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Documentation Index

Fetch the complete documentation index at: https://docs.strix.ai/llms.txt

Use this file to discover all available pages before exploring further.

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

FeatureLocal ModelsCloud Models (GPT-5/Claude 4.5)
Privacy🔒 Data stays localData sent to provider
CostFree (hardware only)Pay-per-token
ReasoningLower (struggles with agents)State-of-the-art
SetupComplex (GPU required)Instant
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.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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Ollama

Ollama is the easiest way to run local models on macOS, Linux, and Windows.

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Setup

  1. Install Ollama from ollama.ai
  2. Pull a high-performance model: 
    ollama pull qwen3-vl
  3. Configure Strix: 
    export STRIX_LLM="ollama/qwen3-vl"
export LLM_API_BASE="http://localhost:11434"
We recommend these models for the best balance of reasoning and tool use: Recommended models:
  • Qwen3 VL (ollama pull qwen3-vl)
  • DeepSeek V3.1 (ollama pull deepseek-v3.1)
  • Devstral 2 (ollama pull devstral-2)

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LM Studio / OpenAI Compatible

If you use LM Studio, vLLM, or other runners: 
export STRIX_LLM="openai/local-model"
export LLM_API_BASE="http://localhost:1234/v1"  # Adjust port as needed