What is the difference?
With LangSmith, your team builds the datasets, writes the evaluators and the simulated-user code, and keeps them current. Its newest features start from production traces. Cortad learns its tests from your code before your first user exists, rebuilds them when your code changes, and in production shows how your agent behaves in every conversation as it happens.
What does each one do?
LangChain says: “Use LangSmith, the agent engineering platform, to improve every step of the agent development lifecycle” (langchain.com, as of 30 Sep 2026). 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 LangSmith compare?
LangSmith facts are as of 30 Sep 2026, each linked to LangChain’s own page.
| Question | Cortad | LangSmith |
|---|---|---|
| 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 built by hand or from production traces. Engine can propose dataset examples from failing traces (docs). |
| Who keeps them current | Cortad. When a prompt, a tool or a route changes, the tests are rebuilt from the code. | Your team, in its datasets, evaluators and test code (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 simulated user from the openevals library that you run in your pytest or vitest suite, with a persona you write as a system prompt (docs). |
| 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. | A function you write in your own test code, which takes one message and a thread id (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 queues, with tools to align a judge with human labels (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. | The LangSmith CLI and skills create and delete datasets and evaluators (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. | A compare view of regressions and improvements against a baseline experiment. 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, with online evaluators you configure and Engine’s issues on top (plans). |
| 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, and Engine proposes examples from failing traces (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. | Experiments you set up and compare (docs). |
| Frameworks | Not tied to one. Cortad runs your app with your own start command, whatever it is built with. | Made by LangChain for agents built on LangChain and LangGraph; other code is traced through its SDK (langchain.com). |
| Price | First run free. Pro $99 a month, Max $499 a month, runs and reruns included, unlimited teammates (pricing). | Plus $39 per seat a month with 10,000 base traces, then pay as you go. Engine is metered in usage units at $1.50 each (pricing). |
Why do teams choose Cortad?
Because they want their agent tested before every change and understood in production, without a team maintaining datasets, evaluators and simulated-user code.
- The tests come from your code and are rebuilt when it changes. They exist before your first user does.
- 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.
- One price per plan, not per seat or per trace, with unlimited teammates.
- 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
- LangSmith: homepage, pricing, CLI, multi-turn simulation, Engine, aligning evaluators, comparing experiments.
- Cortad: the quickstart and pricing.