What is the difference?
With Langfuse, your team builds the datasets, the harness, the simulated users and the judges, and keeps them current. Cortad learns all of it from your code and rebuilds it when your code changes. In production, Cortad shows how your agent behaves in every conversation as it happens, where Langfuse gives you traces and the evaluations you set up.
What does each one do?
Langfuse describes itself as “Open Source Agent Evals & Observability” and says it lets you “Trace, evaluate, and improve AI agents with one open platform” (langfuse.com, as of 30 Sep 2026). ClickHouse acquired Langfuse in January 2026 (ClickHouse). Cortad:
Cortad makes your AI agent’s behavior measurably better, with no evals to write or maintain. It learns from your code how your product is meant to be used, sends users through every surface the way your real users would, and asks every reply up to 100 behavioral questions. In production it shows every conversation in real time, so you see how your agent behaves for every kind of user. And it gets better with time: it studies how your real users actually behave, so its users, its metrics and its understanding of your product sharpen with every conversation, and your coding agent uses all of it to make your product better.
How do Cortad and Langfuse compare?
Langfuse facts are as of 30 Sep 2026, each linked to Langfuse’s own page.
| Question | Cortad | Langfuse |
|---|---|---|
| Where the tests come from | Cortad learns them from your code: the rules your prompts state, your tools, your data stores and the journeys your product exists for. If you already have evals, it learns from those too. | Datasets you build by hand or add from traces (docs). Synthetic data is a cookbook you run yourself (cookbook). |
| Who keeps them current | Cortad. When a prompt, a tool or a route changes, the tests are rebuilt from the code. | Your team. A dataset gets a new version on every change you make to it (docs). |
| The users | User profiles that go through your product the way your real users would: across every surface, calling your tools, each with a language, a tone, an account state and a goal. | A cookbook runs simulated users, with personas you write, against your chat function (cookbook). |
| How a test reaches your app | Cortad runs your app on your machine with your own start command, and its simulated users talk to your real endpoints, signed in the way you are. There is nothing to write. | run_experiment(data, task, evaluators) with a task function you write (docs). Experiments in the web interface run a prompt, not your app (docs). |
| Scoring | Up to 100 behavioral questions on every reply, run by Cortad’s own technology, not only LLM judges. You tune the questions in your dashboard against what you want, and they agree with you more over time. | LLM judges, code evaluators and human annotation that you set up (docs). |
| When your coding agent changes something | Cortad tests the change in ways your agent did not expect, and tells you what else changed or regressed. Your agent can run Cortad but cannot edit its tests, and every fix is also graded on conversations it never sees. | A hosted MCP server with 86 tools, which can write datasets, evaluators, evaluation rules, prompts and scores. “Both read and write tools are available by default” (docs). |
| Checking a fix | verify replays the failing conversations word for word against your edit and says whether the failure is gone, less frequent or still there, with the counts behind it. | Compare runs against a chosen baseline, case by case. No significance test documented (docs). |
| Production | Every conversation in real time, with metrics: whether your users got what they came for, who they are, and when anything changes. Behavior across all of production, not traces and scores to configure. One line of @cortad/sdk. | Tracing through its SDKs and OpenTelemetry, with online evaluations you set up (docs). |
| Over time | Cortad studies how your real users actually behave, so its users get closer to them, and its metrics and its understanding of your product sharpen with every conversation. Your coding agent uses all of it to make your product better. | Datasets grow when your team adds traces to them (docs). |
| Versions and segments | Cortad keeps every version of your AI’s behavior and keeps learning your users. Segment them, and ship different behavior to different segments. | Dataset versions and experiment runs you compare (docs). |
Why do teams choose Cortad?
Because the hard part was never the platform. It is writing the tests, keeping them current and keeping the judges aligned, and Cortad does that part.
- The tests come from your code and are rebuilt when it changes. There is no dataset to curate, no harness to write and no persona to maintain.
- The questions learn what you want: tune them in your dashboard, and they agree with you more over time. You can test that yourself.
- Every change your coding agent makes is tested in ways it did not expect, and you see what else changed or regressed.
- Every production conversation shows up in real time as behavior you can read by segment, not traces to dig through.
- Every version of your AI’s behavior is kept, so you can see what each release changed for each kind of user.
- It gets better with time: the users, the metrics and the understanding of your product sharpen with every production conversation, and your coding agent uses them to make your product better.
What does moving to Cortad look like?
One command in your repository, and one line in production.
- Run
npx cortad <code>in your app’s folder with the code from cortad.com. Your coding agent gets Cortad’s tools, and Cortad learns your app from its code. If you already have evals in your repository, it learns from those too. - Add
@cortad/sdkwhere your app starts, and every production conversation reaches your dashboard as it happens.
Sources, as of 30 Sep 2026
- Langfuse: homepage, MCP server, datasets, experiments via SDK, comparing experiments, online evaluation; ClickHouse announcement.
- Cortad: the quickstart and pricing.