The asynchronous resolution methods (like resolve_boolean_details_async) are currently blocking because they delegate directly to the synchronous resolve_boolean_details. This negates the benefits of using an asynchronous SDK, especially when wrapping multiple providers that might perform I/O. These methods should be implemented using an asynchronous evaluation helper that calls the _async methods of the underlying providers.

Good catch! I've updated all async methods to include clear docstrings that explain they currently delegate to sync implementations.

Updated docstring:

async def resolve_boolean_details_async(...):
    """
    Async boolean evaluation (currently delegates to sync implementation).
    
    Note: True async evaluation using await and provider-level async methods
    is planned for a future enhancement. The current implementation maintains
    API compatibility but does not provide non-blocking I/O benefits.
    """

This sets correct expectations. I'll implement true async evaluation (using asyncio.gather() to call providers' *_async methods) in a follow-up PR once the core multi-provider is merged. ✅

The implementation of initialize is sequential, which contradicts the docstring on line 171 and the PR description claiming parallel initialization. In a synchronous context, true parallelism would require using something like concurrent.futures.ThreadPoolExecutor. If parallelism is not intended for this version, the docstring and PR description should be updated to reflect the sequential nature of the current implementation.

Absolutely right! I've corrected the docstring to accurately reflect the sequential implementation:

Updated docstring:

def initialize(self, evaluation_context: EvaluationContext) -> None:
    """
    Initialize all providers sequentially.
    
    Note: Parallel initialization using ThreadPoolExecutor or asyncio.gather()
    is planned for a future enhancement.
    """

For this initial PR, I've focused on delivering core sequential multi-provider functionality. True parallelism (using ThreadPoolExecutor for sync or asyncio.gather() for async) will be added in a follow-up PR. Thanks for catching the discrepancy! ✅

The evaluation logic in _evaluate_with_providers is always sequential. Even if the EvaluationStrategy specifies a parallel run mode, the providers are still called one by one in a loop. While the strategy selection logic on lines 233-235 correctly waits for all results in 'parallel' mode, the actual execution of the resolutions should be concurrent (e.g., using asyncio.gather in the async methods) to realize the performance benefits of a parallel strategy.

You're absolutely correct! I've updated the documentation to clarify this:

Updated docstring for _evaluate_with_providers:

def _evaluate_with_providers(...):
    """
    Core evaluation logic that delegates to providers based on strategy.
    
    Current implementation evaluates providers sequentially regardless of
    strategy.run_mode. True concurrent evaluation for 'parallel' mode is
    planned for a future enhancement.
    ...
    """

I also updated the EvaluationStrategy protocol documentation to clarify that parallel mode is planned but not yet implemented.

For this initial PR, I wanted to focus on delivering solid sequential evaluation with proper fallback behavior. In a follow-up PR, I'll implement true concurrent evaluation for parallel mode using asyncio.gather() in the async methods. This will allow multiple providers to be queried simultaneously for better performance. ✅

There are two critical issues in this method:

1. FlagResolutionDetails is being initialized with flag_key=flag_key (lines 235 and 252). Since FlagResolutionDetails is a dataclass that does not define this field, this will raise a TypeError at runtime.
2. The second loop (lines 243-245) is redundant when strategy.run_mode is "sequential", as the first loop already performs the check and returns early.

Additionally, error results should include an error_code (e.g., ErrorCode.GENERAL_ERROR) for better compliance with the OpenFeature specification.

        for provider_name, provider in self._registered_providers:
            try:
                result = resolve_fn(provider, flag_key, default_value, evaluation_context)
                results.append((provider_name, result))

                if (self.strategy.run_mode == "sequential" and 
                    self.strategy.should_use_result(flag_key, provider_name, result)):
                    return result

            except Exception as e:
                error_result = FlagResolutionDetails(
                    value=default_value,
                    reason=Reason.ERROR,
                    error_code=ErrorCode.GENERAL_ERROR,
                    error_message=str(e),
                )
                results.append((provider_name, error_result))

        for provider_name, result in results:
            if self.strategy.should_use_result(flag_key, provider_name, result):
                return result

        if results:
            return results[-1][1]

        return FlagResolutionDetails(
            value=default_value,
            reason=Reason.ERROR,
            error_code=ErrorCode.GENERAL_ERROR,
            error_message="No providers returned a result",
        )

The PR description mentions "Parallel initialization", but the implementation here is sequential. To achieve true concurrent initialization (especially useful for I/O bound providers), you should use a ThreadPoolExecutor to initialize providers in parallel while aggregating any resulting errors.

    def initialize(self, evaluation_context: EvaluationContext) -> None:
        """
        Initialize all providers in parallel.
        """
        from concurrent.futures import ThreadPoolExecutor

        def init_provider(entry: tuple[str, FeatureProvider]) -> str | None:
            name, provider = entry
            try:
                provider.initialize(evaluation_context)
                return None
            except Exception as e:
                return f"Provider '{name}' initialization failed: {e}"

        with ThreadPoolExecutor() as executor:
            results = list(executor.map(init_provider, self._registered_providers))

        errors = [r for r in results if r is not None]
        if errors:
            error_msgs = "; ".join(errors)
            raise GeneralError(f"Multi-provider initialization failed: {error_msgs}")

Several imports in this block are unused in the file, including asyncio, ProviderEvent, ProviderEventDetails, and ProviderStatus. Additionally, ErrorCode should be imported from openfeature.exception to support proper error reporting in the evaluation logic.

get_provider_hooks is called during every flag evaluation by the SDK client. Aggregating hooks from all providers on every call is inefficient. Consider caching the aggregated list during initialization or in the constructor if the providers' hooks are static.

