---
title: LLM Configuration
subtitle: Connect your preferred language model provider
slug: self-hosted/llm-configuration
---

Skyvern uses LLMs to analyze screenshots and decide what actions to take. You'll need to configure at least one LLM provider before running tasks.

## How Skyvern uses LLMs

Skyvern makes multiple LLM calls per task step:
1. **Screenshot analysis**: Identify interactive elements on the page
2. **Action planning**: Decide what to click, type, or extract
3. **Result extraction**: Parse data from the page into structured output

A task that runs for 10 steps makes roughly 30+ LLM calls. Choose your provider and model tier with this in mind.

For most deployments, configure a single provider using `LLM_KEY`. Skyvern also supports a `SECONDARY_LLM_KEY` for lighter tasks to reduce costs.

---

## OpenAI

The most common choice. Requires an API key from [platform.openai.com](https://platform.openai.com/).

```bash .env
ENABLE_OPENAI=true
OPENAI_API_KEY=sk-...
LLM_KEY=OPENAI_GPT4O
```

### Available models

| LLM_KEY | Model | Notes |
|---------|-------|-------|
| `OPENAI_GPT4O` | gpt-4o | Recommended for most use cases |
| `OPENAI_GPT4O_MINI` | gpt-4o-mini | Cheaper, less capable |
| `OPENAI_GPT4_1` | gpt-4.1 | Latest GPT-4 family |
| `OPENAI_GPT4_1_MINI` | gpt-4.1-mini | Cheaper GPT-4.1 variant |
| `OPENAI_O3` | o3 | Reasoning model |
| `OPENAI_O3_MINI` | o3-mini | Cheaper reasoning model |
| `OPENAI_GPT4_TURBO` | gpt-4-turbo | Previous generation |
| `OPENAI_GPT4V` | gpt-4-turbo | Legacy alias for gpt-4-turbo |

### Optional settings

```bash .env
# Use a custom API endpoint (for proxies or compatible services)
OPENAI_API_BASE=https://your-proxy.com/v1

# Specify organization ID
OPENAI_ORGANIZATION=org-...
```

---

## Anthropic

Claude models from [anthropic.com](https://www.anthropic.com/).

```bash .env
ENABLE_ANTHROPIC=true
ANTHROPIC_API_KEY=sk-ant-...
LLM_KEY=ANTHROPIC_CLAUDE3.5_SONNET
```

### Available models

| LLM_KEY | Model | Notes |
|---------|-------|-------|
| `ANTHROPIC_CLAUDE4.5_SONNET` | claude-4.5-sonnet | Latest Sonnet |
| `ANTHROPIC_CLAUDE4.5_OPUS` | claude-4.5-opus | Most capable |
| `ANTHROPIC_CLAUDE4_SONNET` | claude-4-sonnet | Claude 4 |
| `ANTHROPIC_CLAUDE4_OPUS` | claude-4-opus | Claude 4 Opus |
| `ANTHROPIC_CLAUDE3.7_SONNET` | claude-3-7-sonnet | Previous generation |
| `ANTHROPIC_CLAUDE3.5_SONNET` | claude-3-5-sonnet | Previous generation |
| `ANTHROPIC_CLAUDE3.5_HAIKU` | claude-3-5-haiku | Cheap and fast |

---

## Azure OpenAI

Microsoft-hosted OpenAI models. Requires an Azure subscription with OpenAI service provisioned.

```bash .env
ENABLE_AZURE=true
LLM_KEY=AZURE_OPENAI
AZURE_DEPLOYMENT=your-deployment-name
AZURE_API_KEY=your-azure-api-key
AZURE_API_BASE=https://your-resource.openai.azure.com/
AZURE_API_VERSION=2024-08-01-preview
```

