{"author":null,"author_email":"OpenFeature ","bugtrack_url":null,"classifiers":["License :: OSI Approved :: Apache Software License","Programming Language :: Python","Programming Language :: Python :: 3"],"description":"\n\n

\n \n \n $\"OpenFeature$ \n \n

\n\n

OpenFeature Python SDK

\n\n\n\n

\n\n \n $\"Specification\"$ \n \n\n \n\n \n $\"Latest$ \n \n\n \n
\n \n $\"Build$ \n \n\n \n $\"Codecov\"$ \n \n\n \n $\"Min$ =3.9-blue.svg\" />\n \n\n \n $\"Repo$ \n \n

\n\n\n[OpenFeature](https://openfeature.dev) is an open specification that provides a vendor-agnostic, community-driven API for feature flagging that works with your favorite feature flag management tool.\n\n\n\n## 🚀 Quick start\n\n### Requirements\n\n- Python 3.9+\n\n### Install\n\n\n\n#### Pip install\n\n```bash\npip install openfeature-sdk==0.8.1\n```\n\n#### requirements.txt\n\n```bash\nopenfeature-sdk==0.8.1\n```\n\n```python\npip install -r requirements.txt\n```\n\n\n\n### Usage\n\n```python\nfrom openfeature import api\nfrom openfeature.provider.in_memory_provider import InMemoryFlag, InMemoryProvider\n\n# flags defined in memory\nmy_flags = {\n \"v2_enabled\": InMemoryFlag(\"on\", {\"on\": True, \"off\": False})\n}\n\n# configure a provider\napi.set_provider(InMemoryProvider(my_flags))\n\n# create a client\nclient = api.get_client()\n\n# get a bool flag value\nflag_value = client.get_boolean_value(\"v2_enabled\", False)\nprint(\"Value: \" + str(flag_value))\n```\n\n## 🌟 Features\n\n| Status | Features | Description |\n|--------|---------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|\n| ✅ | [Providers](#providers) | Integrate with a commercial, open source, or in-house feature management tool. |\n| ✅ | [Targeting](#targeting) | Contextually-aware flag evaluation using [evaluation context](https://openfeature.dev/docs/reference/concepts/evaluation-context). |\n| ✅ | [Hooks](#hooks) | Add functionality to various stages of the flag evaluation life-cycle. |\n| ✅ | [Logging](#logging) | Integrate with popular logging packages. |\n| ✅ | [Domains](#domains) | Logically bind clients with providers. |\n| ✅ | [Eventing](#eventing) | React to state changes in the provider or flag management system. |\n| ✅ | [Shutdown](#shutdown) | Gracefully clean up a provider during application shutdown. |\n| ✅ | [Transaction Context Propagation](#transaction-context-propagation) | Set a specific [evaluation context](https://openfeature.dev/docs/reference/concepts/evaluation-context) for a transaction (e.g. an HTTP request or a thread) |\n| ✅ | [Extending](#extending) | Extend OpenFeature with custom providers and hooks. |\n\n_{Implemented: ✅ | In-progress: ⚠️ | Not implemented yet: ❌}\n\n### Providers\n\n[Providers](https://openfeature.dev/docs/reference/concepts/provider) are an abstraction between a flag management system and the OpenFeature SDK.\nLook [here](https://openfeature.dev/ecosystem/?instant_search%5BrefinementList%5D%5Btype%5D%5B0%5D=Provider&instant_search%5BrefinementList%5D%5Btechnology%5D%5B0%5D=Python) for a complete list of available providers.\nIf the provider you're looking for hasn't been created yet, see the [develop a provider](#develop-a-provider) section to learn how to build it yourself.\n\nOnce you've added a provider as a dependency, it can be registered with OpenFeature like this:\n\n```python\nfrom openfeature import api\nfrom openfeature.provider.no_op_provider import NoOpProvider\n\napi.set_provider(NoOpProvider())\nopen_feature_client = api.get_client()\n```\n\nIn some situations, it may be beneficial to register multiple providers in the same application.\nThis is possible using [domains](#domains), which is covered in more detail below.\n\n### Targeting\n\nSometimes, the value of a flag must consider some dynamic criteria about the application or user, such as the user's location, IP, email address, or the server's location.\nIn OpenFeature, we refer to this as [targeting](https://openfeature.dev/specification/glossary#targeting).\nIf the flag management system you're using supports targeting, you can provide the input data using the [evaluation context](https://openfeature.dev/docs/reference/concepts/evaluation-context).