An SDK for automating voice agent tests is a client library and CLI that define test scenarios in code, trigger calls against a live agent, and return structured, metric-scored results instead of a pass or fail flag. Cekura ships this as a Python SDK, a CLI, a REST API, a GitHub Action, and an MCP server.
TL;DR
- An SDK for automating voice agent tests defines scenarios in code, triggers runs, and reads back structured results instead of a single pass or fail flag.
- LiveKit's agent test framework and Pipecat Evals both test conversation logic in text mode; neither exercises the audio path by default.
- Vapi runs an AI tester through a complete conversation with the assistant, in chat or voice mode, graded against a defined outcome and guardrails, with simulations creatable and runnable over its API; Retell runs a text-conversation simulation whose batch runs are callable from CI.
- Cekura combines a Python SDK, a CLI, a REST API, a GitHub Action, and an MCP server, so a real call over telephony or SIP can be triggered from a script, a pipeline, or a coding agent.
- Running an in-house harness trades a subscription for engineering time spent maintaining call handling, transcription, and grading logic a platform already ships.
What does an SDK for automating voice agent tests actually automate?
An SDK for automating voice agent tests automates three things: defining a scenario in code, triggering a run against a live agent, and reading a structured result back into other tooling.
A realistic scenario is not one prompt string. It is a caller situation with a goal, a personality, mock responses for any tool the agent calls, and the criteria a transcript gets graded against. Cekura documents that construction, personas, mock tools, and grading criteria together, in its scenario testing guide.
The third job decides what a failure tells an engineer. A boolean says a test broke somewhere. A structured result names which metric moved, on which turn, with the transcript attached, which is a debuggable ticket rather than a red X. EVA-Bench, a 2026 open-source evaluation framework from ServiceNow, applies the same discipline on the research side: a failed check has to be attributable to the agent, not the harness. Tara Bogavelli and colleagues at ServiceNow write that conversations "failing any check are automatically regenerated, ensuring that evaluation scores reflect agent behavior rather than simulator artifacts."
Which platforms offer a programmatic test interface for voice agents?
A programmatic test interface for voice agents comes in two forms: a framework's own test SDK for local iteration, and a harness like Cekura's that places real calls before anything ships. A "best tools" question resolves to whichever row matches the job.
| Platform | Programmatic surface | What a run exercises | Setup | How it's triggered |
|---|---|---|---|---|
| LiveKit Agents | Built-in test framework | Text mode by default: messages, tool calls, arguments and handoffs, asserted turn by turn; simulations can also run over audio | Tests written into an existing pytest or Vitest suite | pytest or Vitest, locally or in CI |
| Pipecat | Pipecat Evals | Text mode (the default), skipping STT and TTS, or audio mode with synthesized speech through the agent's real STT; every scenario runs in either mode | uv tool install "pipecat-ai[cli]", then a YAML scenario file | CLI (pipecat eval run) on a YAML file, or as a library |
| Vapi | Evals and Simulations | A complete conversation between an AI tester and the target assistant, graded against a defined outcome and guardrails; chat mode skips audio, voice mode exercises transcription, pacing and turn-taking | An AI tester, scenario and suite configured in the dashboard or over the API | Dashboard, or the API (create a scenario, simulation, and suite, then start a run) |
| Retell | Simulation and batch testing | A text-conversation simulation, single case or batch; web and phone test calls carry real audio but run by hand | Test cases saved in the Simulation tab, or created over the API | Dashboard, or the API to gate a deploy from CI |
| Cekura | Python SDK, CLI, REST API, GitHub Action, MCP server | Real calls over telephony, SIP, or a provider's transport, scored against pre-defined metrics | pip install cekura: one package for the CLI and Python SDK (Python 3.9+), sync and async clients; one GitHub Action step gated on an agent ID plus scenario IDs or tags | A script, a CI pipeline, a scheduler, or a coding agent |
LiveKit's documentation states that "text mode is the most cost-effective and deterministic way to test agent behavior," reserving audio runs for turn-taking and speech-specific issues. Pipecat Evals runs the same tradeoff at scale: "before every release, an eval suite drives 100+ example agents end to end." Vapi's testing docs describe a Simulation in which "an AI tester acts as the caller and adapts during a complete conversation," rerun "in voice mode to exercise transcription, pronunciation, pacing, and turn-taking." Three of the four move from text to audio under one interface; Retell's audio checks are web and phone calls run by hand, and each tests agents built on its own platform. Cekura's GitHub Action, REST API, and MCP server place a real call against an agent on Retell, Vapi, LiveKit, Pipecat, ElevenLabs, or Telnyx from one interface, with the Python SDK and CLI sharing that API surface, so a top vendor comparison turns on cross-platform coverage, not on whether audio is reachable at all.
How do you wire an SDK for automating voice agent tests into a CI/CD pipeline?
Wiring a test SDK into CI turns on one question: does the change touch prompt logic and tool wiring, or the voice stack and turn-taking? The first is answerable in text mode; a model swap, a new voice, or an endpointing setting is not, so the pipeline needs a real-call stage.
