If you evaluate LLM output with DeepEval, your eval results are some of the most valuable test data you have — and until now they lived in a separate tool from the rest of your tests. testrelic-deepeval changes that: it captures your DeepEval runs and uploads them to your TestRelic org, so evals sit next to your browser, API, and mobile tests with the same history, AI Insights, and dashboards.
Today TestRelic speaks Python. We're shipping four pytest-native packages — testrelic-pytest, testrelic-playwright, testrelic-deepeval, and testrelic-appium — that capture the same rich test analytics our JavaScript reporters do, and stream them to the same cloud workspace. Each one installs from PyPI, activates through pytest's plugin system with no conftest.py wiring, and starts uploading on your next pytest run.
The TestRelic MCP server started as a way to give AI coding assistants real testing context — projects, coverage, healing, impact analysis. With 3.1, it grows into a full AI-workflow surface: five new capability sets, a one-click Cursor Marketplace install, and a cleaner tool namespace. It's published as @testrelic/mcp v3.1.2.
Last month we brought Ask AI into Slack. Today we're doing the same for Microsoft Teams — with full parity. Run an Ask AI prompt in a channel, schedule a weekly QA digest, share a dashboard back from the web app, and get a ping the moment a quality gate fails. And alongside it, we're shipping public and private share links for any AI artifact.
Today we're launching the TestRelic Slack App — a way to bring Ask AI, scheduled QA digests, and dashboard share-back directly into the channels where your team already works. Type /testrelic in any channel to ask anything, schedule a weekly flaky digest to #qa-leads, or push any artifact from the web app to Slack in one click.