Test LiveKit voice agents with automated QA, scenario testing, and regression testing across realtime interactions, STT LLM TTS pipelines, multi-turn conversations, and tool calls.
Testing LiveKit agents is harder than testing traditional APIs or chatbots. Agent behavior depends on realtime voice interactions, audio streams, conversation flow, tool execution, and how reliably the agent performs across repeated sessions.
Manual testing can catch obvious issues, but it does not scale across scenarios, prompt changes, model changes, interruptions, telephony calls, or concurrent sessions.
A single successful test call does not prove that a LiveKit voice agent will behave consistently across real usage.
Cekura is a testing platform for LiveKit agents that enables automated QA, scenario-based testing, and regression validation across realtime voice interactions, STT–LLM–TTS pipelines, multi-turn conversations, tool calls, load conditions, and telephony environments. Teams can run repeatable tests on LiveKit agents before deployment, instead of relying on one-off manual checks.
Testing LiveKit agents in realtime voice interactions means validating how agents behave inside actual sessions, not just checking isolated responses. In LiveKit environments, agent behavior depends on how audio streams flow through rooms, how participants interact, and how responses are generated and delivered in real time. Manual testing cannot reliably reproduce these conditions. It is difficult to simulate consistent call flows, validate how agents behave across sessions, or catch failures like silence gaps, delayed responses, dropped audio, and broken turn-taking.
Cekura enables automated testing for LiveKit agents by simulating realtime WebRTC sessions and call flows across different conditions. Teams can test LiveKit voice agents across realtime sessions, validate responses to live audio input, simulate full call flows across browser and telephony pathways, and detect failures such as silence, timeouts, response delays, and interaction breakdowns.
Because LiveKit is built around realtime primitives like rooms, participants, and tracks, testing needs to operate at the same level. Automated testing makes it possible to run repeatable QA across LiveKit sessions and validate that agents behave consistently before deployment.
Testing LiveKit voice agents requires validating the full voice pipeline, from transcription to reasoning to speech output. Errors do not only happen at one layer. Inaccurate transcription, weak LLM reasoning, poor response generation, or low-quality TTS output can all break the end-user experience.
Cekura evaluates each stage of the STT–LLM–TTS pipeline in a single testing workflow. Teams can test transcription accuracy, validate response correctness, evaluate speech output quality, and run the same scenarios across different providers to compare performance and find where errors originate.
These failures are difficult to isolate with manual testing because the issue may appear as a bad response, even when the root cause is actually transcription, model reasoning, speech output, or provider behavior.
Cekura enables automated QA for LiveKit voice agents by evaluating each stage of the STT–LLM–TTS pipeline in a single testing workflow. Teams can test transcription accuracy, validate response correctness, evaluate speech output quality, and run the same scenarios across different providers.
This allows teams to run pipeline testing for LiveKit agents consistently, compare performance across voice stacks, and validate agent behavior before deployment.
Testing LiveKit agents in multi-turn conversations is where many issues actually show up. An agent might respond correctly once, but break down over a longer interaction by losing context, mishandling branching logic, or failing to complete a structured task across multiple turns.
These problems are hard to catch with manual testing because they require repeated conversations, varied user behavior, and consistent scenario reproduction. A manually tested flow might pass once and still fail when the user takes a different path.
Cekura enables scenario-based testing for LiveKit agents by simulating multi-turn conversations across different user behaviors and edge cases. Teams can run tests on LiveKit agents to validate branching flows, unexpected inputs, longer interactions, and full conversation outcomes.
This makes it easier to verify that LiveKit agents behave consistently across complete conversation sequences before deployment.
Prompts, models, tools, and logic change frequently. Small updates can introduce subtle regressions or performance degradations that manual testing often misses because tests are not repeatable.
Cekura enables teams to reuse the same test scenarios across versions, compare outputs after prompt or model changes, and quickly detect when agent behavior worsens so regressions are caught before they reach users.
LiveKit agents change constantly. Prompts are updated, models are swapped, tools are adjusted, and logic evolves over time. Even small changes can introduce subtle issues, including degraded responses, broken flows, inconsistent behavior, or worse performance across scenarios that previously worked.
Manual testing makes this difficult to manage because teams cannot reliably re-run the same conversations after every change. Without repeatable tests, regressions often go unnoticed until they affect real users.
