> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/usestrix/strix/llms.txt
> Use this file to discover all available pages before exploring further.

# LLM Providers

> Configure Strix to work with different LLM providers including OpenAI, Anthropic, Google, and local models

Strix uses [LiteLLM](https://github.com/BerriAI/litellm) to support a wide range of LLM providers. You can use cloud-based models, local models, or the Strix Router for unified access.

## Recommended Models

For best results, we recommend using one of these frontier models:

* **OpenAI GPT-5** - `openai/gpt-5`
* **Anthropic Claude Sonnet 4.6** - `anthropic/claude-sonnet-4-6`
* **Google Gemini 3 Pro Preview** - `vertex_ai/gemini-3-pro-preview`

## Strix Router

[Strix Router](https://models.strix.ai) provides a single API key for accessing multiple LLM providers with intelligent routing and \$10 free credit on signup.

```bash theme={null}
export STRIX_LLM="strix/gpt-5"
export LLM_API_KEY="your-strix-router-key"
```

Available models via Strix Router:

* `strix/gpt-5`
* `strix/claude-sonnet-4-6`
* `strix/gemini-3-pro-preview`

<Note>
  When using the `strix/` prefix, the API base URL is automatically set to `https://models.strix.ai/api/v1`. You don't need to configure `LLM_API_BASE`.
</Note>

## Cloud Providers

### OpenAI

```bash theme={null}
export STRIX_LLM="openai/gpt-5"
export LLM_API_KEY="sk-..."
```

Supported models:

* `openai/gpt-5`
* `openai/gpt-4o`
* `openai/o1`
* `openai/o3-mini`

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

### Anthropic

```bash theme={null}
export STRIX_LLM="anthropic/claude-sonnet-4-6"
export LLM_API_KEY="sk-ant-..."
```

Supported models:

* `anthropic/claude-sonnet-4-6`
* `anthropic/claude-opus-4`
* `anthropic/claude-3.5-sonnet`

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

### Google Cloud (Vertex AI)

```bash theme={null}
export STRIX_LLM="vertex_ai/gemini-3-pro-preview"
# Authentication via gcloud CLI or service account
```

Supported models:

* `vertex_ai/gemini-3-pro-preview`
* `vertex_ai/gemini-2.0-flash-exp`
* `vertex_ai/gemini-1.5-pro`

<Note>
  Vertex AI uses Google Cloud authentication. You need to authenticate via `gcloud auth application-default login` or set `GOOGLE_APPLICATION_CREDENTIALS` to your service account JSON file.
</Note>

### AWS Bedrock

```bash theme={null}
export STRIX_LLM="bedrock/anthropic.claude-sonnet-4-6-v1:0"
# Authentication via AWS credentials
```

Supported models:

* `bedrock/anthropic.claude-sonnet-4-6-v1:0`
* `bedrock/anthropic.claude-opus-4-v1:0`
* `bedrock/anthropic.claude-3-5-sonnet-20241022-v2:0`

<Note>
  AWS Bedrock uses AWS credentials. Configure your credentials via AWS CLI (`aws configure`) or environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_REGION`).
</Note>

### Azure OpenAI

```bash theme={null}
export STRIX_LLM="azure/your-deployment-name"
export LLM_API_KEY="your-azure-api-key"
export LLM_API_BASE="https://your-resource.openai.azure.com"
```

You can also use Azure-specific environment variables:

```bash theme={null}
export AZURE_API_KEY="your-azure-api-key"
export AZURE_API_BASE="https://your-resource.openai.azure.com"
export AZURE_API_VERSION="2024-02-15-preview"
```

## Local Models

### Ollama

[Ollama](https://ollama.ai/) lets you run LLMs locally on your machine.

```bash theme={null}
export STRIX_LLM="ollama/llama3.1:70b"
export LLM_API_BASE="http://localhost:11434"
```

<Note>
  You don't need to set `LLM_API_KEY` when using Ollama. Make sure Ollama is running (`ollama serve`) before starting Strix.
</Note>

Recommended models for security testing:

* `ollama/llama3.1:70b`
* `ollama/qwen2.5:72b`
* `ollama/deepseek-v3`

### LM Studio

[LM Studio](https://lmstudio.ai/) provides a local server for running LLMs with an OpenAI-compatible API.

```bash theme={null}
export STRIX_LLM="openai/model-name"
export LLM_API_BASE="http://localhost:1234/v1"
```

<Note>
  When using LM Studio, the model name should match the model loaded in LM Studio, or use a generic name like `openai/local-model`.
</Note>

### Other Local Providers

Strix works with any OpenAI-compatible API endpoint:

```bash theme={null}
export STRIX_LLM="openai/your-model"
export LLM_API_BASE="http://your-server:port/v1"
```

## Advanced Configuration

### Custom Timeouts

Adjust LLM request timeouts for slower models or connections:

```bash theme={null}
export LLM_TIMEOUT="600"  # 10 minutes
```

### Retry Configuration

Control how many times Strix retries failed LLM requests:

```bash theme={null}
export STRIX_LLM_MAX_RETRIES="10"
```

### Reasoning Effort

Control the reasoning effort level for better or faster responses:

```bash theme={null}
# For thorough analysis (slower)
export STRIX_REASONING_EFFORT="xhigh"

# For quick scans (faster)
export STRIX_REASONING_EFFORT="medium"

# For minimal overhead
export STRIX_REASONING_EFFORT="low"
```

## Provider-Specific Notes

### Using Multiple Providers

You can use different models for different purposes by switching `STRIX_LLM`:

```bash theme={null}
# Quick scan with smaller model
export STRIX_LLM="openai/gpt-4o"
strix --target ./app --scan-mode quick

# Deep analysis with larger model
export STRIX_LLM="openai/gpt-5"
strix --target ./app --scan-mode deep
```

### Authentication Priority

Strix checks for credentials in this order:

1. `LLM_API_KEY` environment variable
2. Provider-specific environment variables (e.g., `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`)
3. Provider-specific authentication mechanisms (e.g., gcloud, AWS credentials)

### Base URL Priority

Strix checks for base URLs in this order:

1. Automatic detection for `strix/` models
2. `LLM_API_BASE` environment variable
3. `OPENAI_API_BASE` environment variable
4. `LITELLM_BASE_URL` environment variable
5. `OLLAMA_API_BASE` environment variable
6. Provider default URLs

## Troubleshooting

### Connection Failed

If you see "LLM CONNECTION FAILED", verify:

1. Your API key is correct and has the necessary permissions
2. Your model name is correct (e.g., `openai/gpt-5`, not `gpt-5`)
3. Your API base URL is correct (for local models)
4. Your network can reach the API endpoint
5. Your model/deployment exists and is accessible

### Model Not Found

Ensure you're using the correct provider prefix:

* ✅ `openai/gpt-5`
* ❌ `gpt-5`

### Rate Limiting

If you hit rate limits, you can:

1. Reduce `STRIX_REASONING_EFFORT` to make fewer requests
2. Use a different model with higher rate limits
3. Increase your API plan limits

### Local Model Performance

For best results with local models:

1. Use models with at least 70B parameters
2. Ensure you have sufficient RAM/VRAM
3. Use GPU acceleration when possible
4. Consider using `--scan-mode quick` for faster scans

## See Also

* [Environment Variables](/configuration/environment-variables)
* [Config File](/configuration/config-file)
* [LiteLLM Providers Documentation](https://docs.litellm.ai/docs/providers)