Run Experiments for Your Agent

Experiments are pre-deployment batch tests. They run an application over a dataset and compare outputs with reusable evaluators. They are appropriate for prompt and model comparisons, regression tests, benchmarks, and CI quality gates.

Hand Off Production Evaluation Requests

If the user wants to score live traces or threads, monitor production quality, or block unsafe traffic, this is the wrong workflow.

If the
```
online-evaluations
```
skill is available, load it and follow it now.

Otherwise, tell the user to install it with:

bash

npx skills@1.5.19 add langwatch/skills/online-evaluations

Do not configure a monitor or guardrail from this skill.

Experiments and Scenarios

Use experiments for many single input and output examples with measurable results. Use the

scenarios

skill for end-to-end, multi-turn behavior and tool-calling sequences.

Determine Scope

For a general request such as "test my agent":

Read the agent code, system prompt, tools, and relevant git history.
Identify the behavior most likely to regress.
Create a domain-specific dataset.
Select evaluators that measure the intended behavior.
Create and run a real experiment.
Interpret the results and recommend concrete improvements.

For a targeted request, focus on that behavior and still run the resulting experiment.

Plan Limits

LangWatch's free plan has limits on prompts, scenarios, evaluators, experiments, and datasets. When you hit a limit, the API returns

"Free plan limit of N reached..."

with an upgrade link.

How to handle:

Work within the limits. If 3 resources of the relevant type are allowed, create 3 meaningful ones, not 10.
Make every creation count: each one should demonstrate clear value.
Show what works FIRST. If you hit a limit, summarize what was accomplished and note that upgrading the plan raises it — point to the subscription settings on the platform (license settings instead, if
```
LANGWATCH_ENDPOINT
```
is set — self-hosted).
Do NOT delete existing resources to make room or repurpose an existing resource to evade the limit.

Prerequisites

Use

langwatch docs <path>

to read documentation as Markdown. Some useful entry points:

bash

langwatch docs                                    # Docs index
langwatch docs integration/python/guide           # Python integration
langwatch docs integration/typescript/guide       # TypeScript integration
langwatch docs prompt-management/cli              # Prompts CLI
langwatch scenario-docs                           # Scenario docs index

Discover commands with

langwatch --help

and

langwatch <subcommand> --help

. List and get commands accept

--format json

for machine-readable output. Read the docs first instead of guessing SDK APIs or CLI flags.

If no shell is available, fetch the same Markdown over plain HTTP. Append

.md

to any docs path (e.g. https://langwatch.ai/docs/integration/python/guide.md). Index: https://langwatch.ai/docs/llms.txt. Scenario index: https://langwatch.ai/scenario/llms.txt

If anything fails or confuses you while following this skill (broken commands, docs that do not match reality, errors you had to work around), ask the user for permission and run

npx langwatch report --user-approved

with a

--title

and

--summary

(or

--session <transcript.jsonl>

) to send it to the LangWatch team. No login needed, secrets and personal data are redacted locally, and it directly shapes what gets fixed.

npx langwatch report --help

explains the options.

Projects and API keys: target a real project, not a personal one.

LangWatch has two kinds of project:

Team / shared projects: real projects inside an organization. Evaluations, experiments, prompts, datasets, simulations and instrumentation must always target one of these.
Personal projects: a private "My Workspace" scratch space tied to a single user. Never send a user's evaluations, experiments or production traces here: it is for personal exploration only and is easily confused with a real project.

And two ways to authenticate:

A project API key in
.env
(
```
LANGWATCH_API_KEY
```
): the credential everything in these skills uses. It is scoped to one real project. This is the default; prefer it unless the user explicitly asks for something else.
langwatch login --device
(AI-tools / SSO): a personal device session for wrapping coding assistants (
```
langwatch claude
```
,
```
langwatch codex
```
, …). It is NOT for evaluations, prompts, datasets, scenarios or SDK instrumentation, and it points at a personal workspace. Do not run it to set up the work in these skills.

So for anything in these skills: make sure

LANGWATCH_API_KEY

for a real, shared project is in the project's

.env

— most environments already have this provisioned. Do NOT run

langwatch login

to pick a project, and never default to a personal project. If

LANGWATCH_ENDPOINT

is set, they are self-hosted, use that endpoint instead of app.langwatch.ai.

