# Python Server SDK

{% callout type="tip" %}
Migrating from the legacy Python SDK? Refer to our

[Migration Guide](https://docs.statsig.com/server-core/migration-guides/python)

.
{% /callout %}

## Set up the SDK

{% steps %}
{% step title="Install the SDK" %}

## Installation

```shell
pip install statsig-python-core
```

{% /step %}

{% step title="Initialize the SDK" %}
After installation, initialize the SDK using a [Server Secret Key from the Statsig console](https://console.statsig.com/api_keys).

{% callout type="warning" %}
Keep Server Secret Keys private. If you expose one, you can disable and recreate it in the Statsig console.
{% /callout %}

An optional `options` parameter accepts a StatsigOptions object to customize the SDK.

```python
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` performs a network request. After `initialize` completes, virtually all SDK operations are synchronous (refer to [Evaluating Feature Gates in the Statsig SDK](https://blog.statsig.com/evaluating-feature-gates-in-the-statsig-sdk-a6f8881a1ad8)). The SDK fetches updates from Statsig in the background independently of API calls.

{% callout type="warning" heading="Process forking and WSGI servers" %}
Never fork processes after calling `statsig.initialize()`. Doing so puts Statsig in an undefined state and can cause a deadlock.

The Python Core SDK uses internal threading and async runtime components that don't work correctly when copied across process boundaries. When a process forks after initialization, these components can become corrupted, leading to:

- Deadlocks in event logging.
- Hanging initialization calls.
- Unpredictable SDK behavior.
- Silent failures in feature evaluation.
{% /callout %}

### Initializing with WSGI servers

For production deployments using WSGI servers like uWSGI or Gunicorn, initialize Statsig **after** the worker processes are forked, not in the main process.

{% tabs %}
{% tab title="uWSGI" %}

#### ✅ Correct: uWSGI example

```python expandable
# app.py
from statsig_python_core import Statsig, StatsigOptions
from flask import Flask

app = Flask(__name__)
statsig = None

def init_statsig():
    global statsig
    if statsig is None:
        options = StatsigOptions()
        options.environment = "production"
        statsig = Statsig("your-server-secret-key", options)
        statsig.initialize().wait()

# Initialize in each worker process
@app.before_first_request
def before_first_request():
    init_statsig()

@app.route('/')
def index():
    # Use statsig here
    return "Hello World"
```

```ini
# uwsgi.ini
[uwsgi]
module = app:app
master = true
processes = 4
# Statsig will be initialized in each worker process
```

{% /tab %}

{% tab title="Gunicorn" %}

#### ✅ Correct: Gunicorn example

```python
# gunicorn_config.py
def post_fork(server, worker):
    # Initialize Statsig after worker process is forked
    from app import init_statsig
    init_statsig() # as defined above

# ...app.py
```

```bash
# Start Gunicorn with post-fork hook
gunicorn --config gunicorn_config.py app:app
```

{% /tab %}

{% tab title="FastAPI" %}

#### ✅ Correct: FastAPI example

```python expandable
from fastapi import FastAPI
from statsig_python_core import Statsig, StatsigOptions

app = FastAPI()
statsig = None

@app.on_event("startup")
async def startup_event():
    global statsig
    options = StatsigOptions()
    options.environment = "production"
    statsig = Statsig("your-server-secret-key", options)
    statsig.initialize().wait()

@app.on_event("shutdown")
async def shutdown_event():
    if statsig:
        statsig.shutdown().wait()

@app.get("/")
async def root():
    # Use statsig here
    return {"message": "Hello World"}
```

{% /tab %}
{% /tabs %}

`initialize` performs a network request. After `initialize` completes, virtually all SDK operations are synchronous (refer to [Evaluating Feature Gates in the Statsig SDK](https://blog.statsig.com/evaluating-feature-gates-in-the-statsig-sdk-a6f8881a1ad8)). The SDK fetches updates from Statsig in the background independently of API calls.
{% /step %}
{% /steps %}

## Working with the SDK

### Checking a feature flag/gate

After you initialize the SDK, you can fetch a [**Feature Gate**](https://docs.statsig.com/feature-flags/overview). Feature Gates create logic branches in code that you can roll out to different users from the Statsig Console. Gates are always **CLOSED** or **OFF** (equivalent to `return false;`) by default.

All APIs require you to specify the user (refer to [Statsig user](#statsig-user)) associated with the request. For example:

