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+ Mellea
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+ Mellea
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+ build enterprise AI without guesswork
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+ GitHub
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+ Mellea
+Build reliable generative programs with our library that merges the power of LLMs with the predictability of engineering.
+ + Get started + + +
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+
+ 
+ 
+
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+ -
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+ + Python not Prose + Write object-oriented code instead of free-form text. ++
+
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+ + Requirements Driven + Define requirements that ensure good model responses. ++
+
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+ + Predictable and Resilient + Provide structures for failure detection and recovery. ++
+
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+ + MCP and A2A Compatible + Connect Mellea programs with other agent frameworks. ++
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+ 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
+
+
+
+
+
+
+ Build composable Gen AI using tried-and-true abstractions.
+ + Learn more + + +
+
+
+
+
+ @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)
+
+
+
+
+
+ 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))
+
+
+
+
+
+ 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