Cekura supports regression testing for LiveKit agents by allowing teams to reuse the same test scenarios across versions. Teams can compare outputs after prompt changes, model changes, or workflow changes, and quickly detect when agent behavior gets worse. This ensures that updates do not silently break existing behavior and that LiveKit agents remain reliable as they evolve.
Realtime voice agents need to handle interruptions and conversation flow naturally. This is difficult to test manually because issues often only appear during live interaction, when users pause, interrupt, speak over the agent, or change direction mid-conversation. Common failures include the agent talking over the user, stopping too slowly, responding too late, missing an interruption, or creating awkward pacing that makes the conversation feel unnatural.
Cekura allows teams to test how LiveKit agents handle interruptions, turn-taking, and conversation flow in realistic scenarios. Teams can simulate users interrupting the agent, measure how quickly the agent stops speaking, and validate whether the conversation continues correctly after the interruption. This gives teams a repeatable way to test LiveKit voice agents for natural interaction quality before deployment.
Many LiveKit agents rely on tool calls and backend systems to complete tasks. This adds another layer of testing complexity because failures do not only come from what the agent says. They can happen when the agent calls the wrong tool, sends incorrect parameters, mishandles a tool response, or fails midway through a workflow. Manual testing is usually not enough to validate tool execution across different scenarios, especially when the agent needs to complete multi-step tasks or produce structured outputs.
Cekura enables testing of tool calls in LiveKit agents as part of the same automated QA workflow. Teams can validate that agents trigger the right actions, handle backend responses correctly, and produce expected outputs across different test scenarios. By simulating both normal and failure cases, Cekura helps teams catch tool execution issues before they affect real users.
Testing a single LiveKit session is not enough. Issues often appear when agents are handling many interactions at the same time. Performance can degrade under load, leading to slower responses, dropped interactions, delayed speech, or inconsistent behavior across sessions. For realtime voice agents, these failures are especially important because latency and reliability directly affect the conversation experience.
Cekura supports load testing for LiveKit voice agents by simulating large numbers of concurrent calls and sessions. Teams can evaluate how agents perform under realistic scale, validate latency and responsiveness, and identify bottlenecks before traffic increases. This allows teams to test LiveKit agents under load and ensure they remain stable as usage grows.
Many LiveKit deployments involve real phone calls, where conditions differ from browser-based WebRTC interactions. Audio quality, latency, routing behavior, caller environment, and telephony infrastructure can all affect how an agent performs.
A LiveKit agent that works well in a browser session may behave differently when tested over SIP or PSTN calls. That makes telephony testing important for teams deploying voice agents into real phone-based workflows.
Cekura enables testing for LiveKit agents over telephony by simulating real SIP and PSTN calls. Teams can validate inbound and outbound call flows, test telephony provider integrations, and ensure consistent agent behavior across phone-based interactions. This helps teams catch failures that only appear outside of browser-based WebRTC environments.
Tools to test LiveKit agents are needed when manual testing can no longer provide enough coverage. As agents become more complex, teams need a way to automate QA across realtime interactions, voice pipelines, multi-turn conversations, tool calls, and regression workflows.
Cekura provides a LiveKit agent testing platform that supports automated testing, scenario-based QA, and regression validation. Instead of relying on one-off calls, teams can run repeatable tests on LiveKit agents across sessions, environments, and versions.
This makes it easier to test LiveKit voice agents consistently before deployment and maintain reliability as prompts, models, and workflows change.
Manual testing can catch obvious issues in LiveKit agents, but it quickly breaks down in realtime environments. It is difficult to reproduce the same conversation twice, test edge cases consistently, or validate how an agent behaves across multiple sessions.
Manual QA also becomes harder as prompts, models, and workflows change. Without repeatable tests, teams cannot easily tell whether a new version improved the agent or introduced a regression.
Automated testing for LiveKit agents solves this by making QA repeatable and scalable. With Cekura, teams can run the same scenarios across different versions, test edge cases systematically, and perform regression testing after every change.
This allows for consistent validation of LiveKit agent behavior across realtime interactions without relying on one-off manual checks.
Cekura is a testing platform for LiveKit agents that helps teams validate agent behavior before deployment. It supports automated QA across realtime voice interactions, voice pipelines, multi-turn conversations, interruptions, tool calls, telephony flows, and load conditions.
With Cekura, teams can test LiveKit agents across the core conditions that affect production behavior:
By running repeatable tests across these areas, teams can catch issues earlier, reduce manual QA effort, and ensure LiveKit agents behave consistently before they reach users.
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