Test Automation
Python's dynamic type system and exception handling make it easier to write code that handles invalid input gracefully — in theory. In practice, Python libraries that parse formats, process untrusted data, or call into native extensions can crash, hang, or exhibit undefined behavior just like C code. Atheris brings
Test Automation
Celery is Python's most popular distributed task queue. Testing Celery tasks well means covering task logic in isolation, testing how tasks interact with the broker and result backend, and verifying retry behaviour. This guide shows how to do all three with pytest. Setup pip install celery redis pytest
AI Testing
CrewAI's abstraction is clean: you define agents with roles and tools, wire them into tasks, and the crew handles delegation. What's less clean is testing it. A crew.kickoff() call can trigger a chain of LLM calls, tool invocations, and agent-to-agent handoffs that costs
AI Testing
Most LangGraph developers test their graphs end-to-end: compile the graph, call .invoke(), assert on the final output. This works until something goes wrong. When a multi-hop workflow produces a wrong answer, you have no idea which node introduced the error, whether it was a routing decision or
TestContainers
Testcontainers eliminates the gap between mocked database behavior and real database behavior by spinning up actual Docker containers during test runs. This post covers the full setup for Postgres, Redis, and MongoDB in both Node.js and Python, including parallel container execution, GitHub Actions integration, and concrete techniques to keep
GraphQL
Code-first GraphQL frameworks like Pothos (TypeScript) and Strawberry (Python) generate schemas from type-annotated code, which fundamentally changes the testing approach. Your types become the schema, so testing the types tests the schema. This post covers testing strategies specific to code-first GraphQL — plugin testing, type resolution, auth guards,
Hasura
Hasura generates a GraphQL API directly from your PostgreSQL schema, which means your tests must cover things that don't exist in hand-written APIs: role-based permission rules, relationship traversal, computed fields backed by SQL functions, and event triggers. This post shows a complete pytest-based testing strategy
Testing
Django ships with a solid testing infrastructure built on top of Python's unittest. Most Django developers use 10% of what's available to them. This guide covers the full picture: TestCase, the test Client, fixtures, factories, and how Django manages test databases. Every section includes working code
Testing
pytest-django replaces Django's built-in test runner with pytest, giving you parametrize, fixtures with dependency injection, better output, and a plugin ecosystem that Django's unittest-based runner simply can't match. This tutorial covers the complete setup and the features that matter most in
Testing
FastAPI is async-native. Endpoint functions declared with async def run in an async context, and testing them requires an async test client and an async test runner. pytest-asyncio combined with httpx.AsyncClient gives you everything you need. This guide covers async endpoint testing, database integration, WebSocket testing, and
Testing
Django REST Framework comes with its own testing layer built on top of Django's TestCase. APITestCase and APIClient understand DRF's authentication schemes, content negotiation, and response parsing. If you're writing raw Django TestCase to test DRF views, you're working harder than you
Testing
FastAPI's design makes testing unusually clean. Dependency injection isn't just an architectural pattern here — it's the mechanism that lets you swap real implementations for test doubles without monkey-patching. This tutorial covers TestClient, dependency overrides, database isolation, and async testing patterns you'll