\n\n```python\nfrom openfeature.api import (\n get_client,\n get_provider,\n set_provider,\n get_evaluation_context,\n set_evaluation_context,\n)\n\nglobal_context = EvaluationContext(\n targeting_key=\"targeting_key1\", attributes={\"application\": \"value1\"}\n)\nrequest_context = EvaluationContext(\n targeting_key=\"targeting_key2\", attributes={\"email\": request.form['email']}\n)\n\n## set global context\nset_evaluation_context(global_context)\n\n# merge second context\nclient = get_client(name=\"No-op Provider\")\nclient.get_string_value(\"email\", \"fallback\", request_context)\n```\n\n### Hooks\n\n[Hooks](https://openfeature.dev/docs/reference/concepts/hooks) allow for custom logic to be added at well-defined points of the flag evaluation life-cycle.\nLook [here](https://openfeature.dev/ecosystem/?instant_search%5BrefinementList%5D%5Btype%5D%5B0%5D=Hook&instant_search%5BrefinementList%5D%5Btechnology%5D%5B0%5D=Python) for a complete list of available hooks.\nIf the hook you're looking for hasn't been created yet, see the [develop a hook](#develop-a-hook) section to learn how to build it yourself.\n\nOnce you've added a hook as a dependency, it can be registered at the global, client, or flag invocation level.\n\n```python\nfrom openfeature.api import add_hooks\nfrom openfeature.flag_evaluation import FlagEvaluationOptions\n\n# set global hooks at the API-level\nadd_hooks([MyHook()])\n\n# or configure them in the client\nclient = OpenFeatureClient()\nclient.add_hooks([MyHook()])\n\n# or at the invocation-level\noptions = FlagEvaluationOptions(hooks=[MyHook()])\nclient.get_boolean_flag(\"my-flag\", False, flag_evaluation_options=options)\n```\n\n### Logging\n\nThe OpenFeature SDK logs to the `openfeature` logger using the `logging` package from the Python Standard Library.\n\n### Domains\n\nClients can be assigned to a domain.\nA domain is a logical identifier which can be used to associate clients with a particular provider.\nIf a domain has no associated provider, the global provider is used.\n\n```python\nfrom openfeature import api\n\n# Registering the default provider\napi.set_provider(MyProvider());\n# Registering a provider to a domain\napi.set_provider(MyProvider(), \"my-domain\");\n\n# A client bound to the default provider\ndefault_client = api.get_client();\n# A client bound to the MyProvider provider\ndomain_scoped_client = api.get_client(\"my-domain\");\n```\n\nDomains can be defined on a provider during registration.\nFor more details, please refer to the [providers](#providers) section.\n\n### Eventing\n\nEvents allow you to react to state changes in the provider or underlying flag management system, such as flag definition changes, provider readiness, or error conditions. Initialization events (PROVIDER_READY on success, PROVIDER_ERROR on failure) are dispatched for every provider. Some providers support additional events, such as PROVIDER_CONFIGURATION_CHANGED.\n\nPlease refer to the documentation of the provider you're using to see what events are supported.\n\n```python\nfrom openfeature import api\nfrom openfeature.provider import ProviderEvent\n\ndef on_provider_ready(event_details: EventDetails):\n print(f\"Provider {event_details.provider_name} is ready\")\n\napi.add_handler(ProviderEvent.PROVIDER_READY, on_provider_ready)\n\nclient = api.get_client()\n\ndef on_provider_ready(event_details: EventDetails):\n print(f\"Provider {event_details.provider_name} is ready\")\n\nclient.add_handler(ProviderEvent.PROVIDER_READY, on_provider_ready)\n```\n\n### Transaction Context Propagation\n\nTransaction context is a container for transaction-specific evaluation context (e.g. user id, user agent, IP).\nTransaction context can be set where specific data is available (e.g. an auth service or request handler) and by using the transaction context propagator it will automatically be applied to all flag evaluations within a transaction (e.g. a request or thread).\n\nYou can implement a different transaction context propagator by implementing the `TransactionContextPropagator` class exported by the OpenFeature SDK.\nIn most cases you can use `ContextVarsTransactionContextPropagator` as it works for `threads` and `asyncio` using [Context Variables](https://peps.python.org/pep-0567/).\n\nThe following example shows a **multithreaded** Flask application using transaction context propagation to propagate the request ip and user id into request scoped transaction context.