# The best voice agent testing platforms in 2026 These voice agent testing platforms phone your agent with simulated callers, so the bugs show up before your real callers do. *By Rogerio Chaves ยท November 12, 2026* Canonical: https://langwatch.ai/blog/best-voice-agent-testing-platforms ![The best voice agent testing platforms in 2026](https://langwatch.ai/blog/best-voice-agent-testing-platforms-cover.webp) Voice agent testing platforms call your voice agent with simulated callers, over real audio, and a judge passes or fails each call against criteria you write. If you still check your agent by calling it yourself after every prompt change, you only hear the calls you thought to make, in your own voice, from a quiet room. Your callers ring from the street, with accents you don't have, and cut the agent off halfway through a sentence. **Short answer:** The best voice agent testing platforms in 2026 are LangWatch, Hamming, Coval, Cekura, Bluejay and Roark. All six simulate callers and score calls. Pick on how the judge decides, whether tests run in your continuous integration (CI) pipeline, and whether the same platform watches your production calls. I ran the test from this post on our ACME demo support agent, which runs on OpenAI Realtime. The caller said an order number once, on a phone line with street noise, and the agent never read it back. One simulated call failed on that, and three extra lines in the prompt made the next run pass. ## The best voice agent testing platforms at a glance (2026) The six platforms below all phone your agent with simulated callers and judge the calls. They differ on how much you can shape the caller, what the judge looks at, how tests reach CI, and what happens to your production calls. | Platform | Simulated callers | How calls are judged | Tests in CI | Production calls | Voice stacks | |---|---|---|---|---|---| | [LangWatch](https://langwatch.ai/voice-ai-agent-testing) | Caller voice, interruptions from 0% to 100%, phone line or background noise, any persona you describe | A large language model (LLM) judge per criterion, on the transcript and the agent's traces, with its reasoning; response times from the audio | [Scenario](https://langwatch.ai/scenario/) tests in pytest or vitest, in your existing CI job | Traces play the call audio, and monitors score each conversation | ElevenLabs and phone numbers from the platform; Pipecat, Twilio, OpenAI Realtime, Gemini Live and your own pipeline in code; LiveKit and Vapi in preview | | [Hamming](https://hamming.ai/product/automated-testing) | Accents, interruptions, background noise, keypad tones and phone menus, 65+ languages | Built-in metrics, custom scorers and LLM judges | Runs in CI | [Monitoring](https://hamming.ai/product/voice-agent-monitoring) with Slack and PagerDuty alerts | [Vapi, Retell, LiveKit, Pipecat, ElevenLabs](https://hamming.ai/integrations); phone lines, SIP (internet calling) and WebRTC | | [Coval](https://www.coval.ai/products/simulation) | A library of 27 voices, 10 languages and 20 background environments, plus accents and personas | LLM judges on the transcript or the recording, plus acoustic, sentiment and latency metrics | Command-line interface (CLI), API and [GitHub Actions](https://docs.coval.ai/getting-started/github-actions-tutorial) that block merges | [Scores every production call](https://www.coval.ai/products/observability) | Pipecat Cloud, LiveKit, Twilio, SIP, WebSocket | | [Cekura](https://www.cekura.ai/) | Personalities with accent, language, speed, interruption timing, background noise and network conditions | LLM judges and Python metrics | GitHub Actions and API | Production monitoring | Retell, Vapi, ElevenLabs, LiveKit, Pipecat, Twilio | | [Bluejay](https://getbluejay.ai/) | Personas with language, accent and background noise; replays of real conversations | LLM judges, deterministic checks and 70+ metrics | GitHub App that fails the build when the score drops | Uptime checks and alerts | Vapi, Retell, LiveKit, Pipecat, ElevenLabs, Bland, phone, WebSocket | | [Roark](https://roark.ai/product/simulation) | Personas, accents, background noise, 45 languages, red teaming | Audio-native metrics, 500+ of them, and custom LLM judges; emotion from Hume | GitHub Action and CLI | Scores every production call | Vapi, Retell, ElevenLabs, LiveKit, Pipecat, Bland | Competitor cells come from each vendor's own site and docs, checked on November 12, 2026. ## What to compare in a voice agent testing platform Compare five things: how real the simulated caller sounds, what the judge reads, whether the platform handles load, how tests reach CI, and what it does with production calls. For an agent already taking calls, I'd weigh the judge and the production loop highest, since those two tell you why a call failed and stop it from happening again. 1. **Simulated callers over real audio.** The caller should speak through text-to-speech (TTS), with the voices and accents your callers have, background noise, a phone line and interruptions. A text simulation tests the prompt, and only audio reaches the speech-to-text (STT), the turn-taking and the timing, where voice bugs live. 2. **The judge.** Look for a verdict per criterion with the judge's reasoning, plus the recording, so you can play the call when the verdict surprises you. A judge that also reads your agent's traces can check a tool call against the tool call the agent made. 3. **Load.