# Configure a model provider

> This guide helps you connect Harn to a model provider. It covers the checks you can run before you put a provider in a program.

Website: https://harnlang.com/provider-setup.html

This page documents Harn, which is pre-1.0. Language, standard library, and CLI APIs may change. If the intended version is unclear, clarify before using this page.

---

This guide helps you connect Harn to a model provider. It covers the checks
you can run before you put a provider in a program.

## 1. Check the local installation

Run the doctor command first:

```bash
harn doctor
harn doctor --check-providers
```

The first command reports the local Harn setup. The second also checks the
configured provider paths. It does not print secret values.

## 2. Find a provider and model

List the models that Harn knows about:

```bash
harn models list
harn models list --provider anthropic
```

Inspect one model before you use it:

```bash
harn models info claude-sonnet-5
harn provider dispatch-explain anthropic claude-sonnet-5
```

The catalog is the source of truth for current aliases and capabilities. Do
not copy an old model name from an example when the catalog gives you a newer
one.

## 3. Set the provider credential

Each provider reads its key from an environment variable. Set the one for the
provider you picked:

```bash
export ANTHROPIC_API_KEY="sk-ant-..."
```

Harn names these providers first, because most people already have an account
with one of them:

| Provider | Environment variable |
|---|---|
| Anthropic | `ANTHROPIC_API_KEY` |
| OpenAI | `OPENAI_API_KEY` |
| Google Gemini | `GEMINI_API_KEY` |
| OpenRouter | `OPENROUTER_API_KEY` |
| Groq | `GROQ_API_KEY` |
| DeepSeek | `DEEPSEEK_API_KEY` |
| Ollama | none — runs locally without a key |

Harn supports dozens more, and some accept more than one variable. For every
provider and the variables it reads, see
[credential variables](./provider-support.md#credential-variables); for the
endpoint and header details behind them, see the
[provider reference](./llm/providers.md#provider-api-details). To see which
variables are already set on this machine, run `harn doctor`.

### Keep the key out of your shell

A variable can hold a secret reference instead of the key itself:

```bash
export ANTHROPIC_API_KEY="harn-secret://work/anthropic"
```

Harn resolves the reference when it makes the call, so the key never lands in
your shell history or a config file. See
[secrets](./orchestrator/secrets.md) for how to store one.

After you set the variable, confirm the provider resolves:

```bash
harn doctor --check-providers
```

That command reports which providers have a working credential path. It never
prints a secret value.

## 4. Test the connection

Use the model test command for a small smoke test:

```bash
harn models test claude-sonnet-5 --provider anthropic
```

The command sends a test request. It can use provider credits. Use a model
that is available to your account.

## 5. Use the provider in a program

Keep the provider and model in the call options:

```harn
fn main(harness: Harness) {
  const response = harness.llm.call(
    "Reply with one short greeting.",
    nil,
    { provider: "anthropic", model: "claude-sonnet-5", max_tokens: 64 }
  )
  harness.stdio.println(response.text)
}
```

For a single project, put stable defaults in `harn.toml` when the provider
reference says the setting is supported. Keep credentials out of that file.

## Local providers

Start your local server first, then use the provider's name and model from the
catalog:

```bash
harn models list --provider ollama
harn provider ready ollama --model <model>
```

For an OpenAI-compatible server, check the endpoint and model settings in the
[provider reference](./llm/providers.md#local-openai-compatible-server).

## Use the mock provider in tests

The `mock` provider needs no credentials and returns deterministic responses.
Use it for syntax checks, unit tests, and examples. A mock run proves that the
program reaches Harn's model-call boundary; it does not prove that a cloud
provider is configured or that a model produces useful answers.

See [mock LLM responses](./llm/llm_call.md#testing-with-mock-llm-responses) for
queued responses and error cases.

---

## Read next

- [Common tasks](https://harnlang.com/common-tasks.md)
- [Run a workflow bundle from the CLI](https://harnlang.com/workflow-authoring-quickstart.md)