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

# Configuring LLM providers

> Connect VectorLint to a supported LLM provider.

VectorLint sends your content to an LLM (Large Language Model) for review. Configure a provider and supply credentials before running your first review.

## Supported providers

| Provider       | `LLM_PROVIDER` value | Notes                              |
| -------------- | -------------------- | ---------------------------------- |
| OpenAI         | `openai`             | GPT-4o and other OpenAI models     |
| Anthropic      | `anthropic`          | Claude Opus, Sonnet, and Haiku     |
| Azure OpenAI   | `azure-openai`       | Azure-hosted OpenAI models         |
| Google Gemini  | `gemini`             | Gemini Pro and other Gemini models |
| Amazon Bedrock | `amazon-bedrock`     | Claude models via AWS Bedrock      |

## How VectorLint loads credentials

VectorLint resolves credentials in this order. Later sources override earlier ones:

1. Built-in defaults
2. Global config: `~/.vectorlint/config.toml`
3. Local `.env` file in your project root
4. Shell environment variables

This means you can set a global default provider in `config.toml` and override it per project with a `.env` file, or override both with environment variables in Continuous Integration/Continuous Deployment (CI/CD).

## Generate a configuration file

Run `vectorlint init` to generate `~/.vectorlint/config.toml` with placeholder values for all supported providers. Fill in the key for the provider you want to use and leave the others blank.

## OpenAI

Get your API key at [platform.openai.com/api-keys](https://platform.openai.com/api-keys).

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
LLM_PROVIDER = "openai"
OPENAI_API_KEY = "sk-..."
```

**Project `.env` file**

```bash theme={null}
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...
```

**Shell / CI environment variables**

```bash theme={null}
export LLM_PROVIDER=openai
export OPENAI_API_KEY=sk-...
```

## Anthropic

Get your API key at [console.anthropic.com](https://console.anthropic.com/).

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
LLM_PROVIDER = "anthropic"
ANTHROPIC_API_KEY = "sk-ant-..."
# Optional
ANTHROPIC_MODEL = "claude-haiku-4-5"
ANTHROPIC_MAX_TOKENS = "4096"
ANTHROPIC_TEMPERATURE = "0.2"
```

**Project `.env` file**

```bash theme={null}
LLM_PROVIDER=anthropic
ANTHROPIC_API_KEY=sk-ant-...
# Optional
ANTHROPIC_MODEL=claude-haiku-4-5
ANTHROPIC_MAX_TOKENS=4096
ANTHROPIC_TEMPERATURE=0.2
```

**Shell / CI environment variables**

```bash theme={null}
export LLM_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...
```

## Azure OpenAI

In addition to the API key, Azure OpenAI requires your resource endpoint, deployment name, and API version. Find these in the [Azure portal](https://azure.microsoft.com/en-us/products/ai-services/openai-service) under your Azure OpenAI resource.

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
LLM_PROVIDER = "azure-openai"
AZURE_OPENAI_API_KEY = "..."
AZURE_OPENAI_ENDPOINT = "https://your-resource.openai.azure.com"
AZURE_OPENAI_DEPLOYMENT_NAME = "your-deployment-name"
AZURE_OPENAI_API_VERSION = "2024-02-15-preview"
# Optional
AZURE_OPENAI_TEMPERATURE = "0.2"
```

**Project `.env` file**

```bash theme={null}
LLM_PROVIDER=azure-openai
AZURE_OPENAI_API_KEY=...
AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
AZURE_OPENAI_DEPLOYMENT_NAME=your-deployment-name
AZURE_OPENAI_API_VERSION=2024-02-15-preview
# Optional
AZURE_OPENAI_TEMPERATURE=0.2
```

**Shell / CI environment variables**

```bash theme={null}
export LLM_PROVIDER=azure-openai
export AZURE_OPENAI_API_KEY=...
export AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com
export AZURE_OPENAI_DEPLOYMENT_NAME=your-deployment-name
export AZURE_OPENAI_API_VERSION=2024-02-15-preview
```

## Google Gemini

Get your API key at [aistudio.google.com/app/apikey](https://aistudio.google.com/app/apikey).

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
LLM_PROVIDER = "gemini"
GEMINI_API_KEY = "..."
```

**Project `.env` file**

```bash theme={null}
LLM_PROVIDER=gemini
GEMINI_API_KEY=...
```

**Shell / CI environment variables**

```bash theme={null}
export LLM_PROVIDER=gemini
export GEMINI_API_KEY=...
```

## Amazon Bedrock

VectorLint accesses Claude models through Amazon Bedrock. You can omit AWS credentials if your environment has an Identity and Access Management (IAM) role or if you've configured a credential profile in `~/.aws/credentials`.

