> ## Documentation Index
> Fetch the complete documentation index at: https://langwatch.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> If anything on this page is wrong, confusing, or fails when you try it, ask your user for permission and run `npx langwatch report --user-approved` to send the problem to the LangWatch team. No login is needed and secrets are redacted locally. `npx langwatch report --help` has the details.

# Running Experiments in CI/CD

> Automate LLM quality gates by running experiments in your CI/CD pipelines.

There are two ways to run experiments in your CI/CD pipeline:

1. **Platform Experiments** - Configure the experiment in LangWatch, then trigger it from CI/CD with a single line
2. **Experiments via SDK** - Define the entire experiment in code and run it in CI/CD

Choose based on your needs:

| Approach | Best For |
| - | - |
| **Platform Experiments** | Non-technical team members can modify experiments; configuration lives in LangWatch |
| **Experiments via SDK** | Version control your experiment config; full flexibility in code |

***

## Option 1: Platform Experiments

Configure your experiment once in the LangWatch Experiments via UI, then trigger it from CI/CD.

### Setup

1. **Create your experiment** in the [Experiments via UI](https://app.langwatch.ai/@project/experiments)
   * Add your dataset
   * Configure targets (prompts, models, or API endpoints)
   * Select evaluators
   * Run it once to verify it works

2. **Get your experiment slug** from the URL:
   ```text theme={null}
   https://app.langwatch.ai/your-project/experiments/your-experiment-slug
                                                      ^^^^^^^^^^^^^^^^^^^^
   ```
   Or open the **Run Options** menu in the experiment toolbar and choose **Run in CI/CD** for ready-to-copy snippets.

![The Run via API dialog with the Python, TypeScript and Shell tabs, the data source tabs and the Python snippet](https://mintcdn.com/langwatch/iG2HKiUWiJlQldW1/images/evaluations/workbench-run-via-api.png?fit=max\&auto=format\&n=iG2HKiUWiJlQldW1\&q=85\&s=39a19b7c134619f85cdbea45561d1342)

3. **Run from CI/CD:**

> [tab] Python
>
> ```python theme={null}
> import langwatch
>
> result = langwatch.experiment.run("your-experiment-slug")
> result.print_summary()
> ```

> [tab] TypeScript
>
> ```typescript theme={null}
> import { LangWatch } from "langwatch";
>
> const langwatch = new LangWatch();
> const result = await langwatch.experiments.run("your-experiment-slug");
> result.printSummary();
> ```

The experiment runs with the configuration saved in LangWatch.

### GitHub Actions Example

```yaml theme={null}
name: LLM Quality Gate

on:
  pull_request:
    branches: [main]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - name: Install LangWatch
        run: pip install langwatch

      - name: Run experiment
        env:
          LANGWATCH_API_KEY: ${{ secrets.LANGWATCH_API_KEY }}
        run: |
          python -c "
          import langwatch
          result = langwatch.experiment.run('my-experiment')
          result.print_summary()
          "
```

### Options

```python theme={null}
import langwatch

result = langwatch.experiment.run(
    "my-experiment",
    timeout=300.0,           # Max wait time (seconds)
    poll_interval=5.0,       # How often to check status
    on_progress=lambda done, total: print(f"{done}/{total}"),
)
result.print_summary(exit_on_failure=True)  # Exit with code 1 on failures
```

### Reading per-row results

`run(...)` waits for the run to finish and gives you the per-row results, so you can assert on them in CI or inspect them locally:

> [tab] Python
>
> ```python theme={null}
> import langwatch
>
> result = langwatch.experiment.run("my-experiment")
> result.print_summary()      # CI-friendly summary; exits 1 on failures
> df = result.results         # per-row results as a pandas DataFrame
> print(result.run_url)       # open the run in LangWatch
> ```

> [tab] TypeScript
>
> ```typescript theme={null}
> const { rows, runUrl } = await langwatch.experiments.runWithResults("my-experiment");
> console.table(rows);        // per-row results
> console.log(runUrl);        // open the run in LangWatch
> ```

### Choosing what to evaluate

By default the run uses the dataset attached to the experiment. You can override the data from CI without changing the platform configuration:

