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

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Supported providers

ProviderLLM_PROVIDER valueNotes
OpenAIopenaiGPT-4o and other OpenAI models
AnthropicanthropicClaude Opus, Sonnet, and Haiku
Azure OpenAIazure-openaiAzure-hosted OpenAI models
Google GeminigeminiGemini Pro and other Gemini models
Amazon Bedrockamazon-bedrockClaude models via AWS Bedrock

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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).

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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.

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OpenAI

Get your API key at platform.openai.com/api-keys. Global config (~/.vectorlint/config.toml) 
[env]
LLM_PROVIDER = "openai"
OPENAI_API_KEY = "sk-..."
Project .env file 
LLM_PROVIDER=openai
OPENAI_API_KEY=sk-...
Shell / CI environment variables 
export LLM_PROVIDER=openai
export OPENAI_API_KEY=sk-...

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Anthropic

Get your API key at console.anthropic.com. Global config (~/.vectorlint/config.toml) 
[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 
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 
export LLM_PROVIDER=anthropic
export ANTHROPIC_API_KEY=sk-ant-...

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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 under your Azure OpenAI resource. Global config (~/.vectorlint/config.toml) 
[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 
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 
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

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Google Gemini

Get your API key at aistudio.google.com/app/apikey. Global config (~/.vectorlint/config.toml) 
[env]
LLM_PROVIDER = "gemini"
GEMINI_API_KEY = "..."
Project .env file 
LLM_PROVIDER=gemini
GEMINI_API_KEY=...
Shell / CI environment variables 
export LLM_PROVIDER=gemini
export GEMINI_API_KEY=...

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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) 
[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 
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 
export LLM_PROVIDER=amazon-bedrock
export AWS_REGION=us-east-1
export AWS_ACCESS_KEY_ID=...
export AWS_SECRET_ACCESS_KEY=...
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.

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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 for this purpose. Global config (~/.vectorlint/config.toml) 
[env]
SEARCH_PROVIDER = "perplexity"
PERPLEXITY_API_KEY = "pplx-..."
Project .env file 
SEARCH_PROVIDER=perplexity
PERPLEXITY_API_KEY=pplx-...
You don’t need a search provider for standard base evaluator rules.

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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) 
[env]
INPUT_PRICE_PER_MILLION = "2.50"
OUTPUT_PRICE_PER_MILLION = "10.00"
Project .env file 
INPUT_PRICE_PER_MILLION=2.50
OUTPUT_PRICE_PER_MILLION=10.00

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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. 
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.

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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.

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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.