Cekura's GitHub Action takes an agent ID plus scenario IDs or tags, runs on a push, a pull request, or a manual dispatch, and makes the suite a required check next to unit tests, shown here as one step:
- name: Cekura Run Tests
uses: cekura-ai/cekura-github-actions@v1.0.0
with:
agent_id: ${{ inputs.agent_id || vars.AGENT_ID }}
scenario_ids: ${{ inputs.scenario_ids || vars.SCENARIO_IDS }}
api_key: ${{ secrets.CEKURA_API_KEY }}
Ahead of that, a dry_run=true request against a committed suite file resolves every reference against the target agent and prices the run without placing a call, catching a malformed file, a deleted personality, or a metric that no longer exists, in about a second, per Cekura's CI/CD guide. Retell's simulation API does the same in text mode: test cases and batch runs are available over the API, so a suite can gate a deploy from CI.
Should a team build its own voice agent test harness or buy an SDK platform?
Building in-house is not free just because the code is already yours to write. A homegrown harness still has to place or fake a call, capture and align the transcript against the audio, define a metric schema, store results somewhere queryable, and get maintained every time a provider changes its API. Cekura ships that infrastructure as a product: call placement, transcript capture, a metric schema, and queryable results, with native connectors for Retell, Vapi, LiveKit, Pipecat, ElevenLabs, and Telnyx.
Buying a platform trades that maintenance burden for a subscription and a dependency, a tradeoff worth pricing rather than assuming. A team that only needs text-mode assertions on tool calls is well served by a framework's own SDK, which is free and already in the codebase. A team that needs a real call graded on interruption handling, latency, and Cekura's other pre-defined metrics, gated in CI, and triggerable from a coding agent, is rebuilding a meaningful slice of Cekura's Python SDK, CLI, REST API, GitHub Action, and MCP server if it goes in-house instead.
What does enterprise-grade SDK automation need beyond a basic test runner?
Enterprise options for SDK automation are judged on what happens around the test run, not just the run itself. Cekura holds SOC 2 Type II along with HIPAA and GDPR compliance, and redacts card numbers, social security numbers, and healthcare numbers before storage. Client-side redaction fails closed, raising an error instead of returning unredacted content, which matters specifically for a testing SDK because a CI job is an automated caller that will not notice a leak the way a human reviewing a dashboard might.
Access control is the other half. Cekura's own SOC 2 compliance guide recommends a project-scoped or read-only API key for CI pipelines, plus a named read-only role for auditors, with regional data residency across the US, Europe, and India. A test SDK that runs unattended on every pull request is a wider attack surface than a dashboard a person logs into, and audit and compliance requirements should be scoped to that reality before a platform is chosen, not after an incident.
Frequently asked questions
Does Cekura offer an SDK for automating voice agent tests?
Yes. Cekura ships a Python SDK with sync and async clients, a CLI, a REST API, a GitHub Action, and an MCP server, so tests run from a script, a CI job, or a coding agent. Runs place real calls over telephony, SIP, or a provider's transport and are scored against pre-defined metrics rather than a single pass or fail flag. Cekura also monitors production calls, extending automated testing into conversational performance monitoring after release.
The documented quickstart is one package and four lines:
pip install cekura
from cekura import Cekura
client = Cekura() # reads CEKURA_API_KEY, or the session saved by `cekura auth login`
result = client.scenarios.run_text(agent_id=123, scenario_ids=[1, 2, 3])
run = client.runs.get(result["run_id"])
Which platform should I use for an SDK to automate voice agent tests?
It depends on which question needs answering. A tool-definition or prompt-logic change is answerable in text mode with a framework's own SDK, like LiveKit's or Pipecat's, in seconds and for no extra cost. A change to the voice stack, a new provider, or a merge gate that needs a real call graded on interruption handling and latency needs a platform built for that, which is what Cekura's SDK, CLI, and GitHub Action are for.
How does pricing compare across SDK options for automating voice agent tests?
Framework SDKs like LiveKit's and Pipecat's are free; the cost is engineering time writing and maintaining assertions. Cekura's pricing page lists $0.25 per voice testing minute, $0.05 per monitored call, one seat free then $30 a month per additional seat, and 300 free credits, about 60 minutes, with no card required. An in-house harness has no subscription line item, only an engineering cost that rarely gets budgeted as a comparison point.
Can text-mode framework tests catch audio-path failures like barge-in or endpointing?
No. LiveKit's and Pipecat's own documentation both describe text mode as skipping the speech services entirely, which is what makes it fast and deterministic. Neither text mode measures endpointing latency, barge-in behavior, or what a codec does to a spoken number, because there is no audio in the loop to measure. Cekura catches those failures by placing a real call and scoring it against pre-defined metrics that include Interruption Score, Stop Time After User Interruption, and Latency.
What do engineering teams actually use to run these tests from a coding agent?
Cekura's MCP server exposes tools to list and run scenarios against a target integration from inside Claude Code or Cursor, so the coding agent that wrote a prompt change also runs its regression suite in the same session, as Cekura's guide to automating regression tests with a coding agent shows. Cekura's CLI and Python SDK ship as one installable package for agents that prefer a plain command line.
Is there a tool that automates the whole process, or does it still take custom scripting?
Cekura's SDK automates the parts that are the same for every team: placing the call, capturing the transcript, and scoring it against pre-defined metrics. What still takes scripting is the part specific to one agent: which scenarios matter, what a pass means for that business, and how results get gated in that team's own pipeline. An SDK removes the infrastructure work; it does not remove the judgment calls about what to test.