### Setup steps

1. Create an Azure OpenAI resource in the [Azure Portal](https://portal.azure.com)
2. Open the Azure AI Foundry portal from your resource's overview page
3. Go to **Shared Resources** → **Deployments**
4. Click **Deploy Model** → **Deploy Base Model** → select GPT-4o or GPT-4
5. Note the **Deployment Name**. Use this for `AZURE_DEPLOYMENT`
6. Copy your API key and endpoint from the Azure Portal

<Note>
The `AZURE_DEPLOYMENT` is the name you chose when deploying the model, not the model name itself.
</Note>

---

## Google Gemini

Gemini models through [Vertex AI](https://cloud.google.com/vertex-ai). Requires a GCP project with Vertex AI enabled.

```bash .env
ENABLE_VERTEX_AI=true
LLM_KEY=VERTEX_GEMINI_3.0_FLASH
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
GCP_PROJECT_ID=your-gcp-project-id
GCP_REGION=us-central1
```

### Setup steps

1. Create a [GCP project](https://console.cloud.google.com/) with billing enabled
2. Enable the **Vertex AI API** in your project
3. Create a service account with the **Vertex AI User** role
4. Download the service account JSON key file
5. Set `GOOGLE_APPLICATION_CREDENTIALS` to the path of that file

### Available models

| LLM_KEY | Model | Notes |
|---------|-------|-------|
| `VERTEX_GEMINI_3.0_FLASH` | gemini-3-flash-preview | Recommended |
| `VERTEX_GEMINI_2.5_PRO` | gemini-2.5-pro | Stable |
| `VERTEX_GEMINI_2.5_FLASH` | gemini-2.5-flash | Cheaper, faster |

---

## Amazon Bedrock

Run Anthropic Claude through your AWS account.

```bash .env
ENABLE_BEDROCK=true
LLM_KEY=BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET
AWS_REGION=us-west-2
AWS_ACCESS_KEY_ID=AKIA...
AWS_SECRET_ACCESS_KEY=...
```

### Setup steps

1. Create an IAM user with `AmazonBedrockFullAccess` policy
2. Generate access keys for the IAM user
3. In the [Bedrock console](https://console.aws.amazon.com/bedrock/), go to **Model Access**
4. Enable access to Claude 3.5 Sonnet

### Available models

| LLM_KEY | Model |
|---------|-------|
| `BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET` | Claude 3.5 Sonnet v2 |
| `BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET_V1` | Claude 3.5 Sonnet v1 |
| `BEDROCK_ANTHROPIC_CLAUDE3.7_SONNET_INFERENCE_PROFILE` | Claude 3.7 Sonnet (cross-region) |
| `BEDROCK_ANTHROPIC_CLAUDE4_SONNET_INFERENCE_PROFILE` | Claude 4 Sonnet (cross-region) |
| `BEDROCK_ANTHROPIC_CLAUDE4.5_SONNET_INFERENCE_PROFILE` | Claude 4.5 Sonnet (cross-region) |

<Note>
Bedrock inference profile keys (`*_INFERENCE_PROFILE`) use cross-region inference and require `AWS_REGION` only. No access keys needed if running on an IAM-authenticated instance.
</Note>

---

## Ollama (Local Models)

Run open-source models locally with [Ollama](https://ollama.ai/). No API costs, but requires sufficient local compute.

```bash .env
ENABLE_OLLAMA=true
LLM_KEY=OLLAMA
OLLAMA_MODEL=llama3.1
OLLAMA_SERVER_URL=http://host.docker.internal:11434
OLLAMA_SUPPORTS_VISION=false
```

### Setup steps

1. [Install Ollama](https://ollama.ai/download)
2. Pull a model: `ollama pull llama3.1`
3. Start Ollama: `ollama serve`
4. Configure Skyvern to connect

<Warning>
Most Ollama models don't support vision. Set `OLLAMA_SUPPORTS_VISION=false`. Without vision, Skyvern relies on DOM analysis instead of screenshot analysis, which may reduce accuracy on complex pages.
</Warning>