```python
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 are useful for simple on/off switches with optional user targeting. To send a different set of values (strings, numbers, and similar types) to clients based on user attributes such as country, use [**Dynamic Configs**](https://docs.statsig.com/dynamic-config/overview). The API is similar to Feature Gates, but returns a full JSON object configurable on the server from which you can fetch typed parameters.

```python
# 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_bool("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 automatically logs exposures when it evaluates configs. You can disable exposure logging for specific checks using the evaluation options.

### Getting a layer/experiment

Use **Layers/Experiments** to run A/B/n experiments. Two APIs are available. Statsig recommends [Layers](https://docs.statsig.com/experiments/layers-overview) because they make parameters reusable and support mutually exclusive experiments.

```Python
# 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)
```

### Retrieving feature gate metadata

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

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

### Parameter stores

Use Parameter Stores when you want to define a parameter without deciding whether it should be a Feature Gate, Experiment, or Dynamic Config. Parameter Stores let you change the parameter type at any point in the Statsig console without a new deployment. Parameter Stores are optional, but parameterizing your application provides future flexibility and allows non-technical Statsig users to turn parameters into experiments.

```python
# 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.

```python
# 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:

```python
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

To track custom events, call the Log Event API. Specify the user, event name, and an optional value or metadata object:

```Python
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"
    }
)
```

### Sending events to Log Explorer

You can forward logs to Logs Explorer for convenient analysis using the Forward Log Line Event API. This lets you include custom metadata and event values with each log.

```python
user = StatsigUser(
    user_id="a-user",
    custom={
        "service": "my-service",
        "pod": "my-pod",
        "namespace": "my-namespace",
        "container": "my-container",
        # ...include any service-specific metadata
    }
)

# levels: trace, debug, info, log, warn, error
statsig.forward_log_line_event(user, "warn", "script failed to load", {
    "custom_metadata": "script_name:my-script"
    # ... include any event-specific metadata
})
```

## Using shared instance

To create a single Statsig instance accessible globally throughout your codebase, use the shared instance functionality, which provides a singleton pattern:

```python
# 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 helps when multiple parts of your codebase need Statsig without passing an instance between them.

- `Statsig.new_shared(sdk_key, options)`: Creates a new shared instance of Statsig that you can access globally
- `Statsig.shared()`: Returns the shared instance
- `Statsig.has_shared_instance()`: Checks if a shared instance exists (useful when the shared instance may not be ready yet)
- `Statsig.remove_shared()`: Removes the shared instance (useful when you want to switch to a new shared instance)

{% callout type="note" %}
`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 results in an error.
{% /callout %}

## Manual exposures

By default, the SDK automatically logs an exposure event when you check a gate, get a config, get an experiment, or call get() on a parameter in a layer. To delay exposure logging (for example, to log only after the user actually uses the feature), use manual exposures.

All main SDK functions (`check_gate`, `get_dynamic_config`, `get_experiment`, `get_layer`) accept an optional `disable_exposure_logging` parameter. When set to `True`, the SDK doesn't automatically log an exposure. You can then log the exposure manually at a later time:

{% tabs %}
{% tab title="Feature Gates" %}

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

```python
statsig.manually_log_gate_exposure(aUser, 'a_gate_name')
```

{% /tab %}

{% tab title="Dynamic Configs" %}

```python
config = statsig.get_dynamic_config(aUser, 'a_dynamic_config_name', DynamicConfigEvaluationOptions(disable_exposure_logging=True))
```