\n\n```python\nfrom flask import Flask, request\nfrom openfeature import api\nfrom openfeature.evaluation_context import EvaluationContext\nfrom openfeature.transaction_context import ContextVarsTransactionContextPropagator\n\n# Initialize the Flask app\napp = Flask(__name__)\n\n# Set the transaction context propagator\napi.set_transaction_context_propagator(ContextVarsTransactionContextPropagator())\n\n# Middleware to set the transaction context\n# You can call api.set_transaction_context anywhere you have information,\n# you want to have available in the code-paths below the current one.\n@app.before_request\ndef set_request_transaction_context():\n ip = request.headers.get(\"X-Forwarded-For\", request.remote_addr)\n user_id = request.headers.get(\"User-Id\") # Assuming we're getting the user ID from a header\n evaluation_context = EvaluationContext(targeting_key=user_id, attributes={\"ipAddress\": ip})\n api.set_transaction_context(evaluation_context)\n\ndef create_response() -> str:\n # This method can be anywhere in our code.\n # The feature flag evaluation will automatically contain the transaction context merged with other context\n new_response = api.get_client().get_string_value(\"response-message\", \"Hello User!\")\n return f\"Message from server: {new_response}\"\n\n# Example route where we use the transaction context\n@app.route('/greeting')\ndef some_endpoint():\n return create_response()\n```\n\nThis also works for asyncio based implementations e.g. FastApi as seen in the following example:\n\n```python\nfrom fastapi import FastAPI, Request\nfrom openfeature import api\nfrom openfeature.evaluation_context import EvaluationContext\nfrom openfeature.transaction_context import ContextVarsTransactionContextPropagator\n\n# Initialize the FastAPI app\napp = FastAPI()\n\n# Set the transaction context propagator\napi.set_transaction_context_propagator(ContextVarsTransactionContextPropagator())\n\n# Middleware to set the transaction context\n@app.middleware(\"http\")\nasync def set_request_transaction_context(request: Request, call_next):\n ip = request.headers.get(\"X-Forwarded-For\", request.client.host)\n user_id = request.headers.get(\"User-Id\") # Assuming we're getting the user ID from a header\n evaluation_context = EvaluationContext(targeting_key=user_id, attributes={\"ipAddress\": ip})\n api.set_transaction_context(evaluation_context)\n response = await call_next(request)\n return response\n\ndef create_response() -> str:\n # This method can be located anywhere in our code.\n # The feature flag evaluation will automatically include the transaction context merged with other context.\n new_response = api.get_client().get_string_value(\"response-message\", \"Hello User!\")\n return f\"Message from server: {new_response}\"\n\n# Example route where we use the transaction context\n@app.get('/greeting')\nasync def some_endpoint():\n return create_response()\n```\n\n### Asynchronous Feature Retrieval\n\nThe OpenFeature API supports asynchronous calls, enabling non-blocking feature evaluations for improved performance, especially useful in concurrent or latency-sensitive scenarios. If a provider *hasn't* implemented asynchronous calls, the client can still be used asynchronously, but calls will be blocking (synchronous).\n\n```python\nimport asyncio\nfrom openfeature import api\nfrom openfeature.provider.in_memory_provider import InMemoryFlag, InMemoryProvider\n\nmy_flags = { \"v2_enabled\": InMemoryFlag(\"on\", {\"on\": True, \"off\": False}) }\napi.set_provider(InMemoryProvider(my_flags))\nclient = api.get_client()\nflag_value = await client.get_boolean_value_async(\"v2_enabled\", False) # API calls are suffixed by _async\n\nprint(\"Value: \" + str(flag_value))\n```\n\nSee the [develop a provider](#develop-a-provider) for how to support asynchronous functionality in providers.\n\n### Shutdown\n\nThe OpenFeature API provides a shutdown function to perform a cleanup of all registered providers. This should only be called when your application is in the process of shutting down.\n\n```python\nfrom openfeature import api\n\napi.shutdown()\n```\n\n## Extending\n\n### Develop a provider\n\nTo develop a provider, you need to create a new project and include the OpenFeature SDK as a dependency.