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Documentation Index

Fetch the complete documentation index at: https://docs.mellea.ai/llms.txt

Use this file to discover all available pages before exploring further.

Prerequisites: Instruct, Validate, Repair complete, pip install mellea, Ollama running locally. act() is the generic method on MelleaSession that runs any Component and returns a result. Every other session method is built on it:
  • instruct() creates an Instruction component and passes it to act()
  • chat() creates a Message component and passes it to act() with strategy=None
  • query() and transform() wrap mified objects into components and pass them to act()
Use act() when you need to work directly with a component — for custom components, fine-grained control, or building your own inference loops.

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Three levels of abstraction

These three snippets all produce the same result: 
import mellea
from mellea import start_session
from mellea.stdlib import functional as mfuncs
from mellea.stdlib.components import Instruction
from mellea.stdlib.context import SimpleContext

# Level 1: instruct() — builds the Instruction for you
m = start_session()
result = m.instruct("Write a haiku about the ocean.")

# Level 2: act() — you build the Instruction, session threads context
m = start_session()
instruction = Instruction(description="Write a haiku about the ocean.")
result = m.act(instruction)

# Level 3: mfuncs.act() — you manage context and backend directly
ctx = SimpleContext()
backend = mellea.start_session().backend
instruction = Instruction(description="Write a haiku about the ocean.")
result, new_ctx = mfuncs.act(instruction, context=ctx, backend=backend)

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Basic usage

Pass any Component to act(). It returns a ModelOutputThunk: 
from mellea import start_session
from mellea.stdlib.components import Instruction

m = start_session()
instruction = Instruction(
    description="List three facts about Mars.",
    requirements=["Each fact must be on its own line."],
)
result = m.act(instruction)
print(str(result))
# Output will vary — LLM responses depend on model and temperature.

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Working with Messages

Message is a component with a role and content string. Pass strategy=None to skip the IVR loop — this is what chat() does internally: 
from mellea import start_session
from mellea.stdlib.components import Message

m = start_session()
result = m.act(Message("user", "What is the capital of France?"), strategy=None)
print(str(result))
# Output will vary — LLM responses depend on model and temperature.

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Working with Documents

Pass document content directly in a Message: 
from mellea import start_session
from mellea.stdlib.components import Message

m = start_session()
msg = Message("user", "Summarize: Mellea is a framework for structured LLM programming.")
result = m.act(msg, strategy=None)
print(str(result))
# Output will vary — LLM responses depend on model and temperature.
Note: The base Document class does not yet support being embedded inside a Message (#636). For rich document processing (PDFs, tables), use RichDocument from mellea.stdlib.components.docs — see Working with Data.
For rich document processing (PDFs, tables), see Working with Data.

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Validation and sampling strategies

act() accepts the same requirements and strategy parameters as instruct(). The default is RejectionSamplingStrategy(loop_budget=2): 
from mellea import start_session
from mellea.core import Requirement
from mellea.stdlib.components import Instruction
from mellea.stdlib.sampling import RejectionSamplingStrategy

m = start_session()
instruction = Instruction(description="List three facts about Mars.")

candidate = m.act(
    instruction,
    requirements=[Requirement("Each fact must be on its own line.")],
    strategy=RejectionSamplingStrategy(loop_budget=3),
    return_sampling_results=True,
)

if candidate.success:
    print(str(candidate.result))
else:
    print(str(candidate.sample_generations[0].value))
See Instruct, Validate, Repair and Inference-Time Scaling for full details on requirements and validation.

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Structured output

Pass a Pydantic BaseModel as the format parameter for constrained decoding: 
from pydantic import BaseModel
from mellea import start_session
from mellea.stdlib.components import Instruction

class Planet(BaseModel):
    name: str
    diameter_km: float
    has_rings: bool

m = start_session()
instruction = Instruction(description="Describe Saturn.")
result = m.act(instruction, format=Planet)
print(result.value)  # A Planet instance
# Output will vary — LLM responses depend on model and temperature.

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The functional API

Advanced: mellea.stdlib.functional exposes act() and aact() as standalone functions. You pass context and backend explicitly instead of relying on a session to thread them. 
from mellea.backends.ollama import OllamaModelBackend
from mellea.stdlib import functional as mfuncs
from mellea.stdlib.components import Instruction
from mellea.stdlib.context import SimpleContext

backend = OllamaModelBackend(model_id="phi4-mini:latest")
ctx = SimpleContext()

instruction = Instruction(description="Explain gravity in one sentence.")
result, new_ctx = mfuncs.act(instruction, context=ctx, backend=backend)
print(str(result))
# Output will vary — LLM responses depend on model and temperature.
The functional act() returns a (ModelOutputThunk, Context) tuple. With return_sampling_results=True it returns (SamplingResult, Context). Use the functional API when you need to branch context for parallel explorations or build custom inference loops. For most use cases, the session API (m.act()) is simpler.

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Async with aact()

aact() is the async counterpart. Same signature, same return types: 
import asyncio
from mellea import start_session
from mellea.stdlib.components import Instruction

async def main():
    m = start_session()
    instruction = Instruction(description="Write a limerick about debugging.")
    result = await m.aact(instruction)
    print(str(result))
    # Output will vary — LLM responses depend on model and temperature.

asyncio.run(main())
The functional async version is mfuncs.aact(): 
result, new_ctx = await mfuncs.aact(instruction, context=ctx, backend=backend)
For parallel generation and streaming patterns, see Async and Streaming.
See also: Async and Streaming | Inference-Time Scaling | Instruct, Validate, Repair