Job: tune prompts and models offline
Promptfoo, DeepEval, Ragas
Run prompt variations and score the answers with metrics. Strong for iterating on prompts and models. Driving your own live systems and pages is left to you.
Alongside your other tools
CertiQA alongside Promptfoo, DeepEval, LangSmith and Braintrust
You do not have to choose. Those tools evaluate prompts and outputs, or trace and monitor what an agent did. CertiQA tests controlled agent scenarios before release: realistic tool behavior and failures, verified across the agent, your systems and the customer-facing experience. Here is what each does best and what CertiQA adds.
Start from the job
Four kinds of tooling, one release process.
Testing an AI-powered product means checking both ordinary code and an agent that reasons and acts. Each kind of tool covers one slice of that.
Job: check pages and endpoints exactly
Playwright, Cypress, Postman
Exact, repeatable checks on pages and endpoints. They do not judge an agent's wording or catch prompt injection.
Job: trace and evaluate agent behavior
LangSmith, Arize Phoenix, Braintrust
Trace agent steps, compare evaluations and, depending on the product and setup, evaluate production traffic. These tools also support pre-release evaluation workflows.
Job: watch uptime and errors
Datadog, Sentry, New Relic
Uptime, resource use and stack traces. Valuable, but not designed to judge agent behavior.
CertiQA’s distinction is the test environment and scope: control which tools are mocked or routed to staging, inject realistic failures, and verify the resulting API state and customer-facing page alongside the agent conversation. Production traces can seed these repeatable scenarios. Use it alongside existing tracing and evaluation tools where they fit.
Side by side
What each tool is, and what it is best for.
| Tool | What it is | Best when |
|---|---|---|
| Promptfoo | Open-source (MIT) command-line tool for evaluating and red-teaming LLM apps, configured in files and run locally or in CI. It has joined OpenAI and says it remains open source. | You want a free tool with a broad red-team library and your team is happy to own the config and the infrastructure. |
| DeepEval | Open-source Python framework for evaluating LLM output with built-in metrics, written like unit tests. Its maker, Confident AI, sells a hosted platform around it. | Your team writes Python and wants pytest-style metric checks on answers. |
| LangSmith | LangChain's platform for tracing, offline and online evaluation, and monitoring LLM applications. | You want an integrated evaluation and tracing workflow, particularly with LangChain or LangGraph. |
| Braintrust | A framework-neutral platform for tracing agent runs, evaluations and experiments. | You want flexible scoring and experiment comparisons across agent steps and model or prompt changes. |
| CertiQA | A hosted platform for controlled, end-to-end agent scenarios: tool mocking, fault injection, red-teaming, CI gates and checks across APIs and web UI. | You need to reproduce realistic agent behavior safely, verify its effects across your systems and UI, and gate those scenarios in CI. |
Reviewed October 2026 from public information. Products and pricing change, so check each vendor's site before deciding. Spotted something out of date? Email support@certiqa.app.
If you need controlled end-to-end agent tests
What CertiQA does for that job.
Tool calls are mocked
Your agent's REST and MCP tool calls get controlled responses, so a test cannot refund a real customer or drop a real table.
Failures are part of the test
Inject timeouts, 429s, 500s and malformed responses, then assert the agent recovered correctly.
Attacks fit your agent
A refund tool gets refund-abuse and argument-tampering attacks, on top of the built-in OWASP LLM Top 10 scenarios.
The whole journey, not just the reply
Seed data through your API, read the result back and check the page a customer sees, in the same test case as the conversation.
Readable by the whole team
Tests are plain steps on a visual canvas, and your AI assistant can draft them over MCP. Reviewers read steps, not code.
One place for the gate
Run history, scorecards, CI exit codes and web UI tests with plain Playwright export, without a harness to maintain.
Know before you choose
When an open-source tool on its own may be enough.
- You need to read, fork and self-run everything. CertiQA is a hosted commercial platform.
- You want a free core. Promptfoo and DeepEval are free to start, while CertiQA plans begin at $299 a month after the trial.
- Your agent runs where CertiQA cannot reach it. CertiQA calls your agent over HTTPS, and agents in a private network use the customer-hosted runner on Enterprise Ultra.
- Your main need is live traffic tracing, which observability tools are built for.
Using more than one
These tools overlap less than they look. A common setup is an observability tool for production traffic, an open-source library for local prompt experiments and CertiQA as the pre-release gate in CI.
Request early access, then run the trial on one real agent and compare what it catches with what you catch today.
FAQ
Comparing AI agent testing tools
What is the best tool for testing AI agents?
No single tool covers every job, and most teams combine them. Open-source libraries such as Promptfoo and DeepEval suit teams that want to own their evaluation code. LangSmith and Braintrust are strong for tracing and scoring prompt or model changes. CertiQA is built for pre-release testing of agents that call tools: it mocks tools, injects failures, red-teams the agent and gates CI, with web UI testing on the same platform.
How is CertiQA different from Promptfoo?
Promptfoo is an open-source command-line tool for evaluating and red-teaming LLM apps that you configure in files and run yourself. CertiQA is a hosted platform that adds tool-call mocking, failure injection, attacks generated from your agent's own tools, a visual test builder, run history and CI gates, with no harness for your team to maintain. It does not replace Promptfoo: many teams keep it for prompt-level evaluations and use CertiQA for the journey around the agent.
How is CertiQA different from DeepEval?
DeepEval is an open-source Python framework for scoring LLM output with built-in metrics, written like unit tests. CertiQA works at the level of the whole agent: conversations, tool calls, failures, attacks and, if you want it, the system state and web page afterward, authored as plain steps rather than Python.
How is CertiQA different from LangSmith or Braintrust?
LangSmith and Braintrust support evaluation workflows as well as tracing production behavior. CertiQA’s emphasis is controlled end-to-end testing: mock or route tool calls, inject failures, verify the resulting API state and customer-facing UI, and gate the scenario in CI. Teams can use both; production traces can also seed CertiQA regression tests.
Can I use CertiQA alongside my existing tools?
Yes. Keep your existing evaluation and observability tools. Use CertiQA when you need controlled tool behavior, injected failures and checks across the agent, your systems and the customer-facing UI in one repeatable scenario. Start with one agent and compare what each tool catches.
Is CertiQA open source?
No. CertiQA is a hosted commercial platform. Your web UI tests export to plain Playwright TypeScript, and your suites are plain steps you can read, so you are not locked into an opaque format. If you need to read, fork and self-run everything, an open-source tool is the better fit.
Try it next to what you already use.
Run one real agent through the trial and compare what CertiQA catches with what your current tools catch.