docs: add documentation to main repository
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docs/llm-providers/anthropic.mdx
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docs/llm-providers/anthropic.mdx
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---
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title: "Anthropic"
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description: "Configure Strix with Claude models"
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---
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## Setup
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```bash
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export STRIX_LLM="anthropic/claude-sonnet-4-5"
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export LLM_API_KEY="sk-ant-..."
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```
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## Available Models
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| Model | Description |
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|-------|-------------|
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| `anthropic/claude-sonnet-4-5` | Best balance of intelligence and speed (recommended) |
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| `anthropic/claude-opus-4-5` | Maximum capability for deep analysis |
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## Get API Key
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1. Go to [console.anthropic.com](https://console.anthropic.com)
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2. Navigate to API Keys
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3. Create a new key
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37
docs/llm-providers/azure.mdx
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docs/llm-providers/azure.mdx
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---
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title: "Azure OpenAI"
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description: "Configure Strix with OpenAI models via Azure"
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---
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## Setup
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```bash
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export STRIX_LLM="azure/your-gpt5-deployment"
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export AZURE_API_KEY="your-azure-api-key"
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export AZURE_API_BASE="https://your-resource.openai.azure.com"
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export AZURE_API_VERSION="2025-11-01-preview"
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```
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## Configuration
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| Variable | Description |
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|----------|-------------|
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| `STRIX_LLM` | `azure/<your-deployment-name>` |
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| `AZURE_API_KEY` | Your Azure OpenAI API key |
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| `AZURE_API_BASE` | Your Azure OpenAI endpoint URL |
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| `AZURE_API_VERSION` | API version (e.g., `2025-11-01-preview`) |
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## Example
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```bash
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export STRIX_LLM="azure/gpt-5-deployment"
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export AZURE_API_KEY="abc123..."
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export AZURE_API_BASE="https://mycompany.openai.azure.com"
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export AZURE_API_VERSION="2025-11-01-preview"
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```
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## Prerequisites
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1. Create an Azure OpenAI resource
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2. Deploy a model (e.g., GPT-5)
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3. Get the endpoint URL and API key from the Azure portal
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47
docs/llm-providers/bedrock.mdx
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docs/llm-providers/bedrock.mdx
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---
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title: "AWS Bedrock"
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description: "Configure Strix with models via AWS Bedrock"
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---
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## Setup
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```bash
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export STRIX_LLM="bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0"
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```
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No API key required—uses AWS credentials from environment.
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## Authentication
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### Option 1: AWS CLI Profile
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```bash
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export AWS_PROFILE="your-profile"
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export AWS_REGION="us-east-1"
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```
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### Option 2: Access Keys
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```bash
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export AWS_ACCESS_KEY_ID="AKIA..."
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export AWS_SECRET_ACCESS_KEY="..."
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export AWS_REGION="us-east-1"
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```
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### Option 3: IAM Role (EC2/ECS)
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Automatically uses instance role credentials.
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## Available Models
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| Model | Description |
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|-------|-------------|
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| `bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0` | Claude 4.5 Sonnet |
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| `bedrock/anthropic.claude-4-5-opus-20251022-v1:0` | Claude 4.5 Opus |
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| `bedrock/anthropic.claude-4-5-haiku-20251022-v1:0` | Claude 4.5 Haiku |
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| `bedrock/amazon.titan-text-premier-v2:0` | Amazon Titan Premier v2 |
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## Prerequisites
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1. Enable model access in the AWS Bedrock console
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2. Ensure your IAM role/user has `bedrock:InvokeModel` permission
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56
docs/llm-providers/local.mdx
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docs/llm-providers/local.mdx
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---
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title: "Local Models"
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description: "Run Strix with self-hosted LLMs for privacy and air-gapped testing"
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---
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Running Strix with local models allows for completely offline, privacy-first security assessments. Data never leaves your machine, making this ideal for sensitive internal networks or air-gapped environments.
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## Privacy vs Performance
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| Feature | Local Models | Cloud Models (GPT-5/Claude 4.5) |
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|---------|--------------|--------------------------------|
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| **Privacy** | 🔒 Data stays local | Data sent to provider |
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| **Cost** | Free (hardware only) | Pay-per-token |
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| **Reasoning** | Lower (struggles with agents) | State-of-the-art |
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| **Setup** | Complex (GPU required) | Instant |
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<Warning>
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**Compatibility Note**: Strix relies on advanced agentic capabilities (tool use, multi-step planning, self-correction). Most local models, especially those under 70B parameters, struggle with these complex tasks.
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For critical assessments, we strongly recommend using state-of-the-art cloud models like **Claude 4.5 Sonnet** or **GPT-5**. Use local models only when privacy is the absolute priority.
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</Warning>
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## Ollama
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[Ollama](https://ollama.ai) is the easiest way to run local models on macOS, Linux, and Windows.
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### Setup
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1. Install Ollama from [ollama.ai](https://ollama.ai)
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2. Pull a high-performance model:
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```bash
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ollama pull qwen3-vl
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```
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3. Configure Strix:
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```bash
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export STRIX_LLM="ollama/qwen3-vl"
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export LLM_API_BASE="http://localhost:11434"
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```
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### Recommended Models
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We recommend these models for the best balance of reasoning and tool use:
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**Recommended models:**
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- **Qwen3 VL** (`ollama pull qwen3-vl`)
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- **DeepSeek V3.1** (`ollama pull deepseek-v3.1`)
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- **Devstral 2** (`ollama pull devstral-2`)
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## LM Studio / OpenAI Compatible
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If you use LM Studio, vLLM, or other runners:
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```bash
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export STRIX_LLM="openai/local-model"
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export LLM_API_BASE="http://localhost:1234/v1" # Adjust port as needed
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```
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31
docs/llm-providers/openai.mdx
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docs/llm-providers/openai.mdx
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---
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title: "OpenAI"
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description: "Configure Strix with OpenAI models"
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---
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## Setup
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```bash
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export STRIX_LLM="openai/gpt-5"
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export LLM_API_KEY="sk-..."