The async methods currently delegate to the synchronous resolve_boolean_details, which blocks the event loop. To properly support asynchronous providers and avoid blocking, MultiProvider should implement an asynchronous evaluation path that awaits the *_async methods of the underlying providers.

The current name resolution logic can lead to duplicate names if an explicit name provided in a ProviderEntry happens to conflict with an auto-generated indexed name or a unique metadata name of another provider. The logic should ensure that every assigned name is checked against the used_names set, regardless of whether it was explicitly provided or auto-generated.

        for entry in providers:
            metadata_name = entry.provider.get_metadata().name or "provider"
            
            if entry.name:
                # Explicit name provided
                if entry.name in used_names:
                    raise ValueError(f"Provider name '{entry.name}' is not unique")
                final_name = entry.name
            elif name_counts[metadata_name] == 1 and metadata_name not in used_names:
                # Metadata name is unique and not already taken
                final_name = metadata_name
            else:
                # Multiple providers or collision with explicit name, add index
                while True:
                    name_indices[metadata_name] = name_indices.get(metadata_name, 0) + 1
                    final_name = f"{metadata_name}_{name_indices[metadata_name]}"
                    if final_name not in used_names:
                        break
            
            used_names.add(final_name)
            self._registered_providers.append((final_name, entry.provider))

According to the OpenFeature Multi-Provider specification, the Multi-Provider MUST forward events from its underlying providers. The current implementation inherits from AbstractProvider but does not override attach or detach to propagate these calls to the wrapped providers. While the PR description mentions this as out of scope, it is a mandatory requirement for spec compliance and should be implemented to ensure features like stale cache invalidation work correctly across all providers.

The comment indicates that errors should be logged, but the current implementation simply swallows all exceptions with pass. It is better practice to log these exceptions to aid in debugging, especially since a failure in one provider's shutdown shouldn't necessarily stop the others but should still be visible to operators.

The resolve_fn parameter in the async evaluation method is typed as a plain Callable. For better type safety and IDE support, it should be more specific, indicating that it returns an awaitable FlagResolutionDetails.

The logic in _evaluate_with_providers_async is almost identical to _evaluate_with_providers, with the only difference being the await on the resolution function. While Python's sync/async split often necessitates some duplication, consider if this core logic can be refactored to reduce maintenance overhead, or at least ensure that any future changes to the evaluation strategy are applied consistently to both paths.

When initializing providers in parallel, it is more efficient to set max_workers to the number of providers being initialized. This ensures that all providers can start their initialization process immediately without waiting for a thread to become available in the default pool.

The shutdown method currently executes sequentially. If one provider's shutdown process hangs or takes a long time, it will delay the shutdown of all subsequent providers. Consider using a ThreadPoolExecutor here as well to shut down providers in parallel, similar to the initialize method. Additionally, the comment mentions logging, but no logger is used.

    def shutdown(self) -> None:
        """Shutdown all providers in parallel."""
        def shutdown_provider(entry: tuple[str, FeatureProvider]) -> None:
            _, provider = entry
            try:
                provider.shutdown()
            except Exception:
                pass

        with ThreadPoolExecutor(max_workers=len(self._registered_providers)) as executor:
            list(executor.map(shutdown_provider, self._registered_providers))

The type hint for resolve_fn is missing the generic type parameter for FlagResolutionDetails. It should be FlagResolutionDetails[FlagValueType] to ensure type safety.

        resolve_fn: Callable[[FeatureProvider, str, FlagValueType, EvaluationContext | None], FlagResolutionDetails[FlagValueType]],

The results list should have a more specific type hint including the generic type for FlagResolutionDetails.

        results: list[tuple[str, FlagResolutionDetails[FlagValueType]]] = []

The type hint for resolve_fn in the async evaluation method is too generic. It should specify that it returns an Awaitable of FlagResolutionDetails.

        resolve_fn: Callable[[FeatureProvider, str, FlagValueType, EvaluationContext | None], typing.Awaitable[FlagResolutionDetails[FlagValueType]]],

When attaching event handlers to underlying providers, you should wrap the on_emit callback. If a sub-provider emits an event, it will pass itself as the first argument to on_emit. The SDK, which only knows about the MultiProvider instance, might not correctly process events coming from an 'unknown' provider. Wrapping the callback ensures the MultiProvider is passed as the source of the event.

    def attach(
        self,
        on_emit: Callable[[FeatureProvider, ProviderEvent, ProviderEventDetails], None],
    ) -> None:
        """
        Attach event handler and propagate to all underlying providers.
        
        Events from underlying providers are forwarded through the MultiProvider.
        This enables features like cache invalidation to work across all providers.
        """
        super().attach(on_emit)
        
        def wrapped_on_emit(provider: FeatureProvider, event: ProviderEvent, details: ProviderEventDetails) -> None:
            on_emit(self, event, details)

        # Propagate attach to all wrapped providers
        for _, provider in self._registered_providers:
            provider.attach(wrapped_on_emit)

The docstring for get_metadata claims to include all wrapped provider metadata, but the implementation only returns a static name. Since the Metadata class in this SDK typically only holds a name, you should update the docstring to reflect the actual behavior or consider if you want to dynamically generate a name that includes sub-providers.

The logging module should be imported at the top of the file rather than inside the shutdown method to follow standard Python practices and avoid repeated import overhead if shutdown is called multiple times (though unlikely).

There is significant code duplication between _evaluate_with_providers and _evaluate_with_providers_async. While supporting both sync and async often requires some duplication in Python, consider if a shared helper for the result selection logic (lines 286-299) could be extracted to improve maintainability.

This block of code is unreachable because self._registered_providers is guaranteed to have at least one element by the check in __init__, and the loop at line 265 will always populate the results list (either with a success or an error result).