** If you expect call spikes, test them before your callers do. Hamming, Cekura and Roark all run many test calls at once. 4. **CI.** Voice tests should run on every prompt, model or provider change, from the same pipeline as your other tests, and fail the build. 5. **Production monitoring.** Real callers bring cases you never wrote down. The platform should score live calls, let you listen to the ones that fail and turn each one into a new test. ## What are the best voice agent testing platforms? LangWatch, Hamming, Coval, Cekura, Bluejay and Roark are the strongest picks in 2026. LangWatch tests voice and chat agents in one suite, from the platform or in code, next to your production traces. Pick Hamming for scale and compliance testing, Coval for CI, Cekura for caller personalities, Bluejay for replaying real conversations, and Roark for audio-native metrics. ### LangWatch LangWatch is an open-source platform for LLM evaluation, observability and agent testing, and its [voice agents](https://langwatch.ai/docs/agent-testing/voice-agents) run the same scenarios you write for chat agents, over audio. A simulated caller speaks to your agent and listens to each reply, turn by turn, and a judge scores the call against your criteria. #### Step 1: Add your voice agent Open **Agents** in the sidebar, press **New Agent** and pick **Voice Agent**. Under **Reached via**, pick **ElevenLabs agent** and paste the agent id, or pick **Phone number** and enter a number in international format, which LangWatch dials from your Twilio account. Add OpenAI under **Settings > Model Providers** too, because the caller speaks with OpenAI text-to-speech. ![The New Voice Agent drawer in LangWatch: a name, Reached via set to ElevenLabs agent, the agent id field, and the Talk to it and Create Agent buttons](https://langwatch.ai/blog/best-voice-agent-testing-platforms-new-voice-agent.webp) For an ElevenLabs agent, **Talk to it** lets you hear the agent from your browser before you write a single scenario. Voice agents on the platform are in private beta, so on LangWatch Cloud you write to support@langwatch.ai to turn them on. #### Step 2: Write the scenario and shape the caller Open **Agent Testing** and create a scenario the way you would for a chat agent: the situation, then the criteria the agent must meet. Under **Customize scenario**, the **Caller voice** group sets the caller's voice, how often they talk over the agent, from 0% to 100%, and an effect: none, a phone line or background noise. ![The scenario editor with one criterion, States the opening hours clearly, and the Caller voice group below it with Voice, Interrupts at 0% and Effects](https://langwatch.ai/blog/best-voice-agent-testing-platforms-caller-voice.webp) The caller voice belongs to the scenario, so every run of it uses the same caller, and a regression can't hide behind a friendlier voice. #### Step 3: Run it and read the verdict Press **Run** and pick your voice agent as the **Agent to be tested**. A voice run takes one to two minutes per scenario, because the caller speaks every turn and LangWatch transcribes it. The result shows each criterion passed or failed with the judge's reasoning, the recording of the whole call, and each turn's audio with its transcript. ![A failed voice run in LangWatch: the whole-call recording on top, each turn's audio with its transcript, and on the right the verdict FAILED with the failed criterion The agent states the opening hours and the judge's reasoning quoting what the agent said](https://langwatch.ai/blog/best-voice-agent-testing-platforms-run-verdict.webp) In this run the agent answered the phone fine and then told the caller it had no access to the opening hours, so the judge failed it on that criterion and quoted the line. You can play that turn's audio right next to the reasoning. #### Step 4: Run the same test from code, in CI The [Scenario](https://langwatch.ai/scenario/) software development kit (SDK) runs voice tests from your own test runner, in Python or TypeScript, and with `LANGWATCH_API_KEY` set each run also shows up in **Agent Testing**. This is the test I ran for this post, against an agent on OpenAI Realtime, from the [voice quick start](https://langwatch.ai/scenario/voice/getting-started/) and the [audio effects recipe](https://langwatch.ai/scenario/voice/recipes/effects/): ```python import pytest import scenario from scenario.config.voice_models import OPENAI_REALTIME_MODEL from scenario.types import AgentRole scenario.configure(default_model="openai/gpt-5-mini") ACME_SUPPORT = ( "You are the ACME phone support agent. Help callers with orders and returns. " "Keep replies to one or two short sentences. Ask for the order number when you need it." ) @pytest.mark.agent_test @pytest.mark.asyncio @pytest.mark.timeout(300) async def test_reads_back_the_order_number(): result = await scenario.run( name="order number on a noisy line", description=( "The caller wants to return a pair of shoes that are too small. " "They say their order number, A47192, once and quickly." ), agents=[ scenario.OpenAIRealtimeAgentAdapter( model=OPENAI_REALTIME_MODEL, voice="alloy", instructions=ACME_SUPPORT, role=AgentRole.AGENT, ), scenario.UserSimulatorAgent( voice="openai/nova", persona="In a hurry, speaks in short sentences.", audio_effects=[ scenario.effects.background_noise("street", volume=0.4), scenario.effects.phone_quality(), ], ), scenario.JudgeAgent(criteria=[ "The agent reads the order number back and waits for the caller to confirm it", "The agent does not promise refund times or policies it was not given", ]), ], script=[ scenario.user(), scenario.agent(), scenario.proceed(turns=4), scenario.judge(), ], ) if result.latency is not None: print(f"p95 response time: {result.latency.p95_response_time:.2f}s") print("PASSED", result.passed_criteria) print("FAILED", result.failed_criteria) assert result.success ``` The first run failed. The agent went straight to "exchange or refund?" and emailed a label, without ever repeating A47192: ```text p95 response time: 0.98s PASSED ['The agent does not promise refund times or policies it was not given'] FAILED ['The agent reads the order number back and waits for the caller to confirm it'] ``` The second criterion is there because of an earlier run, where the same agent told the caller refunds take 5 to 7 business days, a policy no one ever gave it. I added three lines to the prompt: ```diff "Keep replies to one or two short sentences. Ask for the order number when you need it." + " When the caller says an order number, read it back one character at a time " + "and wait for them to confirm before you go on. Never promise refund times: " + "say the return email has the details." ``` On the next run the agent spelled it back, "A, 4, 7, 1, 9, 2", and asked the caller to confirm: ```text p95 response time: 0.97s PASSED ['The agent reads the order number back and waits for the caller to confirm it', 'The agent does not promise refund times or policies it was not given'] FAILED [] ``` Each run took under a minute and a half. Swap the first agent for your transport: Scenario has [adapters](https://langwatch.ai/scenario/voice/choosing-an-adapter/) for Pipecat, ElevenLabs, Twilio, OpenAI Realtime, Gemini Live and your own STT, LLM and TTS pipeline, in Python and TypeScript, with LiveKit, Vapi and WebRTC in preview for Python. Add `interrupt_probability=0.5` to the caller to have them cut in on about half of the agent's turns, and put the file in the same [CI job](https://langwatch.ai/scenario/basics/ci-cd-integration) as your other tests. Our [voice testing tutorial](https://langwatch.ai/blog/how-to-test-voice-ai-agents) goes deeper on interruptions, accents and calls in other languages. #### Step 5: Watch your production calls Scenario also sent every turn of those test calls to the demo project as a trace, with spans for the audio sent and received, the agent's reply and the judge. Production calls get the same treatment: trace your agent with LangWatch, and the trace [plays the call audio back](https://langwatch.ai/docs/integration/python/tutorials/capturing-audio) in the Trace Explorer. ![The Trace Explorer listing voice call turns, each with what the caller said and what the agent answered, and an Origin column that tells simulated calls from application calls](https://langwatch.ai/blog/best-voice-agent-testing-platforms-voice-traces.webp) Then add a [monitor](https://langwatch.ai/docs/evaluations/online-evaluation/overview), an evaluator that scores your live traffic after each response. At **Thread Level** it reads the whole conversation once it goes idle, so a judge like "the caller had to repeat their order number" flags the calls worth a listen, and each bad call becomes the next scenario. ![The New Online Evaluation drawer with Thread Level selected, which evaluates all traces in a thread together, and a Polite and on-topic judge picked as the evaluator](https://langwatch.ai/blog/best-voice-agent-testing-platforms-monitor.webp) ### Hamming Hamming focuses on testing at scale and on compliance. Its callers bring accents, interruptions, background noise, keypad tones and phone menu trees, over phone lines, SIP or WebRTC. For load tests it says it has run 50,000+ calls at once for enterprise customers, so you can rehearse a launch-day spike before it happens. Its [compliance testing](https://hamming.ai/product/compliance) covers payment card and health data rules, and it holds SOC 2 Type II and signs a business associate agreement under HIPAA, the US health privacy law. ### Coval Coval comes from self-driving car testing: its founder led the evaluation job infrastructure team at Waymo. Its simulations pick from a library of voices, languages and background environments, its judges read the transcript or the recording, and its GitHub Actions block a merge on a regression, so it suits a team whose first goal is keeping voice regressions out of main. It also scores production calls, and its OpenTelemetry data works with Langfuse, LangSmith, Arize or Datadog. ### Cekura Cekura tests voice and chat agents through caller personalities, which set the accent, language, speed, interruption timing, background noise and network conditions of a call. You score calls with LLM judges or your own Python metrics, run load tests by setting how many calls go out at once, and monitor production calls in the same app. ### Bluejay Bluejay calls its simulated callers Digital Humans, with personas for language, accent, gender and background noise, and it can replay a real conversation from its transcript, which helps when you already have a folder of bad calls to turn into tests. Its GitHub App fails a build when the score drops, and its