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Python Server SDK

View the Python Core SDK Migration Guide

Installation​

pip install statsig-python-core

Initialize the SDK​

After installation, you will need to initialize the SDK using a Server Secret Key from the Statsig console.

info

Do NOT embed your Server Secret Key in client-side applications, or expose it in any external-facing documents. However, if you accidentally expose it, you can create a new one in the Statsig console.

There is also an optional parameter named options that allows you to pass in a StatsigOptions to customize the SDK.
from statsig_python_core import Statsig, StatsigOptions # note, import statement has underscores while install has dashes

options = StatsigOptions()
options.environment = "development"

statsig = Statsig("secret-key", options)
statsig.initialize().wait()

# If you're running this in a script, be sure to wait for shutdown at the end to flush event logs to statsig
statsig.shutdown().wait()
initialize will perform a network request. After initialize completes, virtually all SDK operations will be synchronous (See Evaluating Feature Gates in the Statsig SDK). The SDK will fetch updates from Statsig in the background, independently of your API calls.

Working with the SDK​

Checking a Feature Flag/Gate​

Now that your SDK is initialized, let's fetch a Feature Gate. Feature Gates can be used to create logic branches in code that can be rolled out to different users from the Statsig Console. Gates are always CLOSED or OFF (think return false;) by default.

From this point on, all APIs will require you to specify the user (see Statsig user) associated with the request. For example, check a gate for a certain user like this:

user = StatsigUser("a-user")

if statsig.check_gate(user, "a_gate"):
    # Gate is on, enable new feature
else:
    # Gate is off

Reading a Dynamic Config​

Feature Gates can be very useful for simple on/off switches, with optional but advanced user targeting. However, if you want to be able send a different set of values (strings, numbers, and etc.) to your clients based on specific user attributes, e.g. country, Dynamic Configs can help you with that. The API is very similar to Feature Gates, but you get an entire json object you can configure on the server and you can fetch typed parameters from it. For example:

# Get a dynamic config for a specific user
config = statsig.get_dynamic_config(StatsigUser("my_user"), "a_config")

# Access config values with type-safe getters and fallback values
product_name = config.get_string("product_name", "Awesome Product v1")  # returns String
price = config.get_float("price", 10.0)  # returns float
should_discount = config.get_boolean("discount", False)  # returns bool
quantity = config.get_integer("quantity", 1)  # returns int64

# Advanced Usage:
# You can disable exposure logging for this specific check
options = DynamicConfigEvaluationOptions(disable_exposure_logging=True)
config = statsig.get_dynamic_config(user, "a_config", options)

# The config object also provides metadata about the evaluation
print(config.rule_id)  # The ID of the rule that served this config
print(config.id_type)  # The type of the evaluation (experiment, config, etc)

The get_dynamic_config() method returns a DynamicConfig object that allows you to:

  • Fetch typed values with fallback defaults using get_string(), get_float(), get_boolean(), and get_integer()
  • Access evaluation metadata through properties like rule_id and id_type
  • Configure evaluation behavior using DynamicConfigEvaluationOptions

By default, Statsig logs exposures automatically when configs are evaluated. You can disable this for specific checks using the evaluation options.

Getting a Layer/Experiment​

Then we have Layers/Experiments, which you can use to run A/B/n experiments. We offer two APIs, but we recommend the use of layers to enable quicker iterations with parameter reuse.

# Values via get_layer
layer = statsig.get_layer(StatsigUser("my_user"), "user_promo_experiments")
title = layer.get_string("title", "Welcome to Statsig!")
discount = layer.get_float("discount", 0.1)

# Via get_experiment
title_exp = statsig.get_experiment(StatsigUser("my_user"), "new_user_promo_title")
price_exp = statsig.get_experiment(StatsigUser("my_user"), "new_user_promo_price")

title = title_exp.get_string("title", "Welcome to Statsig!")
discount = price_exp.get_float("discount", 0.1)

Parameter Stores​

Sometimes you don't know whether you want a value to be a Feature Gate, Experiment, or Dynamic Config yet. If you want on-the-fly control of that outside of your deployment cycle, you can use Parameter Stores to define a parameter that can be changed into at any point in the Statsig console. Parameter Stores are optional, but parameterizing your application can prove very useful for future flexibility and can even allow non-technical Statsig users to turn parameters into experiments.

