Top tools for evaluating voice agents in 2025
Voice AI is moving fast. Companies are deploying agents that book appointments, handle support calls, and qualify leads. The challenge isn't building voice agents anymore. It's testing them at scale.
Most teams test voice agents by calling them manually after every prompt change. This works when you have one agent handling ten calls a day. It falls apart in production when agents handle thousands of conversations daily, each with different accents, background noise, and conversation patterns.
The tools in this guide exist because voice agents fail in ways text agents don't. A 200ms delay that's invisible in chat destroys a phone conversation. An accent the model hasn't heard causes cascading misunderstandings. Background noise turns a simple booking into a five-minute loop. You can't catch these problems by reading transcripts. You need to test with actual audio and realistic caller behavior.
What is voice agent evaluation?
Voice agent evaluation is the process of testing, monitoring, and improving conversational AI that handles audio input and output. It covers offline evals (pre-deployment testing against a dataset) and online evals (scoring live requests in production).
Voice adds complexity that text doesn't have. Latency matters more because conversations happen in real time. Users talk over the agent, change their minds mid-sentence, and express frustration through tone. Background noise, accents, and connection quality all affect comprehension.
The space is moving toward simulated conversations at scale, audio-native evaluation that catches issues transcripts miss, and CI/CD integration that tests every prompt change automatically.
Voice-only platforms vs. general evaluation tools
Two categories have emerged: dedicated voice platforms and general AI evaluation platforms with voice support. The real difference is depth vs. breadth.
Voice-only platforms
Tools like Roark, Hamming, Coval, and Evalion focus exclusively on voice. Simulation engines handle accents, interruptions, and background noise out of the box. Integrations with Vapi, Retell, LiveKit, and Pipecat are deep.
The tradeoff: if you're also building text agents or multimodal systems, you'll need separate tooling.
General evaluation platforms
Braintrust started with LLM evaluation and observability, then added voice. One platform covers text, audio, and multimodal AI. Prompt management, dataset versioning, and experiment comparison live in the same place.
The tradeoff: voice simulation requires partner integrations like Evalion rather than built-in simulation engines.
How we evaluated voice agent testing tools
We evaluated each platform across six criteria:
Simulation capabilities (25%): Realistic scenario generation, multi-turn conversations, accent and interruption simulation.
Evaluation metrics (25%): Voice-specific metrics, custom scorers, audio attachment support.
Production monitoring (20%): Live call tracking, alerting, performance trends.
Integration and workflow (15%): Voice platform compatibility, CI/CD integration, setup time.
Scale and performance (10%): Scenario volume, query speed.
Innovation (5%): Novel approaches to voice-specific challenges.
The 6 best voice agent evaluation tools in 2025
1. Braintrust
Best for: Teams who need evaluation infrastructure that connects voice testing to the rest of their AI workflow
Braintrust is an AI evaluation and observability platform. For voice, it's the evaluation layer: storing scenarios, running scorers, tracking results, connecting failures to your workflow. Simulation comes through Evalion. Braintrust handles everything after the call.
Voice-specific capabilities:
Debugging with actual audio: Attach raw audio files directly to traces. Replay exactly what the agent heard when investigating failures.
Evaluating audio models directly: Works with OpenAI's Realtime API to test voice tasks like language classification against actual speech.
Automated conversation simulation: Evalion runs simulations with callers that interrupt, express frustration, and change their mind. Results flow back to Braintrust for scoring.
Tracking voice-specific metrics: Build custom scorers for latency, CSAT, goal completion. Group results by metadata to find regressions.
Building test datasets without production recordings: Generate synthetic test cases with an LLM, convert to audio with TTS.
Most voice testing tools focus on simulation. Braintrust focuses on what happens after. Did the agent succeed? How does this run compare to the last? Which scenarios are regressing?
Pros:
- Audio attachments let you hear exactly what the agent heard when debugging failures
- Synthetic test generation creates multilingual voice datasets without production recordings
- Custom scorers for voice-specific metrics like latency thresholds and conversation flow
- Evalion integration provides realistic caller simulation without building it yourself
- Same evaluation workflow works across voice, text, and multimodal agents
Cons:
- Requires SDK integration for full tracing
Pricing: Free tier / Pro $249/month / Enterprise custom
Integrations: Evalion
2. Evalion
Best for: Realistic caller simulation with emotional personas
Creates autonomous testing agents that interrupt mid-sentence, change their mind, and express frustration. Normalizes results across scenarios for easier comparison. Integrates natively with Braintrust: scenarios live in Braintrust datasets, Evalion runs calls, results flow back automatically.
Pros:
- Autonomous testing agents with realistic behavior
- Emotional personas (frustrated, confused, impatient)
- Native Braintrust integration
Cons:
- Requires pairing with evaluation platform
Pricing: Contact sales
Integrations: Braintrust
3. Hamming
Best for: Large-scale stress testing with compliance requirements
Runs thousands of concurrent test calls using AI-generated personas with different accents, speaking speeds, and patience levels. Where other tools focus on functional testing, Hamming emphasizes regulatory edge cases. It simulates scenarios that could trigger PCI DSS or HIPAA violations, useful for teams in healthcare or financial services who need to prove their agents handle sensitive data correctly under pressure.
Pros:
- 500+ conversation paths tested concurrently
- Multi-language and accent simulation
- Compliance testing for PCI DSS and HIPAA
- 24/7 production monitoring with prompt version tracking
Cons:
- Best suited for teams with well-defined conversation flows
Pricing: Contact sales
4. Coval
Best for: CI/CD-integrated regression testing
Applies autonomous vehicle testing methodology to voice agents. Every prompt change triggers automated test runs against thousands of scenarios generated from transcripts, prompts, or workflow definitions. The platform catches regressions before deployment, not after users complain. Production monitoring feeds failed calls back into the test suite automatically.
