model AgnosticMore precise and predictable outcomes, regardless of the initial model.
Reusability & PortabilityPortable libraries that simplify prompts and optimize implementations.

Instruct, Validate, Repair

Clean interfaces for Gen AI output validation and sampling strategies.

Learn more

import mellea
from mellea.stdlib.sampling import RejectionSamplingStrategy


def write_email_with_strategy(m: mellea.MelleaSession, name: str, notes: str) -> str:
    email_candidate = m.instruct(
        f"Write an email to {name} using the notes following: {notes}.",
        requirements=[
            "The email should have a salutation.",
            "Use a formal tone.",
        ],
        strategy=RejectionSamplingStrategy(loop_budget=3),
        return_sampling_results=True,
    )

    if email_candidate.success:
        return str(email_candidate.result)

    # If sampling fails, use the first generation
    print("Expect sub-par result.")
    return email_candidate.sample_generations[0].value
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Generative Slots

Build composable Gen AI using tried-and-true abstractions.

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

sentiment = classify_sentiment(m, text=customer_review)

if sentiment == "positive":
    msg = m.instruct("Thank the customer for their post")
else:
    msg = m.instruct(
       description="Apologize for the customer's negative experience and offer a 5% discount for their next visit",
       grounding_context={"review": customer_review}
    )

post_response(msg)
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MObjects

Seamlessly sprinkle GenAI into existing systems.

Learn more

import mellea
from mellea.stdlib.mify import mify, MifiedProtocol
import pandas
from io import StringIO


@mify(fields_include={"table"}, template="{{ table }}")
class MyCompanyDatabase:
  table: str = """| Store      | Sales   |
                    | ---------- | ------- |
                    | Northeast  | $250    |
                    | Southeast  | $80     |
                    | Midwest    | $420    |"""

  def transpose(self):
    pandas.read_csv(
      StringIO(self.table),
      sep='|',
      skipinitialspace=True,
      header=0,
      index_col=False
    )


m = mellea.start_session()
db = MyCompanyDatabase()
assert isinstance(db, MifiedProtocol)
answer = m.query(db, "What were sales for the Northeast branch this month?")
print(str(answer))
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import mellea
from mellea.stdlib.sampling import RejectionSamplingStrategy


def write_email_with_strategy(m: mellea.MelleaSession, name: str, notes: str) -> str:
    email_candidate = m.instruct(
        f"Write an email to {name} using the notes following: {notes}.",
        requirements=[
            "The email should have a salutation.",
            "Use a formal tone.",
        ],
        strategy=RejectionSamplingStrategy(loop_budget=3),
        return_sampling_results=True,
    )

    if email_candidate.success:
        return str(email_candidate.result)

    # If sampling fails, use the first generation
    print("Expect sub-par result.")
    return email_candidate.sample_generations[0].value
Copy