```python
statsig.manually_log_dynamic_config_exposure(aUser, 'a_dynamic_config_name')
```

{% /tab %}

{% tab title="Experiments" %}

```python
experiment = statsig.get_experiment(aUser, 'an_experiment_name', ExperimentEvaluationOptions(disable_exposure_logging=True))
```

```python
statsig.manually_log_experiment_exposure(aUser, 'an_experiment_name')
```

{% /tab %}

{% tab title="Layers" %}

```python
layer = statsig.get_layer(aUser, 'a_layer_name',  LayerEvaluationOptions(disable_exposure_logging=True))
paramValue = layer.get('a_param_name', 'fallback_value')
```

```python
statsig.manually_log_layer_parameter_exposure(aUser, 'a_layer_name', 'a_param_name')
```

{% /tab %}
{% /tabs %}

## Statsig user

The `StatsigUser` object represents a user in Statsig. You must provide a `userID` or at least one of the `customIDs` to identify the user.

When calling APIs that require a user, pass as much information as possible. Complete user information lets you take advantage of advanced gate and config conditions (like country or OS/browser-level checks) and correctly measure the impact of experiments on metrics and events. As explained in [why an ID is always required for server SDKs](https://docs.statsig.com/sdks/user#why-is-an-id-always-required-for-server-sdks), you must provide at least one identifier (userID or customID) to ensure a consistent experience for a given user.

In addition to userID, the top-level fields on StatsigUser are: email, ip, userAgent, country, locale, and appVersion. You can also pass any key-value pairs in an object or dictionary to the custom field and create targeting based on them.

### Private attributes

Private attributes are user attributes that Statsig uses for evaluation but doesn't forward to any integrations. They are useful for PII or sensitive data that you don't want to send to third-party services.

