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Learn moreOkareo
Synthetic users that stress-test your voice and text agents before launch
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What is Okareo?
Okareo is a developer tool. Okareo tests voice and text AI agents with synthetic users called Drivers that explore conversations instead of following scripts, then gates pull requests on the resulting evals and turns production failures into new test scenarios.
Okareo is a testing and evaluation platform for conversational AI agents, covering both voice and text. Its central idea is the Driver: a synthetic user given a personality, a context and a goal rather than a fixed script. Drivers hold long multi-turn conversations with your agent and behave unpredictably on purpose, so the output is not a pass or fail on a checklist but a map of behaviors the agent actually exhibits, including ones nobody thought to write down. The vendor reports one team finding 47 edges across 12 happy paths in a first run, six of which were already live in production. Voice testing is the most distinctive part. Synthetic callers speak 30 or more languages and locales, with background noise, crosstalk and clipping layered in, and the resulting audio, transcript and execution trace share a single timeline. That means a conversation designer and an engineer can look at the same failed call in the same view instead of trading recordings and logs. Text agents and headless agents are tested through the same workflow, so a team does not switch products when it adds a voice channel. For engineering teams, Okareo plugs into continuous integration. Simulations run on every commit and pull request, and releases can be gated on judge-based, symbolic or audio-based evals collected on one scorecard. A failing scenario blocks the merge. The loop then closes in the other direction: a failed production call can be converted into a synthetic scenario that runs against the next pull request, so the test library grows without anyone hand-authoring new cases. Okareo suits teams shipping customer-facing agents where conversation quality matters: support voice bots, bilingual assistants, tool-using agents. It is aimed at cross-functional groups (conversation designers, engineers, ML and QA) rather than a single developer. Compared with generic LLM evaluation frameworks, the differentiators are the voice simulation layer and the CI gate; teams that only need offline prompt scoring may find it heavier than necessary.
How do you use Okareo?
1Create an Okareo account
Sign up at the app to get a workspace. The workspace is shared, which is the point: conversation designers, engineers and QA work from the same projects rather than separate tools.
2Understand how Drivers work
Read the Drivers documentation before building anything. A Driver is defined by personality, context and goal rather than a script, which changes how you think about scenario coverage.
3Run a first simulation against your agent
Connect your text or voice agent and start a simulation run with a small set of Drivers. Review the transcript and trace to see which behaviors the agent actually produced.
4Add voice scenarios if you run a voice channel
For voice agents, configure synthetic callers in the languages and locales your customers use, with background conditions such as noise and crosstalk. Check audio, transcript and trace on the shared timeline.
5Wire the evaluation into CI
Add Okareo simulations and evals to your pipeline so they run on each commit or pull request. Set an eval gate that blocks a merge when a scenario regresses.6Route production failures back into the test library
Take a failed call from production and turn it into a new synthetic scenario. It then runs against future pull requests, so the suite grows with each incident.
Pros and cons
Pros
- Driver-based simulation surfaces edge cases that scripted QA never writes downAI
- Voice testing covers 30+ languages and locales with realistic noise and crosstalkAI
- Audio, transcript and trace share one timeline, so designers and engineers debug togetherAI
- CI integration gates merges on conversation quality rather than vibe testingAI
- Production failures can be converted into scenarios that run on future pull requestsAI
Cons
- No public pricing tiers on the homepage, so cost has to be established through a demoAI
- Simulation-focused design assumes you already have an agent built and deployed somewhereAI
- Value depends on team discipline: a CI gate only helps if the suite is maintainedAI
- The vendor's edge-case statistics come from its own customer anecdotes, not independent benchmarksAI
- Small teams without dedicated QA or conversation design staff may not use most of the platformAI
How much does Okareo cost?
Integrations
The homepage names CrewAI, Fireworks AI, Groq, GitHub, Google Cloud, Hugging Face, Cohere, CircleCI and Anthropic among the tools Okareo works with, including CI providers for pipeline gating.
Features
Synthetic Drivers with personality and goals for multi-turn text and headless agent testing; voice simulation in 30+ languages with noise and crosstalk; unified audio, transcript and trace timeline; judge, symbolic and audio-based evals on one scorecard; CI gating on every pull request; automatic conversion of production failures into test scenarios; shared workspace for designers, engineers, ML and QA.
Learn moreFrequently asked questions about Okareo
How do you use Okareo?
The walkthrough on this page covers 6 steps: 1. Create an Okareo account 2. Understand how Drivers work 3. Run a first simulation against your agent 4. Add voice scenarios if you run a voice channel 5. Wire the evaluation into CI 6. Route production failures back into the test library.What platforms does Okareo support?
Okareo is available on Web App.What does Okareo integrate with?
The homepage names CrewAI, Fireworks AI, Groq, GitHub, Google Cloud, Hugging Face, Cohere, CircleCI and Anthropic among the tools Okareo works with, including CI providers for pipeline gating.What are the limitations of Okareo?
No public pricing tiers on the homepage, so cost has to be established through a demo. Simulation-focused design assumes you already have an agent built and deployed somewhere. Value depends on team discipline: a CI gate only helps if the suite is maintained.
