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Why use Apaleo API Client?

The Apaleo API Client is a type-safe, async Python SDK built on Pydantic v2 and httpx. It provides a modern, developer-friendly way to integrate with Apaleo's hospitality platform—fast, easy to use, and fully documented. Here's why it stands out:

Performance & Efficiency

Async-First Architecture

  • Built on httpx for high-performance concurrent operations
  • Native async/await support for non-blocking I/O
  • Concurrent request batching for pagination
# Fetch multiple pages concurrently
properties = await client.core.v1.inventory.list_properties(
    PropertyListParams(is_concurrently=True, batch_size=100)
)

Smart Pagination

  • Automatic pagination handling with concurrent fetching
  • Configurable batch sizes for optimal performance
  • Built-in memory management for large datasets

Warning

The Apaleo API may have rate limits. Use the is_concurrently option with caution and consider implementing retry logic to handle potential rate limit responses.

Order of results is not guaranteed when using concurrent fetching.

Reliability & Robustness

Comprehensive Error Handling

  • Typed exceptions for all HTTP status codes
  • Detailed error messages with context
  • Graceful handling of API rate limits and transient failures
from apaleoapi.exceptions import ValidationError, NotFoundError

try:
    property = await client.core.v1.inventory.get_property("invalid-id")
except NotFoundError:
    print("Property not found")
except ValidationError as e:
    print(f"Invalid request: {e.message}")

Automatic Token Management

  • OAuth2 Client Credentials flow with automatic refresh
  • Token caching and expiry tracking
  • Seamless handling of 401 responses

Built-in Retries

  • Configurable retry logic for transient failures
  • Exponential backoff with jitter
  • Respect for Retry-After headers

Type Safety & Developer Experience

Complete Type Coverage

  • Full type hints for all methods and responses
  • Pydantic models for request/response validation
  • IDE autocompletion and error detection
# Type-safe parameter construction
params = PropertyListParams(
    page_number=1,          # int
    page_size=50,           # int
    include_archived=True,  # Optional[bool]
)

# Typed response handling
properties: PropertyList = await client.core.v1.inventory.list_properties(params)
for property: PropertyItem in properties.items:
    print(property.name)  # IDE knows this is a str

Note

Apaleo's models may have field names that conflict with Python built-in function names, variable names, or keywords. Use caution when naming your variables or consider using aliases to avoid conflicts. For example, if a model has a field named property, you can use an alias like from builtins import property as property_alias in your code to prevent shadowing the built-in @property decorator.

Rich Data Models

  • Structured dataclasses for all domain objects
  • Automatic serialization/deserialization
  • Validation with detailed error messages

Development & Testing

Dry-Run Mode

Perfect for development and testing:

# No actual API calls made
client = ApaleoAPIClient(..., dry_run=True)
properties = await client.core.v1.inventory.list_properties()
# Returns empty PropertyList for testing

Comprehensive Logging

  • Detailed request/response logging
  • Configurable log levels
  • Integration with Python's standard logging module
from apaleoapi.logging import setup_logging

logger = setup_logging(level="DEBUG")
# See all HTTP requests, responses, and timing information

Testing Support

  • Mock-friendly design with dependency injection
  • Async test patterns with pytest-asyncio
  • Factory classes for test data generation

Enterprise Ready

Production Deployment

  • Configurable timeouts and connection limits
  • Memory-efficient streaming for large responses
  • Monitoring and observability hooks

Security

  • Secure credential handling
  • TLS/SSL verification by default
  • No credential logging in production mode

Scalability

  • Concurrent request limiting to respect API quotas
  • Efficient connection pooling
  • Horizontal scaling support

Use Cases

Property Management Systems (PMS)

  • Sync property data and availability
  • Manage rates and inventory
  • Handle reservations and guest information

Channel Managers

  • Distribute inventory across multiple channels
  • Real-time rate and availability updates
  • Central reservation management

Revenue Management

  • Analyze booking patterns and performance
  • Dynamic pricing optimization
  • Competitive market analysis

Business Intelligence

  • Extract data for reporting and analytics
  • Monitor KPIs and performance metrics
  • Historical trend analysis

Integration Platforms

  • Connect Apaleo with other hospitality systems
  • Data synchronization between platforms
  • Workflow automation