```python expandable
from statsig_python_core import StatsigUser

user = StatsigUser(
    user_id="a-user-id",
    email="user@example.com",
    ip="192.168.1.1",
    user_agent="Mozilla/5.0...",
    country="US",
    locale="en_US",
    app_version="1.0.0",
    custom={
        # Custom fields
        "plan": "premium",
        "age": 25
    },
    custom_ids={
        # Custom ID types
        "stable_id": "stable-id-123"
    },
    private_attributes={
        # Private attributes not forwarded to integrations
        "email": "private@example.com"
    }
)
```

## Statsig options

You can pass an optional `options` parameter in addition to `sdkKey` during initialization to customize the Statsig client.

{% accordion title="StatsigOptions" %}
{% parameter name="specs_url" type="Optional[str]" %}
Custom URL for fetching feature specifications. Provide the full endpoint URL, which the SDK uses as-is.
{% /parameter %}

{% parameter name="specs_sync_interval_ms" type="Optional[int]" %}
How often the SDK updates specifications from Statsig servers (in milliseconds).
{% /parameter %}

{% parameter name="init_timeout_ms" type="Optional[int]" %}
Sets the maximum timeout for initialization requests (in milliseconds).
{% /parameter %}

{% parameter name="log_event_url" type="Optional[str]" %}
Custom URL for logging events.
{% /parameter %}

{% parameter name="disable_all_logging" type="Optional[bool]" %}
When `true`, disables all event logging.
{% /parameter %}

{% parameter name="disable_network" type="Optional[bool]" %}
When `true`, disables all network functions: event & exposure logging, spec downloads, and ID List downloads. Formerly called "localMode".
{% /parameter %}

{% parameter name="event_logging_flush_interval_ms" type="Optional[int]" %}
How often the SDK flushes events to Statsig servers (in milliseconds).
{% /parameter %}

{% parameter name="event_logging_max_queue_size" type="Option<u32>" %}
Maximum number of events to queue before forcing a flush.

- Default is `2000`
- event_logging_max_queue_size \* event_logging_max_pending_batch_queue_size is the upper limit on how many events are queued
- Refer to `event_logging_max_pending_batch_queue_size`
  {% /parameter %}

{% parameter name="event_logging_max_pending_batch_queue_size" type="Option<u32>" %}
Maximum number of event batches to hold in buffer to retry.

- Default is `100`.
- event_logging_max_queue_size \* event_logging_max_pending_batch_queue_size is the upper limit on how many events are queued
- eg: 2000 \* 100 means the SDK can process 200k event per second before events start getting dropped
- Refer to `event_logging_max_queue_size`.
  {% /parameter %}

{% parameter name="enable_id_lists" type="Optional[bool]" %}
Enable/disable ID list functionality. **Required to be `true` when using segments with more than 1000 IDs.** Refer to [ID List segments](https://docs.statsig.com/segments/add-id-list) for more details.
{% /parameter %}

{% parameter name="disable_user_agent_parsing" type="Optional[bool]" %}
If set to true, the SDK doesn't attempt to parse UserAgents (attached to the user object) into browserName, browserVersion, systemName, systemVersion, and appVersion at evaluation time, when needed for evaluation.
{% /parameter %}

{% parameter name="wait_for_user_agent_init" type="Optional[bool]" %}
When set to true, the SDK waits until user agent parsing data is fully loaded during initialization. This may slow down startup by ~1 second but ensures that parsing of the user's userAgent string into fields like browserName, browserVersion, systemName, systemVersion, and appVersion is ready before any evaluations.
{% /parameter %}

{% parameter name="disable_country_lookup" type="Optional[bool]" %}
If set to true, the SDK doesn't attempt to parse IP addresses (attached to the user object at user.ip) into Country codes at evaluation time, when needed for evaluation.
{% /parameter %}

{% parameter name="wait_for_country_lookup_init" type="Optional[bool]" %}
When set to true, the SDK waits for country lookup data (e.g., GeoIP or YAML files) to fully load during initialization. This may slow down startup by ~1 second but ensures that IP-to-country parsing is ready at evaluation time.
{% /parameter %}

{% parameter name="id_lists_url" type="Optional[str]" %}
Custom URL for fetching the ID list manifest (the `get_id_lists` endpoint). Provide the full endpoint URL, which the SDK uses as-is.
{% /parameter %}

{% parameter name="download_id_list_file_api" type="Optional[str]" %}
Origin (scheme and host only, no path) for downloading individual ID list files. Unlike `specs_url` and `id_lists_url`, which take a full endpoint URL, this takes an origin only: the SDK appends each per-file path from the ID list manifest. Setting a full path (for example, one ending in `/v1/download_id_list_file`) produces a doubled path and a 401. Overriding only `specs_url` and `id_lists_url` leaves individual ID list file downloads going to the Statsig CDN; set this option to route them through your proxy.
{% /parameter %}

{% parameter name="id_lists_sync_interval_ms" type="Optional[int]" %}
How often the SDK updates ID lists from Statsig servers (in milliseconds).
{% /parameter %}

{% parameter name="fallback_to_statsig_api" type="Optional[bool]" %}
Whether to fall back to the Statsig API if custom endpoints fail.
{% /parameter %}

{% parameter name="environment" type="Optional[str]" %}
Environment parameter for evaluation.
{% /parameter %}

{% parameter name="output_log_level" type="Optional[str]" %}
Controls the verbosity of SDK logs.
{% /parameter %}

{% parameter name="persistent_storage" type="Optional[PersistentStorage]" %}
Adapter / Interface to use persistent assignment within SDK. Refer to [Persistent Assignment](https://docs.statsig.com/server/concepts/persistent_assignment/) for more details.
{% /parameter %}

{% parameter name="observability_client" type="Optional[ObservabilityClient]" %}
Adapter to monitor the health of the SDK. Refer to [SDK Monitoring](https://docs.statsig.com/infrastructure/sdk-monitoring) for more details.
{% /parameter %}

{% parameter name="data_store" type="Optional[DataStore]" %}
Custom data store implementation for storing and retrieving configuration data. Used for advanced caching or storage strategies.
{% /parameter %}

{% parameter name="event_logging_max_pending_batch_queue_size" type="Optional[int]" %}
Maximum number of batches of events to hold in buffer to retry.
{% /parameter %}

{% parameter name="global_custom_fields" type="Optional[Dict]" %}
Custom fields to include in all events logged by the SDK.
{% /parameter %}

{% parameter name="config_compression_mode" type="Optional[str]" %}
Compression method for exposure logging. Options: "gzip", "dictionary"
{% /parameter %}

{% parameter name="proxy_config" type="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")
- `ca_cert_path`: Optional path to a PEM CA bundle for outbound TLS
  {% /parameter %}

{% parameter name="spec_adapter_configs" type="Optional[List[SpecAdapterConfig]]" %}
Advanced configuration for customizing how the SDK fetches feature specifications. Allows you to configure multiple spec adapters with different priorities and settings.

Each `SpecAdapterConfig` object has the following properties:

- `adapter_type`: String specifying the adapter type (e.g., "http", "grpc")
- `specs_url`: Optional custom URL for fetching specifications
- `init_timeout_ms`: Optional timeout for initialization (in milliseconds)

{% callout type="note" %}
**Migration note:** Statsig previously named this parameter `init_resources` in earlier versions. If you're upgrading from an older version, replace `init_resources` with `spec_adapter_configs`.
{% /callout %}
{% /parameter %}
{% /accordion %}

### Proxy and custom network routing

Use `proxy_config` if your service needs a standard outbound HTTP proxy. Use `spec_adapter_configs` if you need to route spec downloads through [Statsig Forward Proxy](https://docs.statsig.com/infrastructure/api_proxy/introduction) or another custom spec source.

