getsentry/responses

Features

• API Request Mocking - Intercepts outgoing HTTP calls made with the Python Requests library to return predefined simulated responses.
• HTTP Mock Servers - Intercepts outgoing requests to specific URLs and returns predefined status codes, headers, and bodies to simulate a server.
• Traffic Serialization - Persists captured network responses to structured files on disk for offline playback.
• Request Matching Engines - Uses a lookup table of URL patterns and HTTP methods to resolve incoming requests to simulated responses.
• Module Monkey Patching - Replaces the internal request methods of the Requests library to divert traffic to a mock registry.
• Python HTTP Mocking Libraries - Provides a specialized library for intercepting and mocking HTTP calls within Python applications using the Requests library.
• API Response Recordings - Captures real API responses from a server and saves them to files for deterministic replay in tests.
• API Integration Testing - Provides tools to verify that applications correctly send requests and handle various server responses using mock data.
• API Simulation and Mocking - Implements flexible mock servers that generate different responses based on request bodies, headers, or sequences.
• Request Verification - Includes a framework for asserting that outgoing requests contain the expected headers, bodies, and query parameters.
• Mock Registry Management - Add, replace, or remove registered mock data dynamically during test execution to simulate different server scenarios.
• Network Failure Simulations - Simulates network-level errors such as connection timeouts and failures to verify application resilience.
• Sequential Response Queues - Maintains an ordered queue of responses for a single endpoint to simulate evolving server states.
• Exception Simulation Utilities - Triggers specific Python exceptions instead of standard HTTP responses to test application error handling.
• Predicate-Based Validations - Matches requests using custom logic and predicates applied to headers, bodies, and query parameters.
• Context Managers - Implements Python decorators and context managers to isolate mock configurations and reset the registry between tests.
• Dynamic Response Callbacks - Provides callback functions that generate mock HTTP responses based on incoming request data.
• Response Sequencing - Returns a sequence of different responses for the same URL to test state changes and retry logic.
• HTTP Traffic Recording - Captures real network interactions and saves them to files for deterministic replay in test suites.
• Lifecycle Management - Provides automated handling of mock setup, teardown, and state resetting using decorators and context managers.
• Network Request Assertions - Verifies that specific network requests were executed and tracks the number of times they were called.
• Outgoing Request Validation - Validates that outgoing HTTP request bodies and headers match expected schemas or criteria.
• Mocking and Fixtures - Utility for mocking the requests library.
• Mocking Libraries - Utility for mocking network requests.
• Testing Tools - Listed in the “Testing Tools” section of the Awesome Python awesome list.

Star-Verlauf