Open Source Testing Projects
Software testing checks whether applications behave as expected, helps uncover defects, and reduces the risk of changes causing regressions. It ranges from checking individual functions to validating complete systems, and can include generated inputs, simulated services, network inspection, and security-focused testing. As applications increasingly use machine learning models and automated agents, testing also helps assess output quality, reliability, and safety across varied scenarios.
Open source testing tools include test frameworks, data generators, mocks and fakes, protocol debugging utilities, fuzzers, and evaluation systems. When choosing one, consider its maturity, license, maintenance activity, supported languages and platforms, integration with your development workflow, and resource requirements. Testing tools are useful to developers, quality engineers, security teams, and researchers building or maintaining software, APIs, and AI-enabled applications.
49 repositories · updated October 14, 2026
agent-anvil: Test AI Agent Tool Use in CI
Agent Anvil evaluates tool-using AI agents through scenario-based runs, trace checks, and optional semantic grading. It helps teams catch unsafe or incorrect tool behavior before it reaches production.
NewHealth 42/100
meshtastic-mcp: Connect AI Agents to Meshtastic Devices
A Python MCP server and agent skills for discovering, operating, observing, and testing Meshtastic devices and apps. Its portable core works over serial or TCP, with optional capabilities for firmware builds, mobile-app testing, replay, and hardware benches.
NewHealth 62/100
skills: Ground Coding Agents in Azure and Microsoft SDKs
Microsoft skills provides reusable guidance, agents, and MCP configurations for coding agents working with Azure SDKs and Microsoft AI Foundry. Install only the skills relevant to a project to guide code generation with current domain patterns.
ActiveHealth 59/100
ragas: Evaluate and Improve LLM Applications
Ragas is a Python toolkit for evaluating and improving LLM applications with metrics, generated test data, and feedback from production data. It suits teams building evaluation workflows for retrieval-augmented generation and other LLM-powered systems.
QuietHealth 47/100
aimock: Mock AI Services for Deterministic Testing
aimock is a TypeScript toolkit that provides local, deterministic mock endpoints for AI providers, agent protocols, and supporting services. It helps teams test AI applications without live credentials, network calls, or unpredictable provider responses.
ActiveHealth 67/100
nose2: Extend Python's unittest Framework
nose2 adds test discovery, configuration, and testing tools on top of Python's unittest framework. It suits teams that want to keep a unittest-style workflow while adding capabilities such as parameterized tests and improved assertion output.
ActiveHealth 45/100
green: Run Python Unit Tests with Clear, Parallel Output
Green is a Python test runner for unittest projects, with colorized, hierarchical results and parallel execution. It suits developers who want clearer test feedback and automatic test discovery without changing their unittest test classes.
InactiveHealth 27/100
freezegun: Control Time in Python Tests
Freezegun lets Python tests set and manipulate the system date and time, making time-dependent behavior predictable. Use it to test dates, timers, and time-based logic without waiting for real time to pass.
InactiveHealth 36/100
httmock: Mock HTTP Requests for Python Tests
httmock lets Python tests replace HTTP calls made with requests, so you can exercise API clients without contacting real services. It supports conditional URL matching and configurable mocked responses.
InactiveHealth 29/100
HTTPretty: Mock HTTP Requests in Python Tests
HTTPretty intercepts Python HTTP traffic at the socket level and returns registered fake responses. It helps developers test API integrations without relying on live services, and can capture requests for assertions or playback.
InactiveHealth 31/100
python-mocket: Mock Socket Traffic for Python Tests
Mocket lets Python developers intercept socket and SSL traffic so tests can mock HTTP and other protocol clients without making real network requests. It supports synchronous and asynchronous clients, strict network blocking, and custom socket-level mocks.
ActiveHealth 59/100
vcrpy: Replay HTTP Interactions in Tests
VCR.py records HTTP interactions to cassette files and replays matching responses in later test runs. It helps Python developers make network-dependent tests faster, deterministic, and usable offline after recording.
QuietHealth 49/100