```python
from statsig_python_core import ProxyConfig, Statsig, StatsigOptions

proxy_config = ProxyConfig(
    proxy_host="proxy.example.com",
    proxy_port=8080,
    proxy_protocol="https",
    ca_cert_path="/etc/ssl/certs/corporate-ca.pem",  # Optional
)

options = StatsigOptions()
options.proxy_config = proxy_config

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

Set `ca_cert_path` when your environment requires a custom PEM CA bundle for outbound TLS.

### Using spec_adapter_configs with multiple sources

```python
from statsig_python_core import StatsigOptions, SpecAdapterConfig

# Configure multiple spec adapters with priority order
# First source: Statsig CDN
primary_adapter = SpecAdapterConfig(
    adapter_type="http",
    specs_url="https://api.statsigcdn.com/v2/download_config_specs",
    init_timeout_ms=3000
)

# Second source: Data Store adapter
# The SDK will try this source if the primary source fails
data_store_adapter = SpecAdapterConfig(
    adapter_type="data_store",
    init_timeout_ms=5000
)

options = StatsigOptions()
options.spec_adapter_configs = [primary_adapter, data_store_adapter]
options.environment = "production"

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

## Shutting Statsig down

Statsig batches and periodically flushes events. To flush all logged events before shutdown, call `shutdown()` before your app or server shuts down:

```python
statsig.shutdown().wait()
```

## Local overrides

Local Overrides let you override the values of gates, configs, experiments, and layers for testing. This is useful for local development or testing when you want to force a specific value without changing the configuration in the Statsig console.

```python
# 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")
```

## Client SDK bootstrapping | SSR

If you use the Statsig client SDK in a browser or mobile app, you can bootstrap the client SDK with values from the server SDK to avoid a network request on the client. This is useful for server-side rendering (SSR) or to reduce network requests on the client.

## Client initialize response

The Python Core SDK provides a method to generate a client initialize response that you can use to bootstrap client SDKs without requiring network requests.