* `parameters`: constant inputs merged into every row (and the single row when there is no dataset), for example a feature flag or a model name
* `data`: inline rows to evaluate
* `dataset_id`: a different platform dataset by id (`data` and `dataset_id` are mutually exclusive)
* `row_indices`: a subset of dataset row indices to evaluate, instead of the whole dataset
* `concurrency`: how many cells run at once, from 1 to 24, for this run only. A cell is one row against one target, so an experiment with two targets has two cells per row. Without it, the run uses the server's default. Use `1` to run the cells one at a time, in order, for example so the first row fills a prompt cache before the next rows read it. The experiment's saved run options stay unchanged.
* `dataset_version`: the version of the saved dataset to run, for example `1`. It overrides the version the experiment pins. Without either, the run saves the dataset's current content as a version and reads that, so every run records the exact rows it used. A version the dataset does not have answers `404` with `dataset_version_not_found`

> [tab] Python
>
> ```python theme={null}
> import langwatch
>
> # Inline rows
> langwatch.experiment.run("my-experiment", data=[{"input": "What is 2 + 2?"}])
> # A platform dataset by id
> langwatch.experiment.run("my-experiment", dataset_id="dataset_xxxxxxxxxxxx")
> # Constant parameters merged into every row
> langwatch.experiment.run("my-experiment", parameters={"model": "gpt-6-luna"})
> # One row at a time, in order
> langwatch.experiment.run("my-experiment", concurrency=1)
> # Version 1 of the experiment's saved dataset
> langwatch.experiment.run("my-experiment", dataset_version=1)
> ```

> [tab] TypeScript
>
> ```typescript theme={null}
> import { LangWatch } from "langwatch";
>
> const langwatch = new LangWatch();
>
> // Inline rows
> await langwatch.experiments.runWithResults("my-experiment", { data: [{ input: "What is 2 + 2?" }] });
> // A platform dataset by id
> await langwatch.experiments.runWithResults("my-experiment", { datasetId: "dataset_xxxxxxxxxxxx" });
> // Constant parameters merged into every row
> await langwatch.experiments.runWithResults("my-experiment", { parameters: { model: "gpt-6-luna" } });
> // One row at a time, in order
> await langwatch.experiments.runWithResults("my-experiment", { concurrency: 1 });
> // Version 1 of the experiment's saved dataset
> await langwatch.experiments.runWithResults("my-experiment", { datasetVersion: 1 });
> ```

Test suite runs also accept a `parameters` field. There the values apply to every scenario in the run. See [Scenario run parameters](https://langwatch.ai/docs/agent-testing/run-parameters.md).

***

## Option 2: Experiments via SDK

Define your entire experiment in code. This gives you full control and version control over your experiment configuration.

### Basic Example

> [tab] Python
>
> ```python theme={null}
> import langwatch
>
> # Load your dataset
> dataset = langwatch.dataset.get_dataset("my-dataset").to_pandas()
>
> # Initialize experiment
> experiment = langwatch.experiment.init("ci-quality-check")
>
> # Run through each test case
> for idx, row in experiment.loop(dataset.iterrows()):
>     # Call your LLM/agent
>     response = my_llm(row["input"])
>
>     # Run evaluators
>     experiment.evaluate(
>         "ragas/faithfulness",
>         index=idx,
>         settings={},
>         data={
>             "input": row["input"],
>             "output": response,
>             "contexts": row["contexts"],
>         },
>     )
>
> # Print summary and exit with code 1 on failure
> experiment.print_summary()
> ```

> [tab] TypeScript
>
> ```typescript theme={null}
> import { LangWatch } from "langwatch";
>
> const langwatch = new LangWatch();
>
> // Load your dataset
> const dataset = await langwatch.datasets.get("my-dataset");
>
> // Initialize experiment
> const experiment = await langwatch.experiments.init("ci-quality-check");
>
> // Run through each test case
> await experiment.run(
>   dataset.entries.map(e => e.entry),
>   async ({ item, index }) => {
>     // Call your LLM/agent
>     const response = await myLLM(item.input);
>
>     // Run evaluators
>     await experiment.evaluate("ragas/faithfulness", {
>       index,
>       data: {
>         input: item.input,
>         output: response,
>         contexts: item.contexts,
>       },
>     });
>   },
>   { concurrency: 4 }
> );
>
> // Print summary and exit with code 1 on failure
> experiment.printSummary();
> ```