### Docker networking

When running Skyvern in Docker and Ollama on the host:

| Host OS | OLLAMA_SERVER_URL |
|---------|-------------------|
| macOS/Windows | `http://host.docker.internal:11434` |
| Linux | `http://172.17.0.1:11434` (Docker bridge IP) |

---

## OpenAI-Compatible Endpoints

Connect to any service that implements the OpenAI API format, including LiteLLM, LocalAI, vLLM, and text-generation-inference.

```bash .env
ENABLE_OPENAI_COMPATIBLE=true
OPENAI_COMPATIBLE_MODEL_NAME=llama3.1
OPENAI_COMPATIBLE_API_KEY=sk-test
OPENAI_COMPATIBLE_API_BASE=http://localhost:4000/v1
LLM_KEY=OPENAI_COMPATIBLE
```

This is useful for:
- Running local models with a unified API
- Using LiteLLM as a proxy to switch between providers
- Connecting to self-hosted inference servers

---

## OpenRouter

Access multiple models through a single API at [openrouter.ai](https://openrouter.ai/).

```bash .env
ENABLE_OPENROUTER=true
LLM_KEY=OPENROUTER
OPENROUTER_API_KEY=sk-or-...
OPENROUTER_MODEL=mistralai/mistral-small-3.1-24b-instruct
```

---

## Groq

Inference on open-source models at [groq.com](https://groq.com/).

```bash .env
ENABLE_GROQ=true
LLM_KEY=GROQ
GROQ_API_KEY=gsk_...
GROQ_MODEL=llama-3.1-8b-instant
```

<Note>
Groq specializes in fast inference for open-source models. Response times are typically much faster than other providers, but model selection is limited.
</Note>

---

## Using multiple models

### Primary and secondary models

Configure a cheaper model for lightweight operations:

```bash .env
# Main model for complex decisions
LLM_KEY=OPENAI_GPT4O

# Cheaper model for simple tasks like dropdown selection
SECONDARY_LLM_KEY=OPENAI_GPT4O_MINI
```

### Task-specific models

For fine-grained control, you can override models for specific operations:

```bash .env
# Model for data extraction from pages (defaults to LLM_KEY if not set)
EXTRACTION_LLM_KEY=ANTHROPIC_CLAUDE3.5_SONNET

# Model for generating code/scripts in code blocks (defaults to LLM_KEY if not set)
SCRIPT_GENERATION_LLM_KEY=OPENAI_GPT4O
```

Most deployments don't need task-specific models. Start with `LLM_KEY` and `SECONDARY_LLM_KEY`.

---

## Troubleshooting

### "To enable svg shape conversion, please set the Secondary LLM key"

Some operations require a secondary model. Set `SECONDARY_LLM_KEY` in your environment:

```bash .env
SECONDARY_LLM_KEY=OPENAI_GPT4O_MINI
```

### "Context window exceeded"

The page content is too large for the model's context window. Options:
- Use a model with a larger context (GPT-4o supports 128k tokens)
- Simplify your prompt to require less page analysis
- Start from a more specific URL with less content

### "LLM caller not found"

The configured `LLM_KEY` doesn't match any enabled provider. Verify:
1. The provider is enabled (`ENABLE_OPENAI=true`, etc.)
2. The `LLM_KEY` value matches a supported model name exactly
3. Model names are case-sensitive: `OPENAI_GPT4O` not `openai_gpt4o`

### Container logs show authentication errors

Check your API key configuration:
- Ensure the key is set correctly without extra whitespace
- Verify the key hasn't expired or been revoked
- For Azure, ensure `AZURE_API_BASE` includes the full URL with `https://`

### Slow response times

LLM calls typically take 2-10 seconds. Longer times may indicate:
- Network latency to the provider
- Rate limiting (the provider may be throttling requests)
- For Ollama, insufficient local compute resources

---

## Next steps

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