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
LLM_PROVIDER = "amazon-bedrock"
AWS_REGION = "us-east-1"
# Optional if using IAM roles or ~/.aws/credentials
AWS_ACCESS_KEY_ID = "..."
AWS_SECRET_ACCESS_KEY = "..."
# Optional
BEDROCK_MODEL = "global.anthropic.claude-sonnet-4-5-20250929-v1:0"
BEDROCK_TEMPERATURE = "0.2"
```

**Project `.env` file**

```bash theme={null}
LLM_PROVIDER=amazon-bedrock
AWS_REGION=us-east-1
# Optional if using IAM roles or ~/.aws/credentials
AWS_ACCESS_KEY_ID=...
AWS_SECRET_ACCESS_KEY=...
# Optional
BEDROCK_MODEL=global.anthropic.claude-sonnet-4-5-20250929-v1:0
BEDROCK_TEMPERATURE=0.2
```

**Shell / Continuous Integration (CI) environment variables**

```bash theme={null}
export LLM_PROVIDER=amazon-bedrock
export AWS_REGION=us-east-1
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
```

<Note>
  If your environment already provides AWS credentials through an IAM role or `~/.aws/credentials`, you can omit AWS\_ACCESS\_KEY\_ID and AWS\_SECRET\_ACCESS\_KEY.
</Note>

## Search provider (optional)

The `technical-accuracy` evaluator verifies factual claims against live web search. You need a separate search provider credential. VectorLint currently supports [Perplexity](https://www.perplexity.ai/) for this purpose.

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
SEARCH_PROVIDER = "perplexity"
PERPLEXITY_API_KEY = "pplx-..."
```

**Project `.env` file**

```bash theme={null}
SEARCH_PROVIDER=perplexity
PERPLEXITY_API_KEY=pplx-...
```

You don't need a search provider for standard `base` evaluator rules.

## Tracking LLM costs

You can configure per-token pricing so VectorLint calculates and reports estimated costs after each run. Check your provider's pricing page for current values.

**Global config** (`~/.vectorlint/config.toml`)

```toml theme={null}
[env]
INPUT_PRICE_PER_MILLION = "2.50"
OUTPUT_PRICE_PER_MILLION = "10.00"
```

**Project `.env` file**

```bash theme={null}
INPUT_PRICE_PER_MILLION=2.50
OUTPUT_PRICE_PER_MILLION=10.00
```

## Using VectorLint in CI/CD

In GitHub Actions, store credentials as repository secrets and pass them as environment variables. Never hard-code API keys in workflow files.

```yaml theme={null}
name: Lint content

on: [push, pull_request]

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

      - name: Run VectorLint
        env:
          LLM_PROVIDER: openai
          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
        run: npx vectorlint content/**/*.md
```

For other CI/CD systems, use the equivalent secret mechanism: GitLab CI variables, CircleCI contexts, or Jenkins credentials.

## Security practices

**Keep credentials out of version control.** Add `.env` and any local config files to `.gitignore`:

```
.env
.vectorlint.ini
```

**Use separate keys per project or team.** Project-scoped keys let you rotate more easily if compromised and give you granular cost tracking.

**Rotate keys periodically.** Generate a new key in the provider dashboard, update your configuration, verify VectorLint still works, then delete the old key.

## Troubleshooting

**`Authentication failed` or `Invalid API key`:** Verify the key value is correct, confirm it hasn't expired, and check that your account has available credits.

**`Unknown provider: xyz`:** Check that `LLM_PROVIDER` matches one of the supported values exactly: `openai`, `anthropic`, `azure-openai`, `gemini`, `amazon-bedrock`. The value is case-sensitive.

**Configuration not loading:** Verify the global config is at `~/.vectorlint/config.toml` and the file is readable. Confirm the TOML syntax is valid. As a quick test, try passing the credentials as environment variables directly.

**`Rate limit exceeded`:** Reduce the `Concurrency` setting in the `.vectorlint.ini` file in your project, upgrade your API tier with the provider, or switch to a provider with higher rate limits.