```python
import json
from statsig_python_core import Statsig, StatsigUser

# Get client initialize response for a user
response_data = statsig.get_client_initialize_response(user)
response = json.loads(response_data)

# Pass response to a client SDK to initialize without a network request
```

{% accordion-group %}
{% accordion title="Initialize Response Parameters" %}
The `get_client_initialize_response` method accepts the following parameters:

```python
def get_client_initialize_response(
    user: StatsigUser,
    hash: Optional[str] = None,
    client_sdk_key: Optional[str] = None,
    include_local_overrides: Optional[bool] = None
) -> str:
```

- **`user`**: `StatsigUser` - The user to generate the initialize response for
- **`hash`**: `Optional[str]` - Algorithm used for hashing gate/experiment names (default: 'djb2')
- **`client_sdk_key`**: `Optional[str]` - Client SDK key to use for initialization
- **`include_local_overrides`**: `Optional[bool]` - Whether to include local overrides in the response
  {% /accordion %}

{% accordion title="Hash Algorithm" %}
The `hash` parameter specifies which algorithm to use for hashing gate and experiment names in the client initialize response. The default is `'djb2'` for better performance and smaller payload size.

Available options:

- `'djb2'` (default) - DJB2 hashing algorithm for better performance
- `'sha256'` - SHA-256 hashing algorithm
- `'none'` - No hashing applied

```python
# Use djb2 hashing algorithm (default)
response_data = statsig.get_client_initialize_response(user, hash='djb2')

# Use SHA-256 hashing algorithm
response_data = statsig.get_client_initialize_response(user, hash='sha256')

# Disable hashing
response_data = statsig.get_client_initialize_response(user, hash='none')
```

{% /accordion %}

{% accordion title="Client SDK Key" %}
The `client_sdk_key` parameter lets you filter the response to only the specific feature gates, experiments, dynamic configs, layers, or parameter stores that a particular client key has access to, effectively letting you apply [target apps](https://docs.statsig.com/sdks/target-apps).

```python
# Specify a client SDK key
response_data = statsig.get_client_initialize_response(
    user,
    client_sdk_key='client-key'
)
```

{% /accordion %}

{% accordion title="Local Overrides" %}
The `include_local_overrides` parameter determines whether to consider [local overrides](#local-overrides) you've set when evaluating each config in the response.

```python
# Include local overrides in the response
response_data = statsig.get_client_initialize_response(
    user,
    include_local_overrides=True
)
```

{% /accordion %}

{% accordion title="Full Code Example" %}
Below is a complete example of using the client initialize response to bootstrap a client SDK. You can parallelize or inline the initialize response data with other requests to your server to eliminate additional round trips and latency.

```python
# Server-side code
import json
from statsig_python_core import Statsig, StatsigUser, StatsigOptions
from flask import Flask, request, jsonify

app = Flask(__name__)

# Initialize the server SDK
options = StatsigOptions()
statsig = Statsig('server-secret-key', options)
statsig.initialize().wait()

# In your API endpoint handler
@app.route('/statsig-bootstrap')
def statsig_bootstrap():
    # Create a user object from the request
    user = StatsigUser(
        user_id=request.args.get('userID', ''),
        email=request.args.get('email'),
        ip=request.remote_addr,
        user_agent=request.headers.get('User-Agent')
    )

    # Generate the client initialize response
    response_data = statsig.get_client_initialize_response(
        user,
        hash='djb2',
        client_sdk_key='client-sdk-key'
    )

    # Parse the JSON response
    statsig_values = json.loads(response_data)

    # Return the values to the client
    return jsonify({'statsigValues': statsig_values})
```

```javascript
// Client-side code using @statsig/js-client
import { Statsig } from "@statsig/js-client";

// Fetch bootstrap values from your API
const response = await fetch("/statsig-bootstrap");
const { statsigValues } = await response.json();

// Initialize the client SDK with the bootstrap values
await Statsig.initialize({
  sdkKey: "client-sdk-key",
  initializeValues: statsigValues,
});
```

{% /accordion %}

{% accordion title="Response Format" %}
The method returns a JSON string containing the client initialize response. You need to parse this string to access the data:

```python
response_data = statsig.get_client_initialize_response(user)
response = json.loads(response_data)

