How to Test Voice Agents Built with LiveKit
This guide is for what comes next after the demo: deploying LiveKit agents to production, especially with real telephony integration, where text-only evaluation stops being enough. If you're still building WebRTC demos or internal prototypes, LiveKit's built-in testing helpers will get you there.
Quick filter: If your tests never touch real audio, you haven’t tested a LiveKit agent.
Here's what surprised me about LiveKit testing. The pytest-based framework is solid—you can validate logic, check tool calls, confirm error handling. Agents passed everything. Then we connected real audio streams and watched the same agents struggle. Turn-taking felt awkward. Latency compounded under network jitter. Interruptions broke the conversation flow. Text-mode testing validates that your agent is correct. Audio testing validates that your agent is usable.
There's a blind spot here—call it the "text-mode blind spot"—where everything looks perfect when you strip away the audio layer. LiveKit's built-in testing focuses on text/logic validation; you'll likely need additional tooling to test end-to-end audio behavior.
When you build on LiveKit, you're building a real-time WebRTC application where audio quality, turn-taking, timing, and tool usage are inseparable from the agent's behavior. The challenge is that most testing strategies stop at text-only evaluations, which confirm that the agent is correct in theory but not that it survives real streams. In this article, I will walk you through how to test voice agents built with LiveKit.
What is LiveKit?
LiveKit is a real-time WebRTC platform that powers interactive voice agents, streaming applications, and bidirectional audio experiences. It provides the infrastructure and SDKs for building agents that communicate over live audio, handle turn-taking in real time, and execute tool calls inside a WebRTC session. From a testing perspective, the key detail is that LiveKit is a real-time event system. Your agent is responsible for:
- Interpreting audio streams under variable network conditions
- Selecting the correct tool at the correct time
- Controlling turn-taking with accurate timing
- Maintaining conversational context across session events
What Should You Test in a LiveKit Agent?
Before choosing a testing strategy, confirm what matters most to reliability. For LiveKit teams, most evaluation falls into five categories:
LiveKit agents need to be tested like real-time systems. You are not only checking if the output is right, you are checking if the output is delivered at the right moment, in the right state, and without breaking the session. Playground tests cannot replicate this because real-time behavior depends on audio timing, network jitter, and streaming logic.
- Velocity: Time to first word, response timing, and session event handling
- Outcomes: Whether the agent completes tasks correctly and handles tool workflows end to end
- Intelligence: Context retention, grounding, hallucination resistance, and error handling
- Conversation: Turn-taking controls, interruption behavior, and mis-timing recovery
- Experience: Audio clarity, latency, overlapping turns, and trust markers
Three Ways to Test LiveKit Voice Agents
LiveKit’s Built-In Testing Helpers
LiveKit includes testing helpers for Python that integrate with pytest or your preferred test framework. These let you write behavioral tests that validate:
- Prompt logic and expected responses
- Tool calls and tool outputs
- Error handling and failure responses
- Grounding and hallucination avoidance
- Misuse resistance
LiveKit’s helpers operate in text-only mode. They validate the agent’s logic in isolation, which makes them ideal for fast iteration and unit-test coverage. They also help you prevent regressions during refactors because you can turn any failure into a behavioral test.
The issue is coverage. LiveKit’s built-in testing does not hit the audio pipeline. You're not exercising the real WebRTC connection, timing jitter, overlapping speech, network interference, or turn-taking behavior under real timing constraints. This leaves blind spots that only show up in real-stream conditions.
Manual Streaming QA
Manual QA inside a LiveKit room is still the fastest way to answer:
"Does this agent feel right?"
This helps you evaluate:
- Streaming stability
- Audio quality and compression artifacts
- Interruption behavior
- Conversation pacing
- User experience
However, once you start preventing regressions or testing multiple flows, manual QA becomes unscalable. You cannot reliably recreate edge cases like network jitter, rapid speaker changes, or a tool error at the exact moment of a streaming handoff. You also cannot validate hundreds of flows on demand.
End-to-End WebRTC Testing with Hamming
If your LiveKit agents will support production traffic, you'll need end-to-end testing that exercises the entire WebRTC path with measurable pass or fail criteria.
Hamming creates automated LiveKit rooms, runs synthetic callers against your agents, and evaluates each run across 50+ metrics.
Hamming answers questions that local testing cannot:
- Does the agent drop context during long sessions
- Does latency spike during tool execution
- Does turn-taking break if the user interrupts mid-sentence
- Does audio drift degrade grounding or hallucination resistance
- Does a network blip break session stability
At a high level, Hamming runs automated WebRTC rooms against your LiveKit agents and evaluates both:
- Outcomes: Did the agent execute the task correctly
- Interaction Quality: Did the agent behave correctly under real timing and audio conditions
LiveKit proves your agent behaves correctly as a program, WebRTC testing proves your agent behaves correctly as a call.
Production requires both.
What Hamming Validates for LiveKit Teams
WebRTC-Only Testing
No phone numbers, no SIP. LiveKit-to-LiveKit sessions only.
Flexible Room Provisioning
Auto-provision rooms or use your existing signaling and webhook flow.
Replayable Sessions
Every session includes transcripts, audio, and event logs.
50+ Metrics Across the Streaming Stack
Latency, time to first word, turn control, talk ratio, barge-in stability, confirmation clarity, hallucination scoring, tool call outputs.
Scale Testing
Run up to 1,000 concurrent sessions to validate behavior under load.
Regression Gates in CI
Gate production pushes on test results to prevent shipping breakage.
First Report in 10 Minutes
Connect, sync, run, review.
How to Get Started Testing LiveKit Agents
You can generate your first test report in under 10 minutes:
Connect LiveKit
Add your API key, secret, and server URL.
Configure Rooms
Choose auto-provisioned or controlled rooms.
Run WebRTC Tests
Trigger a test room and review the replay.
LiveKit's testing helpers validate agent logic at the text layer, which is essential for development and regression control. But they do not test the audio pipeline, timing behavior, or real-time instability. If you're deploying to production, you need WebRTC testing that simulates real sessions, reproduces streaming failures, and verifies that behavior, timing, and state integrity hold under real conditions.
Flaws but Not Dealbreakers
LiveKit testing has inherent challenges:
WebRTC testing isn't phone testing. LiveKit is WebRTC-native. If your users will call via phone (PSTN/SIP), you're testing a different audio path than they'll experience. The codecs, latency profiles, and failure modes are different.
Text-mode testing is still valuable. LiveKit's built-in testing validates agent logic cheaply and quickly. Don't skip it. Use text-mode for rapid iteration and audio testing for pre-production validation.
Network simulation has limits. We can inject jitter and packet loss, but real network conditions are more unpredictable than any simulation. Production monitoring remains essential.