### GitHub Actions Example

```yaml theme={null}
name: LLM Quality Gate

on:
  pull_request:
    branches: [main]

jobs:
  evaluate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"

      - name: Install dependencies
        run: |
          pip install langwatch openai  # Add your LLM SDK

      - name: Run experiment
        env:
          LANGWATCH_API_KEY: ${{ secrets.LANGWATCH_API_KEY }}
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: python scripts/run_evaluation.py
```

Where `scripts/run_evaluation.py` contains your full experiment code.

### Comparing Multiple Configurations

SDK experiments can compare different configurations in the same run:

```python theme={null}
import langwatch

dataset = langwatch.dataset.get_dataset("qa-dataset").to_pandas()
experiment = langwatch.experiment.init("model-comparison-ci")

for idx, row in experiment.loop(dataset.iterrows()):
    def compare(idx, row):
        # Test the OpenAI model
        with experiment.target("gpt-6-luna", {"model": "gpt-6-luna"}):
            response = call_openai("gpt-6-luna", row["input"])
            experiment.log_response(response)
            experiment.evaluate("ragas/faithfulness", index=idx, settings={}, data={
                "input": row["input"],
                "output": response,
                "contexts": row["contexts"],
            })

        # Test the Anthropic model
        with experiment.target("claude-sonnet-5", {"model": "claude-sonnet-5"}):
            response = call_anthropic("claude-sonnet-5", row["input"])
            experiment.log_response(response)
            experiment.evaluate("ragas/faithfulness", index=idx, settings={}, data={
                "input": row["input"],
                "output": response,
                "contexts": row["contexts"],
            })

    experiment.submit(compare, idx, row)

# Print summary and exit with code 1 on failure
experiment.print_summary()
```

***

## Results Summary

Both approaches output a CI-friendly summary:

```text theme={null}
════════════════════════════════════════════════════════════
  EXPERIMENT RESULTS
════════════════════════════════════════════════════════════
  Run ID:     run_abc123
  Status:     COMPLETED
  Duration:   45.2s
────────────────────────────────────────────────────────────
  Passed:     42
  Failed:     3
  Pass Rate:  93.3%
────────────────────────────────────────────────────────────
  TARGETS:
    gpt-6-luna: 20 passed, 2 failed
      Avg latency: 1250ms
      Total cost: $0.0125
    claude-sonnet-5: 22 passed, 1 failed
      Avg latency: 980ms
      Total cost: $0.0098
────────────────────────────────────────────────────────────
  EVALUATORS:
    Faithfulness: 95.0% pass rate
      Avg score: 0.87
────────────────────────────────────────────────────────────
  View details: https://app.langwatch.ai/project/experiments/...
════════════════════════════════════════════════════════════
```

The `print_summary()` method:

* Outputs results in a structured format
* Returns exit code 1 if any evaluations failed (unless `exit_on_failure=False`)
* Provides a link to view detailed results in LangWatch

***

## CI Platform Examples

### GitLab CI

> [tab] Platform Experiment
>
> ```yaml theme={null}
> evaluate:
>   stage: test
>   image: python:3.11
>   script:
>     - pip install langwatch
>     - python -c "
>       import langwatch
>       result = langwatch.experiment.run('my-experiment')
>       result.print_summary()
>       "
>   variables:
>     LANGWATCH_API_KEY: $LANGWATCH_API_KEY
> ```

> [tab] via SDK
>
> ```yaml theme={null}
> evaluate:
>   stage: test
>   image: python:3.11
>   script:
>     - pip install langwatch openai
>     - python scripts/run_evaluation.py
>   variables:
>     LANGWATCH_API_KEY: $LANGWATCH_API_KEY
>     OPENAI_API_KEY: $OPENAI_API_KEY
> ```