Read the experiment documentation before writing code:

bash

langwatch docs evaluations/experiments/overview
langwatch docs evaluations/experiments/sdk

Build a Domain-Specific Dataset

The examples must match what the application actually does. Read the system prompt, function signatures, tools, and knowledge sources first.

Good examples resemble real requests to this application and cover normal cases, edge cases, and past failures. Never use generic trivia such as "What is 2+2?" or "What is the capital of France?" unless the application itself is a trivia system.

If an existing LangWatch dataset is appropriate, inspect it with

langwatch dataset list --format json

and

langwatch dataset get --help

. Otherwise create the dataset in code or use the

datasets

skill.

Create the Experiment

Use the SDK that matches the codebase. Keep credentials in environment variables and use the project's existing dependency manager.

Python

python

import langwatch
import pandas as pd

dataset = pd.DataFrame([
    {
        "input": "A realistic request for this application",
        "expected_output": "The expected behavior",
    },
])

experiment = langwatch.experiment.init("agent-regression")

for index, row in experiment.loop(dataset.iterrows()):
    response = my_agent(row["input"])
    experiment.evaluate(
        "ragas/response_relevancy",
        index=index,
        data={"input": row["input"], "output": response},
        settings={"model": "openai/gpt-5-mini", "max_tokens": 2048},
    )

TypeScript

typescript

import { LangWatch } from "langwatch";

const langwatch = new LangWatch();
const dataset = [
  {
    input: "A realistic request for this application",
    expectedOutput: "The expected behavior",
  },
];

const experiment = await langwatch.experiments.init("agent-regression");

await experiment.run(dataset, async ({ item, index }) => {
  const response = await myAgent(item.input);
  await experiment.evaluate("ragas/response_relevancy", {
    index,
    data: { input: item.input, output: response },
    settings: { model: "openai/gpt-5-mini", max_tokens: 2048 },
  });
});

Read

langwatch docs evaluations/evaluators/list

before choosing an evaluator, and take the type slug from

langwatch evaluator types --format json

— never from memory. If an evaluation fails with a

validation_error

naming the slug and an

expected

list, correct it from that list and retry once. Reuse project evaluators when appropriate. A scoring function is part of the experiment, not the experiment itself.

Run and Verify

Always execute the experiment. An unrun experiment is incomplete.

Python script: run it with the project's Python environment.
Notebook: execute all cells, for example with
```
jupyter nbconvert --to notebook --execute
```
.
TypeScript: run it with the project's package manager, for example
```
pnpm exec tsx experiment.ts
```
.

After it runs, verify the result with the CLI:

bash

langwatch experiment list --format json

If the CLI supports a more specific read or run for the installed version, discover it with

langwatch experiment --help

before using it.

Consultant Mode

After delivering initial results, transition to consultant mode to help the user get maximum value.

Phase 1: read first. Before generating ANY content: read the codebase end-to-end (every system prompt, function, tool definition), study git history for agent-related changes (

git log --oneline -30

, then drill into prompt/agent/eval-related commits because the WHY in commit messages matters more than the WHAT), and read READMEs and comments for domain context.

Phase 2: quick wins. Generate best-effort content based on what you learned. Run everything, iterate until green. Show the user what works and create the a-ha moment.

Phase 3: go deeper. Once Phase 2 lands, summarize what you delivered, then suggest 2-3 specific improvements grounded in the codebase: domain edge cases, areas that need expert terminology or real data, integration points (APIs, databases, file uploads), or regression patterns from git history that deserve test coverage. Ask light questions with options, not open-ended ("Want scenarios for X or Y?", "I noticed Z was a recurring issue. Add a regression test?", "Do you have real customer queries I could use?"). Respect "that's enough" and wrap up cleanly.

Do NOT ask permission before Phase 1 and 2. Deliver value first. Do NOT ask generic questions or overwhelm with too many suggestions. Do NOT generate generic datasets. Everything must reflect the actual domain.

Common Mistakes

Do not configure production monitoring or guardrails from this skill.
Do not call a batch run an online evaluation.
Do not use placeholder datasets.
Do not guess SDK APIs when the installed documentation is available.
Do not stop after writing the experiment. Run it and inspect the real result.

experiments

NPX Install

Tags

SKILL.md Content

Run Experiments for Your Agent

Hand Off Production Evaluation Requests

Experiments and Scenarios

Determine Scope

Plan Limits

Prerequisites

Build a Domain-Specific Dataset

Create the Experiment

Python

TypeScript

Run and Verify

Consultant Mode

Common Mistakes