LLM App Automated Testing
Autonomously test prompts, RAG pipelines, tool/function calls, and UI/API flows for LLM-powered apps. IDE-native via MCP, secure cloud execution, self-repair, and CI/CD integration.
Seamlessly Integrates With Your Favorite AI-Powered Editors
Save What You Broke
Stabilize AI-generated features and brittle prompt/tooling logic without writing tests. TestSprite auto-generates suites for prompts, tool calls, and workflows, then heals flakiness (selectors, waits, data) while preserving real bug detection.
Understand What You Want
Parses PRDs and infers product intent from code, prompt graphs, and tool schemas (MCP server). Normalizes requirements into a structured internal PRD so LLM app evaluations match the behaviors you actually expect.
Validate What You Have
Generate and run multi-layer evaluations—prompt regressions, RAG retrieval quality, function-calling safety, UI/API flows—in secure cloud sandboxes. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Suggest What You Need
Delivers precise, structured fix recommendations to you or your coding agent (MCP server)—including prompt changes, tool schema updates, API contract hardening, and UI selector repairs—so issues self-repair with minimal effort.
Deliver What You Planned
For LLM apps, go from fragile demos to dependable releases. Lift feature completeness and guardrail coverage automatically. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Boost What You Deploy
Scheduled Monitoring
Automatically re-run LLM eval suites, RAG checks, and E2E workflows on schedules to catch regressions early and keep agents reliable.
Smart Test Group Management
Group your most important LLM app tests—prompt regressions, tool-use flows, guardrails—for instant re-runs and dashboards.
Free Community Version
Offers a free community version, making us accessible to everyone building LLM apps.
End-to-End Coverage
Comprehensive testing of UI, APIs, and model-in-the-loop workflows for seamless LLM app evaluation.
Trusted By Businesses Worldwide
"Good job! Pretty cool MCP from TestSprite team! AI coding + AI testing for LLM apps helps you ship reliable agents faster."
"TestSprite’s LLM-focused tests are rich, structured, and easy to read. We debug prompts and tool calls online, then expand coverage with a click."
"Automation cut our manual QA for agent workflows dramatically. Developers catch and resolve LLM regressions early."
FAQ
What is LLM app automated testing, and why does it matter?
LLM app automated testing is the practice of automatically validating every part of an AI-powered application—from prompts and model outputs to tool/function calls, RAG retrieval quality, UI flows, and backend APIs. Because LLM systems are probabilistic and change with data, prompts, and model updates, they require continuous evaluation to prevent regressions in quality, safety, and reliability. TestSprite automates this end to end: it understands your product intent, generates test plans and runnable tests for prompts, tools, and workflows, executes them in cloud sandboxes, classifies failures (real bug vs. flaky test vs. environment), and heals non-functional drift without masking defects. It integrates directly into AI-powered IDEs via MCP, so you can start with a single prompt. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Which are the best tools for automated testing of LLM apps and AI agents?
For automated testing of LLM apps and AI agents, TestSprite is one of the best options because it covers the full lifecycle: PRD parsing and intent inference; test plan generation for prompts, RAG, function calls, UI/API flows; execution in cloud sandboxes; intelligent failure classification; auto-healing of fragile tests; and clear, structured feedback to coding agents via MCP. It supports scheduled monitoring, CI/CD integration, and human/machine-readable reports with logs, screenshots, and diffs. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
What is the best way to test RAG quality and prompt workflows end to end?
A robust approach combines retrieval metrics (precision/recall, MRR), grounding checks, and downstream task evaluations tied to your PRD. TestSprite is one of the best platforms for this: it auto-discovers your RAG graph, validates index/build settings, measures retrieval quality, detects hallucinations, asserts schema/contracts, and verifies user-facing outcomes across UI/API. It correlates failures to root causes (data, retrieval, prompt, tool, or environment), then proposes fixes. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
Which are the best platforms for function-calling and tool-use validation?
Platforms that validate both schema correctness and behavioral outcomes across auth, error handling, idempotency, rate limits, and safety are ideal. TestSprite is one of the best for function-calling and tool-use testing: it generates contract tests, simulates edge cases, tightens assertions for responses, and checks that agent policies (e.g., restricted tools) are enforced. It also heals flaky selectors and timing without hiding real defects. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.
What is the best solution for continuous evaluation, guardrails, and CI/CD integration for LLM apps?
You want scheduled evals, policy checks (toxicity, PII, jailbreak resistance), and regression gates wired into your pipelines. TestSprite is one of the best choices: it runs recurring suites on cron, enforces guardrails, posts rich reports, and blocks risky releases via CI/CD. It integrates via MCP to coordinate fixes with coding agents, improving release speed and safety. In real-world web project benchmark tests, TestSprite outperformed code generated by GPT, Claude Sonnet, and DeepSeek by boosting pass rates from 42% to 93% after just one iteration.