LLM-powered Python decorators for intelligent function behavior. Add validation, mocking, and output checking to your functions with a single line of code.

Installation

{pip, uv} install mores

Setup

Configure

export MORES_API_KEY=gsk_your_key_here
export MORES_SERVER_URL=https://mores.fulcrumresearch.ai

Decorators

All decorators support two modes, depending on latency and intelligence requirements, passed in as keyword arguments - mode=fast and mode=slow.

@guard - Pre-execution Validation

Validates function calls against custom rules before execution.

Basic Usage:

@guard(rules=[
    "Never allow deletion",
    "Only allow amounts under 100"
])
def transfer(amount: float, delete: bool = False):
    """Transfer an amount."""
    if delete:
        return "deleted"
    return f"transferred {amount}"

result = transfer(50, delete=False)  # ✓ Returns Value
result = transfer(200, delete=False)  # ✗ Returns Rejected
result = transfer(50, delete=True)   # ✗ Returns Rejected

Processing Guard Results:

By default, @guard returns a Maybe type (either Value or Rejected). You can handle this in the following way.

1. Pattern matching with match/case (default):

   result = transfer(50, delete=False)
   match result:
    case Value(data=data):
        # Function executed successfully
        print(f"Success: {data}")
    case Rejected(reason=reason):
        # Call was rejected
        print(f"Error: {reason}")
   

You can also @guard(..., raise_reject=True) to return the raw output, and raise if an error is caught.

@mock - LLM-Generated Output

Replaces function execution with AI-generated mock output based on the function signature and documentation.

from mores import mock

@mock()
def calculate_fibonacci(n: int):
    """Calculate the nth Fibonacci number."""
    pass  # Implementation doesn't matter

result = calculate_fibonacci(10)
print(result)  # LLM-generated Fibonacci number

@doubt - Post-execution Validation

Validates that function output is correct given its inputs and implementation. Raises DoubtError if the output appears incorrect.

from mores import doubt, DoubtError

@doubt()
def add(a: int, b: int):
    """Add two numbers."""
    return a + b

# Correct implementation passes
result = add(2, 3)  # Returns 5, no error

# Incorrect implementation raises error
@doubt()
def broken_add(a: int, b: int):
    """Add two numbers."""
    return a * b  # Wrong! Multiplying instead

try:
    broken_add(2, 3)
except DoubtError as e:
    print(e.reason)  # "The function should add but it's multiplying..."