Getting a Parameter Store​

# Get a Parameter Store by name
param_store = statsig.get_parameter_store(user, "my_parameter_store")

Retrieving Parameter Values​

Parameter Store provides methods for retrieving values of different types with fallback defaults.

# String parameters
string_value = param_store.get_string("string_param", "default_value")

# Boolean parameters
bool_value = param_store.get_bool("bool_param", False)

# Numeric parameters
float_value = param_store.get_float("float_param", 0.0)
integer_value = param_store.get_integer("integer_param", 0)

# Complex parameters
default_array = ["item1", "item2"]
array_value = param_store.get_array("array_param", default_array)

default_map = {"key": "value"}
map_value = param_store.get_map("map_param", default_map)

Evaluation Options​

You can disable exposure logging when retrieving a parameter store:

from statsig_python_core import ParameterStoreEvaluationOptions

options = ParameterStoreEvaluationOptions(disable_exposure_logging=True)
param_store = statsig.get_parameter_store(user, "my_parameter_store", options)

Logging an Event​

Now that you have a Feature Gate or an Experiment set up, you may want to track some custom events and see how your new features or different experiment groups affect these events. This is super easy with Statsig—simply call the Log Event API and specify the user and event name to log; you additionally provide some value and/or an object of metadata to be logged together with the event:

statsig.log_event(
    user=StatsigUser("user_id"),  # Replace with your user object
    event_name="add_to_cart",
    value="SKU_12345",
    metadata={
        "price": "9.99",
        "item_name": "diet_coke_48_pack"
    }
)

Learn more about identifying users, group analytics, and best practices for logging events in the logging events guide.

Retrieving Feature Gate Metadata​

In certain scenarios, you may need more information about a gate evaluation than just a boolean value. For additional metadata about the evaluation, use the Get Feature Gate API, which returns a FeatureGate object:

gate = statsig.get_feature_gate(user, "example_gate");
print(gate.rule_id)
print(gate.value)

Using Shared Instance​

In some applications, you may want to create a single Statsig instance that can be accessed globally throughout your codebase. The shared instance functionality provides a singleton pattern for this purpose:

# Create a shared instance that can be accessed globally
statsig = Statsig.new_shared("secret-key", options)
statsig.initialize().wait()

# Access the shared instance from anywhere in your code
shared_statsig = Statsig.shared()
is_feature_enabled = shared_statsig.check_gate(StatsigUser("user_id"), "feature_name")

# Check if a shared instance exists
if Statsig.has_shared_instance():
    # Use the shared instance
    pass

# Remove the shared instance when no longer needed
Statsig.remove_shared()

The shared instance functionality provides a singleton pattern where a single Statsig instance can be created and accessed globally throughout your application. This is useful for applications that need to access Statsig functionality from multiple parts of the codebase without having to pass around a Statsig instance.

  • Statsig.new_shared(sdk_key, options): Creates a new shared instance of Statsig that can be accessed globally
  • Statsig.shared(): Returns the shared instance
  • Statsig.has_shared_instance(): Checks if a shared instance exists (useful when you aren't sure if the shared instance is ready yet)
  • Statsig.remove_shared(): Removes the shared instance (useful when you want to switch to a new shared instance)

Note that has_shared_instance() and remove_shared() are helpful in specific scenarios but aren't required in most use cases where the shared instance is set up near the top of your application.

Also note that only one shared instance can exist at a time. Attempting to create a second shared instance will result in an error.

Manual Exposures​

warning

Manually logging exposures can be tricky and may lead to an imbalance in exposure events. For example, only triggering exposures for users in the Test group of an experiment will imbalance the experiment, making it useless.

Manual exposures give you the option to query your gates/experiments without triggering an exposure, and optionally, manually log the exposure after the fact.

