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.
Ask AI is TestRelic’s primary full-page conversational interface for turning natural language into answers about your organization’s tests, runs, and quality signals—and, when appropriate, into structured artifacts you can share or iterate on. The authoritative specification lives in the Ask AI documentation; this article expands that material with additional cross-links for teams evaluating Ask AI against IDE-only tooling such as the TestRelic MCP server.
Coding assistants are only as good as the context they see. Pasting stack traces into chat scales poorly; re-fetching structured run history from your org is better. The TestRelic MCP server implements the Model Context Protocol so compatible hosts—Cursor, Claude Desktop, VS Code MCP clients, and others—can call tools that read your TestRelic cloud projects, runs, coverage signals, and related data. The MCP overview positions MCP alongside the SDK reporters: reporters write run data from CI and laptops; MCP reads and acts on that data inside the IDE loop.
If you run Playwright in CI, you already have pass/fail signals. Testing observability means capturing the execution story around each failure: which URLs you touched, how the SPA navigated, what the network layer did, and which build or branch produced the run. The @testrelic/playwright-analytics reporter is designed for that workflow—it runs locally, writes structured JSON and HTML, and can feed the TestRelic cloud platform when you add an API key. This guide consolidates the official docs into one narrative for teams standardizing on Playwright.
Welcome to the official TestRelic documentation. Whether you ship browser suites with Playwright, mobile automation with Appium and WebdriverIO, or Maestro flows, the same idea applies: capture structured execution signals locally, optionally sync them to the cloud, and use dashboards, AI, and integrations to shorten mean time to understand a failure.
This hub page orients new readers: what TestRelic is, which packages to install, how the cloud fits in, and where to read next—including longer blog articles that tie multiple doc sections together for SEO-friendly learning paths.