integrations include Bland next to Vapi, Retell, LiveKit, Pipecat and ElevenLabs. ### Roark Roark scores calls on hundreds of audio-native metrics, with sentiment and emotion from Hume, and its simulations cover dozens of languages and red teaming. It runs from a GitHub Action or its CLI, and the same metrics score every production call, which suits a team that reads its calls through metrics more than transcripts. ### Other tools Speechmatics' list of voice agent testing platforms also names Cyara, SuperBryn, TestZeus and Tuner. Evalion, which was on our 2025 list, now builds software for clinical trials. ## Hamming vs Coval vs other voice agent testing tools, which should I use? Pick by what you need the tests to prove. Hamming fits a regulated contact center that has to stress test at scale and show compliance. Coval fits a team that wants every merge blocked on a voice regression. LangWatch fits an AI platform team that runs chat and voice agents and wants one test suite and one place for traces. - **Regulated industries and big call volumes:** Hamming, for compliance scenarios and concurrent load. LangWatch runs self-hosted from the same image as LangWatch Cloud, for teams that can't send call data out. - **Engineers who want voice tests in every pull request:** Coval's GitHub Actions or Scenario in your own pytest or vitest job, with LangWatch. - **QA teams that think in personas:** Cekura and Bluejay. - **Teams that live in audio metrics:** Roark. - **AI platform teams with chat and voice agents:** LangWatch, because a scenario runs against either one, voice tests sit next to your production traces and monitors, and Scenario is open source, so the tests stay in your repo. ## Which voice agent testing fits your stack: Vapi, Retell, LiveKit, Pipecat and Bland Your voice platform probably ships some testing already, and it's a fine place to start. Add a dedicated platform when you want audio tests in CI, one suite across agents, or production calls feeding new tests. | Stack | Built-in testing | Testing it from LangWatch | |---|---|---| | [Vapi](https://docs.vapi.ai/observability/simulations-overview) | Evals with mock conversations and LLM judges; Simulations with an AI tester over voice or chat, with personality, voice, background sound and interrupt settings | Vapi adapter in Scenario, in preview for Python | | [Retell](https://docs.retellai.com/test/llm-simulation-testing) | Simulation and batch tests run as text conversations, graded on success criteria; web and phone tests are real calls | Dial the agent's phone number | | [LiveKit](https://docs.livekit.io/testing/simulations/) | Unit tests in text mode; Agent Simulations in text or audio on LiveKit Cloud, scored on turn-taking, interruptions, transcription and latency | LiveKit room adapter in Scenario, in preview for Python | | [Pipecat](https://docs.pipecat.ai/pipecat/evals/overview) | Pipecat Evals: scripted or simulated scenarios with an LLM judge and latency budgets, in text or audio, from the `pipecat eval` CLI | `PipecatAgentAdapter` connects to the WebSocket your bot already serves | | [Bland](https://docs.bland.ai/agents/evaluations) | Evaluations with judges and test cases you can generate from up to 50 production calls | Dial the agent's phone number | If you have no voice tests yet, take the last call that went wrong, write it as one scenario with two criteria, and run it before your next prompt change. ## Frequently asked questions ### What is a voice agent testing platform? A voice agent testing platform calls your voice agent with simulated callers over real audio, with the voices, accents, background noise and interruptions your real callers bring, and a judge passes or fails each call against criteria you write. Good platforms run the same tests on every change, in continuous integration, and also score your production calls, so a bad real call can become a new test. ### Do voice agent tests need real audio, or is a text simulation enough? Run both. A text simulation tests the prompt and the tools quickly and cheaply. Bugs that only voice has, like misheard order numbers, slow replies and the agent talking over the caller, live in the speech-to-text, the turn-taking and the timing, so only a test that goes through real audio catches them. ### How do I test a Bland AI voice agent? Bland has Evaluations built in, with judges and test cases you can generate from your own production calls. To test a Bland agent from outside, give its phone number to a platform that dials it: in LangWatch you add a voice agent reached by phone number, through your Twilio account, and run scenarios against it. Bluejay and Roark also list Bland integrations. ### Can I test a voice agent without writing code? Yes. In LangWatch you add a voice agent by its ElevenLabs agent id or a phone number, write the scenario in the editor, set the caller's voice, interruptions and effects, and press Run. Each run shows the verdict per criterion with the judge's reasoning, the recording and the transcript per turn. ### How long does a voice agent test take, and what does it cost? A voice scenario runs in real time, so a run takes about 30 to 120 seconds from the Scenario SDK and one to two minutes per scenario on the LangWatch platform. Every simulated call uses your voice provider's minutes, ElevenLabs or Twilio, plus OpenAI audio tokens for the caller.