To check a gate without an exposure being logged, call the following.

result = statsig.check_gate(aUser, 'a_gate_name', FeatureGateEvaluationOptions(disable_exposure_logging=True))

Later, if you would like to expose this gate, you can call the following.

statsig.manually_log_gate_exposure(aUser, 'a_gate_name')

Statsig User​

When calling APIs that require a user, you should pass as much information as possible in order to take advantage of advanced gate and config conditions (like country or OS/browser level checks), and correctly measure impact of your experiments on your metrics/events. At least one ID (userID or customID) is required because it's needed to provide a consistent experience for a given user (click here)

Besides userID, we also have email, ip, userAgent, country, locale and appVersion as top-level fields on StatsigUser. In addition, you can pass any key-value pairs in an object/dictionary to the custom field and be able to create targeting based on them.

note

Previous Statsig SDKs enabled country and user agent parsing by default, but our new class of SDKs require you to opt-in by setting StatsigOptions.enable_country_lookup and StatsigOptions.enable_user_agent_parsing. Providing parsed fields yourself is often advantageous for consistency and speed.

Note that while typing is lenient on the StatsigUser object to allow you to pass in numbers, strings, arrays, objects, and potentially even enums or classes, the evaluation operators will only be able to operate on primitive types - mostly strings and numbers. While we attempt to smartly cast custom field types to match the operator, we cannot guarantee evaluation results for other types. For example, setting an array as a custom field will only ever be compared as a string - there is no operator to match a value in that array.

user = StatsigUser(
    user_id="123",
    email="testuser@statsig.com",
    ip="192.168.1.101",
    user_agent="Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.78 Safari/537.36",
    country="US",
    locale="en_US",
    app_version="4.3.0",
    custom={"cohort": "June 2021"},
    private_attributes={"gender": "female"},
)

Private Attributes​

Have sensitive user PII data that should not be logged? No problem, we have a solution for it! On the StatsigUser object we also have a field called privateAttributes, which is a simple object/dictionary that you can use to set private user attributes. Any attribute set in privateAttributes will only be used for evaluation/targeting, and removed from any logs before they are sent to Statsig server.

For example, if you have feature gates that should only pass for users with emails ending in "@statsig.com", but do not want to log your users' email addresses to Statsig, you can simply add the key-value pair { email: "my_user@statsig.com" } to privateAttributes on the user and that's it!

Statsig Options​

StatsigOptions Class​

The statsig.initialize() method takes an optional parameter options to customize the Statsig client. Below is the structure of the StatsigOptions class, including available parameters and their descriptions:

Parameters​

  • specs_url: Optional[str]
    Custom URL for fetching feature specifications.

  • specs_sync_interval_ms: Optional[int]
    How often the SDK updates specifications from Statsig servers (in milliseconds).

  • init_timeout_ms: Optional[int]
    Sets the maximum timeout for initialization requests (in milliseconds).

  • log_event_url: Optional[str]
    Custom URL for logging events.

  • disable_all_logging: Optional[bool]
    When true, disables all event logging.

  • disable_network: Optional[bool] When true, disables all network functions: event & exposure logging, spec downloads, and ID List downloads. Formerly called "localMode".

  • event_logging_flush_interval_ms: Optional[int]
    How often events are flushed to Statsig servers (in milliseconds).

  • event_logging_max_queue_size: Optional[int]
    Maximum number of events to queue before forcing a flush.

  • enable_id_lists: Optional[bool]
    Enable/disable BIG ID list functionality (IDList > 1000)

  • disable_user_agent_parsing Optional[bool] Default false. If set to true, the SDK will NOT attempt to parse UserAgents (attached to the user object) into browserName, browserVersion, systemName, systemVersion, and appVersion at evaluation time, when needed for evaluation.

  • wait_for_user_agent_init Optional[bool] Default: false

    When set to true, the SDK will wait until user agent parsing data is fully loaded during initialization. This may slow down by ~1 second startup but ensures that parsing of the user’s userAgent string into fields like browserName, browserVersion, systemName, systemVersion, and appVersion is ready before any evaluations.

  • disable_country_lookup Optional[bool] Default false. If set to true, the SDK will NOT attempt to parse IP addresses (attached to the user object at user.ip) into Country codes at evaluation time, when needed for evaluation.

  • wait_for_country_lookup_init Optional[bool] Default: false

    When set to true, the SDK will wait for country lookup data (e.g., GeoIP or YAML files) to fully load during initialization. This may slow down by ~1 second startup but ensures that IP-to-country parsing is ready at evaluation time

  • id_lists_url: Optional[str]
    Custom URL for fetching ID lists.

  • id_lists_sync_interval_ms: Optional[int]
    How often the SDK updates ID lists from Statsig servers (in milliseconds).

