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

# Self-hosted evaluations

If you're running a self-hosted Laminar instance, point the SDK at it by passing `baseUrl`, `httpPort`, and `grpcPort` to `evaluate()`. Evaluations talk to Laminar over two channels: HTTP for evaluation metadata, datapoints, and scores; gRPC for the OpenTelemetry traces.

## Configuration

<Tabs>
  <Tab title="TypeScript">
    ```typescript theme={null}
    import { evaluate } from '@lmnr-ai/lmnr';

    evaluate({
      data: evaluationData,
      executor: runExecutor,
      evaluators: { accuracy },
      config: {
        projectApiKey: process.env.LMNR_PROJECT_API_KEY,
        baseUrl: 'http://localhost',
        httpPort: 8000,
        grpcPort: 8001,
      },
    });
    ```
  </Tab>

  <Tab title="Python">
    ```python theme={null}
    from lmnr import evaluate

    evaluate(
        data=data,
        executor=run_executor,
        evaluators={"accuracy": accuracy},
        project_api_key=os.environ["LMNR_PROJECT_API_KEY"],
        base_url="http://localhost",
        http_port=8000,
        grpc_port=8001,
    )
    ```
  </Tab>
</Tabs>

<Note>
  `baseUrl` / `base_url` is the scheme + host only. Do not include a port in it; the ports go in `httpPort` / `grpcPort`.
</Note>

## `evaluate` parameters

| Parameter                                  | Description                                                                       | Default               |
| ------------------------------------------ | --------------------------------------------------------------------------------- | --------------------- |
| `data`                                     | List of datapoints or a `LaminarDataset` instance.                                | required              |
| `executor`                                 | (Optionally async) function that takes `data` and returns the output to score.    | required              |
| `evaluators`                               | Map of name → scoring function. Each function returns a number or map of numbers. | required              |
| `name`                                     | Evaluation name shown in the UI.                                                  | random                |
| `groupName` / `group_name`                 | Group identifier. Only runs sharing a group name can be compared.                 | `default`             |
| `metadata`                                 | Arbitrary JSON on the evaluation row for filtering.                               | none                  |
| `projectApiKey` / `project_api_key`        | Overrides `LMNR_PROJECT_API_KEY`.                                                 | env                   |
| `baseUrl` / `base_url`                     | Laminar host (no port).                                                           | `https://api.lmnr.ai` |
| `httpPort` / `http_port`                   | HTTP port for evaluation metadata and datapoints.                                 | `443`                 |
| `grpcPort` / `grpc_port`                   | gRPC port for OTel traces.                                                        | `8443`                |
| `concurrencyLimit` / `batch_size`          | Parallel executor invocations.                                                    | `5`                   |
| `instrumentModules` / `instrument_modules` | Client modules to instrument (e.g. `OpenAI`, `Anthropic`).                        | none                  |

See the [SDK reference](/sdk/evaluations) for the full TypeScript and Python signatures.

## Why both HTTP and gRPC

* **HTTP (`httpPort`)** carries the bookkeeping: evaluation name, group, datapoints, scores, executor outputs.
* **gRPC (`grpcPort`)** carries the OpenTelemetry spans: every LLM call, tool call, evaluator, and the wrapping `EVALUATION` / `EXECUTOR` / `EVALUATOR` spans.

Both have to be reachable from the machine running the evaluation. If only HTTP is open, the evaluation run appears with datapoints and scores but no traces.

## Hosting options

For how to actually run a self-hosted Laminar (Docker Compose for single-node, Helm for Kubernetes, and the hybrid data-plane variant) see [Hosting options](/hosting-options).

## Next steps

<CardGroup cols={2}>
  <Card title="Quickstart" href="/evaluations/quickstart" icon="play">
    Write your first evaluation against your self-hosted instance.
  </Card>

  <Card title="Hosting options" href="/hosting-options" icon="server">
    Docker Compose, Helm, and hybrid data-plane deployment options.
  </Card>

  <Card title="Manual API" href="/evaluations/manual-evaluation" icon="wrench">
    Use `LaminarClient.evals` for finer control; the same base-URL + port config applies.
  </Card>

  <Card title="SDK reference" href="/sdk/evaluations" icon="code">
    Full parameters for `evaluate` and related SDK types.
  </Card>
</CardGroup>