Pros:
- Scenario generation from existing transcripts and workflows
- CI/CD integration catches regressions on every commit
- Production failures become test cases automatically
Cons:
- Recommends pairing with LLM observability platforms for managing traces
Pricing: Contact sales
Integrations: Retell, Pipecat
5. Roark
Best for: Production call analytics and replay testing
When a production call fails, most teams read the transcript and guess what went wrong. Roark captures the actual call and lets you replay it against updated agent logic. You hear the background noise, the user's tone, the awkward pause before they hung up. The platform tracks 40+ metrics and integrates with Hume to detect emotional signals that transcripts miss entirely.
Pros:
- Production failures become replayable test cases
- 40+ built-in metrics including sentiment via Hume
- One-click integrations with Vapi, Retell, LiveKit
- SOC2 and HIPAA compliant
Cons:
- Stronger on monitoring than pre-deployment simulation
Pricing: $500/month for 5,000 call minutes
Summary
| Tool | Starting Price | Best For | Key Differentiator |
|---|---|---|---|
| Braintrust | Free / $249/mo | Unified eval + observability | Audio attachments, voice metrics |
| Evalion | Contact sales | Realistic simulation | Emotional personas, Braintrust integration |
| Hamming | Contact sales | Scale stress testing | 500+ paths, compliance testing |
| Coval | Contact sales | CI/CD regression testing | AV methodology, auto test generation |
| Roark | $500/month | Production monitoring | Real call replay, 40+ metrics |
Why Braintrust works for voice agent evaluation
Audio attachments mean you debug with actual audio. Evalion integration means realistic simulation without building it yourself. Custom scorers track the metrics that matter. The feedback loop between production logs and evaluation datasets means tests improve over time.
Teams at Notion, Stripe, Zapier, and Instacart use Braintrust for AI evaluation. The same workflow that handles text agents handles voice.
FAQs
What is voice agent evaluation?
Voice agent evaluation tests how well conversational AI handles spoken interactions: simulating calls with different accents and caller behaviors, measuring response latency, tracking goal completion, and monitoring live performance. Unlike text evaluation, voice evaluation has to account for audio quality, interruptions, and timing. Braintrust supports this through audio attachments that let you replay exactly what the agent heard, custom scorers for latency and conversation flow, and Evalion integration for caller simulation.
How do I choose the right voice agent evaluation tool?
Three things matter. Simulation: can the tool generate test scenarios with different accents, interruptions, and emotional states? Metrics: does it track voice-specific metrics like response latency, goal completion, and CSAT? Workflow integration: does it connect to your CI/CD pipeline and feed production failures back into your test suite? Braintrust covers all three with synthetic dataset generation, custom scorers, and a platform that connects evaluation to production monitoring.
Is Braintrust better than Coval for voice agent testing?
They solve different problems. Coval specializes in simulation and regression testing with deep CI/CD integration, using methodology from autonomous vehicle testing. Braintrust provides broader evaluation and observability with native voice support, including audio attachments and OpenAI Realtime API integration. Many teams use Coval for simulation and Braintrust for evaluation, scoring, and tracking results over time. If you need one platform for voice, text, and multimodal agents, Braintrust handles all three.
What's the difference between voice evaluation and LLM observability?
LLM observability tracks what your model does: inputs, outputs, latency, token usage, costs. Voice evaluation tests whether the agent actually achieves its goals across realistic scenarios, handles interruptions without breaking, and maintains response times that feel natural. Observability tells you the agent responded in 450ms. Evaluation tells you whether that response was correct, followed instructions, and moved the conversation toward resolution. Braintrust combines both in one platform.
How does Evalion work with Braintrust?
Evalion handles simulation. It creates testing agents that behave like real callers: interrupting mid-sentence, expressing frustration, changing their mind, asking the same question twice. Braintrust handles evaluation: storing test scenarios in datasets, running scorers against results, comparing performance across runs, connecting failures to your development workflow. You define scenarios in Braintrust, Evalion runs the calls, and results flow back automatically.
How quickly can I see results from voice agent testing?
You can run your first evaluation within an hour. Create a dataset of test scenarios, define a task function that handles audio input, add scorers for the metrics you care about (latency, goal completion, instruction compliance), and run the experiment. Results show up in the UI where you can compare against previous runs, drill into failures, and add problem cases to your dataset. The Evalion integration adds voice simulation without additional setup.
What metrics matter most for voice agent quality?
Four metrics cover most use cases. Response latency should stay under 500ms for conversations to feel natural. Goal completion rate measures whether the agent accomplished what the caller needed. CSAT captures caller satisfaction through post-call surveys or model-based estimation. Instruction compliance tracks whether the agent followed its guidelines, stayed on topic, and avoided prohibited behaviors. Braintrust lets you build custom scorers for all of these, plus any domain-specific metrics you need.
What are the best alternatives to Hamming for voice agent testing?
Depends on what you need. For evaluation with broader observability and support for text and multimodal agents alongside voice, Braintrust provides a unified platform with audio attachments and custom voice metrics. For CI/CD-integrated regression testing, Coval offers simulation with autonomous vehicle methodology. For production monitoring and call replay, Roark captures failed calls and lets you test fixes against real audio. Braintrust works well as the evaluation layer paired with any of these.