# Access different parts of the response
feature_gates = response.get('feature_gates', {})
dynamic_configs = response.get('dynamic_configs', {})
layer_configs = response.get('layer_configs', {})
```

The response includes:

- `feature_gates`: Feature gate evaluations for the user
- `dynamic_configs`: Dynamic config and experiment evaluations
- `layer_configs`: Layer evaluations
- `has_updates`: Boolean indicating if there are updates
- `time`: Timestamp of the response
  {% /accordion %}
  {% /accordion-group %}

## Persistent storage

The Persistent Storage interface lets you implement custom storage for user-specific configurations. This enables you to persist user assignments across sessions, ensuring consistent experiment groups when a user returns. This is useful for client-side A/B testing where users must always receive the same variant.

```python
class PersistentStorage(PersistentStorageBaseClass):
    def __init__():
        # When you initialize, remember to call super.__init__()
        super().__init__()
        self.load_fn = self.load
        self.save_fn = self.save
        self.delete_fn = self.delete

    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 lets you implement custom storage for Statsig configurations, enabling advanced caching strategies and integration with your preferred storage systems.

```python
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 Provider interface lets you customize how the SDK logs internal messages.

```python
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 lets you monitor the health of the SDK by integrating with your own observability systems. This enables you to track metrics, errors, and performance data. For more information on the metrics emitted by Statsig SDKs, refer to the [Monitoring documentation](https://docs.statsig.com/infrastructure/sdk-monitoring).

```python
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

{% accordion-group %}
{% accordion title="How do I run experiments for logged out users?" %}
Refer to the guide on [device level experiments](https://docs.statsig.com/guides/first-device-level-experiment).
{% /accordion %}
{% /accordion-group %}

## Reference

### API methods

- `check_gate(user: StatsigUser, gate_name: str, options: Optional[FeatureGateEvaluationOptions] = None) -> bool`
- `get_dynamic_config(user: StatsigUser, config_name: str, options: Optional[DynamicConfigEvaluationOptions] = None) -> DynamicConfig`
- `get_experiment(user: StatsigUser, experiment_name: str, options: Optional[ExperimentEvaluationOptions] = None) -> DynamicConfig`
- `get_layer(user: StatsigUser, layer_name: str, options: Optional[LayerEvaluationOptions] = None) -> Layer`
- `get_feature_gate(user: StatsigUser, gate_name: str, options: Optional[FeatureGateEvaluationOptions] = None) -> FeatureGate`
- `get_parameter_store(user: StatsigUser, parameter_store_name: str, options: Optional[ParameterStoreEvaluationOptions] = None) -> ParameterStore`
- `log_event(user: StatsigUser, event_name: str, value: Optional[Union[str, float]] = None, metadata: Optional[Dict[str, str]] = None) -> None`
- `manually_log_gate_exposure(user: StatsigUser, gate_name: str) -> None`
- `manually_log_dynamic_config_exposure(user: StatsigUser, config_name: str) -> None`
- `manually_log_experiment_exposure(user: StatsigUser, experiment_name: str) -> None`
- `manually_log_layer_parameter_exposure(user: StatsigUser, layer_name: str, parameter_name: str) -> None`
- `override_experiment_by_group_name(experiment_name: str, group_name: str, id: Optional[str] = None) -> None`
- `get_client_initialize_response(user: StatsigUser, options: Optional[ClientInitializeResponseOptions] = None) -> ClientInitializeResponse`
- `shutdown() -> AsyncResult[None]`

### Fields needed methods

The following methods return information about which user fields evaluation requires:

- `get_gate_fields_needed(gate_name: str) -> List[str]`
- `get_dynamic_config_fields_needed(config_name: str) -> List[str]`
- `get_experiment_fields_needed(experiment_name: str) -> List[str]`
- `get_layer_fields_needed(layer_name: str) -> List[str]`

These methods return a list of strings representing the user fields that are required to properly evaluate the specified gate, config, experiment, or layer.