\nThis can be a new repository or included in [the existing contrib repository](https://github.com/open-feature/python-sdk-contrib) available under the OpenFeature organization.\nYou’ll then need to write the provider by implementing the `AbstractProvider` class exported by the OpenFeature SDK.\n\n```python\nfrom typing import List, Optional, Union\n\nfrom openfeature.evaluation_context import EvaluationContext\nfrom openfeature.flag_evaluation import FlagResolutionDetails\nfrom openfeature.hook import Hook\nfrom openfeature.provider import AbstractProvider, Metadata\n\nclass MyProvider(AbstractProvider):\n def get_metadata(self) -> Metadata:\n ...\n\n def get_provider_hooks(self) -> List[Hook]:\n return []\n\n def resolve_boolean_details(\n self,\n flag_key: str,\n default_value: bool,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[bool]:\n ...\n\n def resolve_string_details(\n self,\n flag_key: str,\n default_value: str,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[str]:\n ...\n\n def resolve_integer_details(\n self,\n flag_key: str,\n default_value: int,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[int]:\n ...\n\n def resolve_float_details(\n self,\n flag_key: str,\n default_value: float,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[float]:\n ...\n\n def resolve_object_details(\n self,\n flag_key: str,\n default_value: Union[dict, list],\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[Union[dict, list]]:\n ...\n```\n\nProviders can also be extended to support async functionality.\nTo support add asynchronous calls to a provider:\n\n- Implement the `AbstractProvider` as shown above.\n- Define asynchronous calls for each data type.\n\n```python\nclass MyProvider(AbstractProvider):\n ...\n async def resolve_boolean_details_async(\n self,\n flag_key: str,\n default_value: bool,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[bool]:\n ...\n\n async def resolve_string_details_async(\n self,\n flag_key: str,\n default_value: str,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[str]:\n ...\n\n async def resolve_integer_details_async(\n self,\n flag_key: str,\n default_value: int,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[int]:\n ...\n\n async def resolve_float_details_async(\n self,\n flag_key: str,\n default_value: float,\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[float]:\n ...\n\n async def resolve_object_details_async(\n self,\n flag_key: str,\n default_value: Union[dict, list],\n evaluation_context: Optional[EvaluationContext] = None,\n ) -> FlagResolutionDetails[Union[dict, list]]:\n ...\n\n```\n\n> Built a new provider? [Let us know](https://github.com/open-feature/openfeature.dev/issues/new?assignees=&labels=provider&projects=&template=document-provider.yaml&title=%5BProvider%5D%3A+) so we can add it to the docs!\n\n### Develop a hook\n\nTo develop a hook, you need to create a new project and include the OpenFeature SDK as a dependency.\nThis can be a new repository or included in [the existing contrib repository](https://github.com/open-feature/python-sdk-contrib) available under the OpenFeature organization.\nImplement your own hook by creating a hook that inherits from the `Hook` class.\nAny of the evaluation life-cycle stages (`before`/`after`/`error`/`finally_after`) can be override to add the desired business logic.\n\n```python\nfrom openfeature.hook import Hook\n\nclass MyHook(Hook):\n def after(self, hook_context: HookContext, details: FlagEvaluationDetails, hints: dict):\n print(\"This runs after the flag has been evaluated\")\n\n```\n\n> Built a new hook? [Let us know](https://github.com/open-feature/openfeature.dev/issues/new?assignees=&labels=hook&projects=&template=document-hook.yaml&title=%5BHook%5D%3A+) so we can add it to the docs!\n\n\n\n## ⭐️ Support the project\n\n- Give this repo a ⭐️!\n- Follow us on social media:\n - Twitter: [@openfeature](https://twitter.com/openfeature)\n - LinkedIn: [OpenFeature](https://www.linkedin.com/company/openfeature/)\n- Join us on [Slack](https://cloud-native.slack.com/archives/C0344AANLA1)\n- For more, check out our [community page](https://openfeature.dev/community/)\n\n## 🤝 Contributing\n\nInterested in contributing? Great, we'd love your help! 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