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```
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## Available Models
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See [OpenAI Models Documentation](https://platform.openai.com/docs/models) for the full list of available models.
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## Get API Key
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1. Go to [platform.openai.com](https://platform.openai.com)
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2. Navigate to API Keys
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3. Create a new secret key
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## Custom Base URL
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For OpenAI-compatible APIs:
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```bash
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export STRIX_LLM="openai/gpt-5"
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export LLM_API_KEY="your-key"
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export LLM_API_BASE="https://your-proxy.com/v1"
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```
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37
docs/llm-providers/openrouter.mdx
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docs/llm-providers/openrouter.mdx
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---
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title: "OpenRouter"
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description: "Configure Strix with models via OpenRouter"
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---
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[OpenRouter](https://openrouter.ai) provides access to 100+ models from multiple providers through a single API.
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## Setup
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```bash
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export STRIX_LLM="openrouter/openai/gpt-5"
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export LLM_API_KEY="sk-or-..."
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```
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## Available Models
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Access any model on OpenRouter using the format `openrouter/<provider>/<model>`:
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| Model | Configuration |
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|-------|---------------|
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| GPT-5 | `openrouter/openai/gpt-5` |
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| Claude 4.5 Sonnet | `openrouter/anthropic/claude-sonnet-4.5` |
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| Gemini 3 Pro | `openrouter/google/gemini-3-pro-preview` |
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| GLM-4.7 | `openrouter/z-ai/glm-4.7` |
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## Get API Key
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1. Go to [openrouter.ai](https://openrouter.ai)
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2. Sign in and navigate to Keys
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3. Create a new API key
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## Benefits
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- **Single API** — Access models from OpenAI, Anthropic, Google, Meta, and more
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- **Fallback routing** — Automatic failover between providers
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- **Cost tracking** — Monitor usage across all models
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- **Higher rate limits** — OpenRouter handles provider limits for you
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61
docs/llm-providers/overview.mdx
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docs/llm-providers/overview.mdx
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---
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title: "Overview"
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description: "Configure your AI model for Strix"
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---
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Strix uses [LiteLLM](https://docs.litellm.ai/docs/providers) for model compatibility, supporting 100+ LLM providers.
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## Recommended Models
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For best results, use one of these models:
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| Model | Provider | Configuration |
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| ----------------- | ------------- | -------------------------------- |
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| GPT-5 | OpenAI | `openai/gpt-5` |
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| Claude 4.5 Sonnet | Anthropic | `anthropic/claude-sonnet-4-5` |
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| Gemini 3 Pro | Google Vertex | `vertex_ai/gemini-3-pro-preview` |
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## Quick Setup
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```bash
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export STRIX_LLM="openai/gpt-5"
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export LLM_API_KEY="your-api-key"
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```
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## Provider Guides
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<CardGroup cols={2}>
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<Card title="OpenAI" href="/llm-providers/openai">
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GPT-5 and Codex models.
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</Card>
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<Card title="Anthropic" href="/llm-providers/anthropic">
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Claude 4.5 Sonnet, Opus, and Haiku.
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</Card>
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<Card title="OpenRouter" href="/llm-providers/openrouter">
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Access 100+ models through a single API.
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</Card>
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<Card title="Google Vertex AI" href="/llm-providers/vertex">
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Gemini 3 models via Google Cloud.
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</Card>
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<Card title="AWS Bedrock" href="/llm-providers/bedrock">
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Claude 4.5 and Titan models via AWS.
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</Card>
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<Card title="Azure OpenAI" href="/llm-providers/azure">
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GPT-5 via Azure.
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</Card>
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<Card title="Local Models" href="/llm-providers/local">
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Llama 4, Mistral, and self-hosted models.
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</Card>
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</CardGroup>
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## Model Format
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Use LiteLLM's `provider/model-name` format:
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```
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openai/gpt-5
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anthropic/claude-sonnet-4-5
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vertex_ai/gemini-3-pro-preview
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bedrock/anthropic.claude-4-5-sonnet-20251022-v1:0
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ollama/llama4
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```
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53
docs/llm-providers/vertex.mdx
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docs/llm-providers/vertex.mdx
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---
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title: "Google Vertex AI"
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description: "Configure Strix with Gemini models via Google Cloud"
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---
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## Installation
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Vertex AI requires the Google Cloud dependency. Install Strix with the vertex extra:
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```bash
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pipx install "strix-agent[vertex]"
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```
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## Setup
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```bash
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export STRIX_LLM="vertex_ai/gemini-3-pro-preview"
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```
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No API key required—uses Google Cloud Application Default Credentials.
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## Authentication
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### Option 1: gcloud CLI
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```bash
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gcloud auth application-default login
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```
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### Option 2: Service Account
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```bash
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export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"
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```
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## Available Models
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| Model | Description |
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|-------|-------------|
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| `vertex_ai/gemini-3-pro-preview` | Best overall performance for security testing |
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| `vertex_ai/gemini-3-flash-preview` | Faster and cheaper |
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## Project Configuration
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```bash
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export VERTEXAI_PROJECT="your-project-id"
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export VERTEXAI_LOCATION="us-central1"
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```
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## Prerequisites
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1. Enable the Vertex AI API in your Google Cloud project
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2. Ensure your account has the `Vertex AI User` role
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