Sumanth (@Sumanth_077) “Pytest for Google ADK agents! Testing AI agents is fundamentally different from” — TopicDigg

Pytest for Google ADK agents! Testing AI agents is fundamentally different from testing regular code. There's no single output to assert against. Every run can produce a different response. And when something goes wrong, it's rarely obvious which step caused it. AI agents built using Google ADK run across multiple steps. A model call plans the action. A tool executes it. Another model call generates the final response. Any of these can quietly go wrong without an obvious error. DeepEval is an open-source LLM evaluation framework, for evaluating large-language model systems. DeepEval's Google ADK integration brings Pytest to this problem. One call to "instrument_google_adk()" and every agent run is automatically traced. Every model call, tool invocation, and agent step becomes a component span you can evaluate independently. Testing works at two levels. End-to-end evaluation scores the full agent run on task completion. Component-level evaluation attaches metrics to individual LLM calls or tool spans, so you know exactly which step failed when a test breaks. The pytest integration works the same way as any other DeepEval framework. Parametrize your test with Goldens, run the agent inside the test function, call "assert_test()" at the end. A failing metric fails the test, which fails the build. Run it with "deepeval test run". You can also run evals outside CI. Loop through Goldens in a script, run the agent, and score each resulting trace without touching pytest at all. Key capabilities: • Auto-instrumentation with one function call • Trace, agent, LLM, and tool spans all independently evaluable • Native pytest integration with assert_test() for CI/CD • Metrics: TaskCompletion, AnswerRelevancy, Faithfulness, G-Eval and more • Script-based eval outside of CI • Optional Confident AI dashboard for trace visualization 100% open source. I've shared the link in the replies!