  • fallback_to_statsig_api: Optional[bool]
    Whether to fallback to the Statsig API if custom endpoints fail.

  • environment: Optional[str]
    Environment parameter for evaluation.

  • output_log_level: Optional[str]
    Controls the verbosity of SDK logs.

  • persistent_storage: Optional[PersistentStorageBaseClass]
    Adapter / Interface to use persistent assignment within SDK. More details

  • observability_client: Optional[ObservabilityClientBaseClass]
    Adapter to listen monitor the health of SDK. See details

  • data_store: Optional[DataStore]
    Custom data store implementation for storing and retrieving configuration data. Used for advanced caching or storage strategies.

  • event_logging_max_pending_batch_queue_size: Optional[int]
    Maximum number of batches of events to hold in buffer to retry.

  • global_custom_fields: Optional[Dict]
    Custom fields to include in all events logged by the SDK.

  • config_compression_mode: Optional[str]
    Compression method for exposure logging. Default: "gzip". Options: "gzip", "dictionary"

  • proxy_config: Optional[ProxyConfig]
    Configuration for connecting through a proxy server. The ProxyConfig object has the following properties:

    • proxy_host: Optional string specifying the proxy server host
    • proxy_port: Optional number specifying the proxy server port
    • proxy_auth: Optional string for proxy authentication (format: "username:password")
    • proxy_protocol: Optional string specifying the protocol (e.g., "http", "https")

Example Usage​

from statsig_python_core import StatsigOptions

# Define proxy configuration if needed
proxy_config = {
    "proxy_host": "proxy.example.com",
    "proxy_port": 8080,
    # "proxy_auth": "username:password",  # Uncomment if authentication is needed
    "proxy_protocol": "http"
}

# Initialize StatsigOptions with custom parameters
options = StatsigOptions()
options.environment = "development"
options.init_timeout_ms = 3000
options.disable_all_logging = False
options.proxy_config = proxy_config

# Pass the options object into statsig.initialize()
statsig = Statsig("secret-key", options)
statsig.initialize().wait()

Shutting Statsig Down​

Because we batch and periodically flush events, some events may not have been sent when your app/server shuts down.

To make sure all logged events are properly flushed, you should tell Statsig to shutdown when your app/server is closing:

statsig.shutdown().wait()

Local Overrides ​

To override the return value of a gate/config/experiment/layer locally, we expose a set of override APIs. Coupling this with StatsigOptions.disable_network can be helpful when writing unit tests.

// Overrides the given gate to the specified value
statsig.override_gate("a_gate_name", true)
	
// Overrides the given dynamic config to the provided value
statsig.override_dynamic_config("a_config_name", { "key": "value" })

// Overrides the given experiment to the provided value
statsig.override_experiment("an_experiment_name", { "key": "value" })
	
// Overrides the given layer to the provided value
statsig.override_layer("a_layer_name", { "key": "value" })

// Overrides the given experiment to a particular groupname, available for experiments only:
statsig.override_experiment_by_group_name("an_experiment_name", "a_group_name")
note
  1. These only apply locally - they do not update definitions in the Statsig console or elsewhere.
  2. The local override API is not designed to be a full mock. They are only a convenient way to override the value of the gate/config/etc.

Server Core​

Statsig Server Core is a performance-focused rewrite of our server SDKs with a shared, core Rust library. With extensive optimization and Rust's inherent speed, Core SDKs can evaluate 5-10x as fast as our native SDKs.

Server Core also introduces new features, like Parameter Stores, the SDK Observability Interface, and streaming flag/experiment changes (from the Statsig Forward Proxy)

Server Core is currently available for Java, Node, Elixir, Rust and Python. Need another language, or run into any issues? Let us know in the Statsig Slack and we'll prioritize it.