### CircleCI

> [tab] Platform Experiment
>
> ```yaml theme={null}
> version: 2.1
>
> jobs:
>   evaluate:
>     docker:
>       - image: python:3.11
>     steps:
>       - checkout
>       - run:
>           name: Run experiment
>           command: |
>             pip install langwatch
>             python -c "
>             import langwatch
>             result = langwatch.experiment.run('my-experiment')
>             result.print_summary()
>             "
> ```

> [tab] via SDK
>
> ```yaml theme={null}
> version: 2.1
>
> jobs:
>   evaluate:
>     docker:
>       - image: python:3.11
>     steps:
>       - checkout
>       - run:
>           name: Install dependencies
>           command: pip install langwatch openai
>       - run:
>           name: Run experiment
>           command: python scripts/run_evaluation.py
> ```

***

## Error Handling

> [tab] Python
>
> ```python theme={null}
> from langwatch.evaluation import (
>     EvaluationNotFoundError,
>     EvaluationTimeoutError,
>     EvaluationRunFailedError,
> )
>
> try:
>     result = langwatch.experiment.run("my-experiment", timeout=300)
>     result.print_summary()
> except EvaluationNotFoundError:
>     print("Experiment not found - check the slug")
>     exit(1)
> except EvaluationTimeoutError as e:
>     print(f"Timeout: only {e.progress}/{e.total} completed")
>     exit(1)
> except EvaluationRunFailedError as e:
>     print(f"Run failed: {e.error_message}")
>     exit(1)
> ```

> [tab] TypeScript
>
> ```typescript theme={null}
> import {
>   ExperimentNotFoundError,
>   EvaluationTimeoutError,
>   EvaluationRunFailedError,
> } from "langwatch";
>
> try {
>   const result = await langwatch.experiments.run("my-experiment", { timeout: 300000 });
>   result.printSummary();
> } catch (error) {
>   if (error instanceof ExperimentNotFoundError) {
>     console.error("Experiment not found - check the slug");
>   } else if (error instanceof EvaluationTimeoutError) {
>     console.error(`Timeout: only ${error.progress}/${error.total} completed`);
>   } else if (error instanceof EvaluationRunFailedError) {
>     console.error(`Run failed: ${error.errorMessage}`);
>   }
>   process.exit(1);
> }
> ```

***

## REST API (Platform Experiments)

For custom integrations, you can use the REST API directly. The paths below are also served under `/api/evaluations/v3/`, the prefix earlier SDK versions call.

### Start a Run

```bash theme={null}
curl -X POST "https://app.langwatch.ai/api/experiments/{slug}/run" \
  -H "X-Auth-Token: ${LANGWATCH_API_KEY}"
```

Response:

```json theme={null}
{
  "runId": "run_abc123",
  "status": "running",
  "total": 45,
  "runUrl": "https://app.langwatch.ai/..."
}
```

### Poll for Status

```bash theme={null}
curl "https://app.langwatch.ai/api/experiments/runs/{runId}" \
  -H "X-Auth-Token: ${LANGWATCH_API_KEY}"
```

Response (completed):

```json theme={null}
{
  "runId": "run_abc123",
  "status": "completed",
  "progress": 45,
  "total": 45,
  "summary": {
    "totalCells": 45,
    "completedCells": 45,
    "failedCells": 3,
    "duration": 45000
  }
}
```

### Fetch Results

```bash theme={null}
curl "https://app.langwatch.ai/api/experiments/runs/{runId}/results?experimentSlug={slug}" \
  -H "X-Auth-Token: ${LANGWATCH_API_KEY}"
```

Returns the per-row dataset entries (input, output, trace id, duration) and evaluation scores for the run. `experimentSlug` is optional for the first 24 hours after the run and required after that.

***

## Next Steps

> [card] [Experiments via UI](https://langwatch.ai/docs/evaluations/experiments/ui/answer-correctness.md)
>
> Create experiments in the platform UI

> [card] [Experiments via SDK](https://langwatch.ai/docs/evaluations/experiments/sdk.md)
>
> Full guide to SDK experiments

> [card] [Evaluators](https://langwatch.ai/docs/evaluations/evaluators/list.md)
>
> Browse available evaluators

> [card] [Datasets](https://langwatch.ai/docs/datasets/overview.md)
>
> Manage your test datasets


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