toggly

Feature flag management SDK for Python - zero dependencies core library.

Can be used WITH or WITHOUT Toggly.io.

What is a Feature Flag

A feature flag (or feature toggle) is a software development technique that allows you to enable or disable features in your application without deploying new code. This enables:

• Gradual Rollouts: Release features to a percentage of users
• A/B Testing: Test different implementations with different user groups
• Kill Switches: Instantly disable problematic features
• Environment-Specific: Different feature states per environment

Installation

pip install toggly

Entity ContextProperty filters evaluate {kind, key, attributes} and are ANDed with user filters. Register kinds with register_context (startup PUT to sdk/{appKey}/contexts, opt out via register_contexts_on_startup=False).

Quick Start

Basic Usage with Toggly.io

from toggly import TogglyClient, TogglyConfig

# Create configuration
config = TogglyConfig(
    app_key="your-app-key",
    environment="Production"
)

# Initialize client
client = TogglyClient(config)
client.init()

# Check if a feature is enabled
if client.is_enabled("new-checkout-flow"):
    # New checkout implementation
    pass
else:
    # Original checkout implementation
    pass

Using Decorators

from toggly import TogglyClient, TogglyConfig, feature_flag, set_default_client

# Set up default client
config = TogglyConfig(app_key="your-app-key")
client = TogglyClient(config)
client.init()
set_default_client(client)

# Use decorator to control function execution
@feature_flag("new-algorithm")
def calculate_score(data):
    return new_algorithm(data)

# Or with a fallback
@feature_flag("new-algorithm", fallback=old_algorithm)
def calculate_score(data):
    return new_algorithm(data)

Using Context Manager

with client.feature_context("new-feature") as enabled:
    if enabled:
        # Feature is enabled
        do_new_thing()
    else:
        # Feature is disabled
        do_old_thing()

Async Support

from toggly import AsyncTogglyClient, TogglyConfig

config = TogglyConfig(app_key="your-app-key")
client = AsyncTogglyClient(config)

async def main():
    await client.init()

    if await client.is_enabled("new-feature"):
        await do_new_thing()

Feature Gates (Multiple Features)

Evaluate multiple features together:

from toggly import FeatureRequirement

# All features must be enabled
if client.evaluate_gate(
    ["feature-a", "feature-b"],
    requirement=FeatureRequirement.ALL
):
    # Both features are enabled
    pass

# Any feature must be enabled
if client.evaluate_gate(
    ["feature-a", "feature-b"],
    requirement=FeatureRequirement.ANY
):
    # At least one feature is enabled
    pass

User Targeting

Target features to specific users or groups:

from toggly import EvaluationContext

# Create user context
context = EvaluationContext(
    identity="user-123",
    groups=["beta-testers", "premium"],
    traits={"country": "US", "plan": "enterprise"}
)

# Evaluate with context
if client.is_enabled("premium-feature", context):
    # Feature is enabled for this user
    pass

Offline Mode (Without Toggly.io)

Use feature flags without a server connection:

from toggly import TogglyClient, TogglyConfig

config = TogglyConfig(
    feature_defaults={
        "feature-a": True,
        "feature-b": False,
        "feature-c": True
    }
)

client = TogglyClient(config)
client.init()

# Works completely offline using defaults
if client.is_enabled("feature-a"):
    pass

Caching

Use file-based caching for offline support:

from toggly import TogglyClient, TogglyConfig, FileSnapshotProvider

provider = FileSnapshotProvider(directory="/path/to/cache")

config = TogglyConfig(
    app_key="your-app-key",
    snapshot_provider=provider
)

client = TogglyClient(config)
client.init()  # Loads from cache if server unavailable

State Change Handlers

React to feature flag changes:

def on_feature_change(key: str, old_value: bool, new_value: bool):
    print(f"Feature {key} changed: {old_value} -> {new_value}")

config = TogglyConfig(
    app_key="your-app-key",
    state_change_handlers=[on_feature_change]
)

Custom Evaluators

Register custom filter evaluators:

from toggly.evaluator import FilterEvaluator, FeatureFilter
from toggly import EvaluationContext

class CustomEvaluator(FilterEvaluator):
    def evaluate(
        self,
        filter_: FeatureFilter,
        feature_key: str,
        context: EvaluationContext,
    ) -> bool:
        # Custom evaluation logic
        return context.traits.get("custom_field") == filter_.parameters.get("value")

# Register with client
client.registry.register("CustomFilter", CustomEvaluator())

Configuration Options

Debug Information

Get current client state:

info = client.get_debug_info()
print(f"Environment: {info.environment}")
print(f"Feature count: {info.feature_count}")
print(f"Last refresh: {info.last_refresh}")
print(f"Initialized: {info.is_initialized}")

Framework Integrations

For framework-specific features, use the integration packages:

• Django: pip install toggly toggly-django
• Flask: pip install toggly toggly-flask
• FastAPI: pip install toggly toggly-fastapi
• Redis/Memcached caching: pip install toggly toggly-cache[redis]

Requirements

• Python 3.8+
• No dependencies (zero dependencies core)

Type Hints

The library is fully typed with Python type hints and includes py.typed marker for static type checkers.

from toggly import TogglyClient, TogglyConfig, EvaluationContext

config: TogglyConfig = TogglyConfig(app_key="key")
client: TogglyClient = TogglyClient(config)
enabled: bool = client.is_enabled("feature")

Thread Safety

The TogglyClient is thread-safe and can be shared across threads. Internal state is protected with locks.

License

MIT

Find Out More

Visit Toggly.io for more information and to create your free account.