Persistent Storage​

The Persistent Storage interface allows you to implement custom storage for experiment assignments. This ensures consistent user experiences across sessions by persisting experiment assignments. For more information on persistent assignments, see the Persistent Assignment documentation.

class PersistentStorage(PersistentStorageBaseClass):
    def load(self, key: str) -> Optional[UserPersistedValues]:
        """
        Load persisted values for a user from storage
        
        Args:
            key: A string key that uniquely identifies a user

        Returns:
            Dictionary mapping config names to their persisted values
        """
        pass

    def save(self, key: str, config_name: str, data: StickyValues):
        """
        Save a persistent value for a user
        
        Args:
            key: A string key that uniquely identifies a user
            config_name: The name of the config/experiment
            data: The values to persist
        """
        pass

    def delete(self, key: str, config_name: str):
        """
        Delete a persistent value for a user
        
        Args:
            key: A string key that uniquely identifies a user
            config_name: The name of the config/experiment to delete
        """
        pass

Data Store​

The Data Store interface allows you to implement custom storage for Statsig configurations. This enables advanced caching strategies and integration with your preferred storage systems.

class DataStore(DataStoreBase):
    def initialize(self):
        """
        Initialize the data store. Called when the Statsig client initializes.
        """
        pass

    def shutdown(self):
        """
        Clean up resources when the Statsig client shuts down.
        """
        pass

    def get(self, key: str) -> Optional[DataStoreResponse]:
        """
        Retrieve value from the data store.
        
        Args:
            key: The key to retrieve the value for
            
        Returns:
            DataStoreResponse containing the result and time
        """
        pass

    def set(self, key: str, value: str, time: Optional[int] = None):
        """
        Store a value in the data store.
        
        Args:
            key: The key to store the value under
            value: The value to store
            time: Optional timestamp
        """
        pass

    def support_polling_updates_for(self, key: str) -> bool:
        """
        Whether the data store supports polling for updates for the given key.
        
        Args:
            key: The key to check
            
        Returns:
            True if polling is supported, False otherwise
        """
        return False

Custom Output Logger​

The Output Logger interface allows you to customize how the SDK logs messages. This enables integration with your own logging system and control over log verbosity.

class OutputLoggerProvider(OutputLoggerProviderBase):
    def init(self):
        """
        Initialize the logger. Called when the Statsig client initializes.
        """
        pass

    def debug(self, tag: str, msg: str):
        """
        Log a debug message.
        
        Args:
            tag: Category/component tag for the message
            msg: The message to log
        """
        pass

    def info(self, tag: str, msg: str):
        """
        Log an info message.
        
        Args:
            tag: Category/component tag for the message
            msg: The message to log
        """
        pass

    def warn(self, tag: str, msg: str):
        """
        Log a warning message.
        
        Args:
            tag: Category/component tag for the message
            msg: The message to log
        """
        pass

    def error(self, tag: str, msg: str):
        """
        Log an error message.
        
        Args:
            tag: Category/component tag for the message
            msg: The message to log
        """
        pass

    def shutdown(self):
        """
        Clean up resources when the Statsig client shuts down.
        """
        pass

Observability Client​

The Observability Client interface allows you to monitor the health of the SDK by integrating with your own observability systems. This enables tracking metrics, errors, and performance data. For more information on the metrics emitted by Statsig SDKs, see the Monitoring documentation.

class ObservabilityClient(ObservabilityClientBase):
    def init(self):
        """
        Initialize the observability client. Called when the Statsig client initializes.
        """
        pass

    def increment(self, metric_name: str, value: float, tags: Optional[Dict[str, str]] = None):
        """
        Report a counter metric.
        
        Args:
            metric_name: The name of the metric
            value: The amount to increment by
            tags: Optional tags to associate with the metric
        """
        pass

    def gauge(self, metric_name: str, value: float, tags: Optional[Dict[str, str]] = None):
        """
        Report a gauge metric.
        
        Args:
            metric_name: The name of the metric
            value: The current value
            tags: Optional tags to associate with the metric
        """
        pass

    def dist(self, metric_name: str, value: float, tags: Optional[Dict[str, str]] = None):
        """
        Report a distribution metric.
        
        Args:
            metric_name: The name of the metric
            value: The value to record
            tags: Optional tags to associate with the metric
        """
        pass

    def error(self, tag: str, error: str):
        """
        Report an error.
        
        Args:
            tag: Category/component tag for the error
            error: The error message
        """
        pass

    def should_enable_high_cardinality_for_this_tag(self, tag: str) -> bool:
        """
        Determine if high cardinality should be enabled for a tag.
        
        Args:
            tag: The tag to check
            
        Returns:
            True if high cardinality should be enabled, False otherwise
        """
        pass

FAQ​

How do I run experiments for logged out users?​

See the guide on device level experiments