Local Models

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.

Privacy vs Performance

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. </Warning>

Ollama

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

Setup

1. Install Ollama from ollama.ai
2. Pull a high-performance model:
   bashCopy

   ollama pull qwen3-vl
   

3. Configure Strix:
   bashCopy

   export STRIX_LLM="ollama/qwen3-vl"
   export LLM_API_BASE="http://localhost:11434"
   

Recommended Models

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)

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

Gateways that require custom headers

Some OpenAI-compatible gateways require extra HTTP headers (for attribution or tenant routing) alongside the bearer token. Set them with LLM_EXTRA_HEADERS as a JSON object — they are sent on every request:

export STRIX_LLM="openai/your-model"
export LLM_API_BASE="https://your-gateway.example/v1"
export LLM_API_KEY="your-bearer-token"          # sent as Authorization: Bearer ...
export LLM_EXTRA_HEADERS='{"X-Feature-Key":"value","X-Tenant":"acme"}'

For endpoints behind a private CA, point Strix at your certificate bundle with the standard SSL_CERT_FILE=/path/to/ca-bundle.pem — never disable TLS verification against a real endpoint.

Tool calling must return structured tool_calls

Strix is entirely tool-driven: every working turn must be a native function/tool call. If your inference server returns the tool call as plain assistant text instead of a structured tool_calls field, Strix never sees a call it can execute, so the agent makes no real progress — it re-prompts the model for a tool call and gives up once its recovery attempts are exhausted.

This is almost always an inference-server configuration problem, not a model or Strix problem. Common symptoms are the model printing a call as text such as:

<tool_call>{"name": "exec_command", "arguments": {"cmd": "nmap ..."}}</tool_call>
exec_command(cmd="nmap ...", timeout=180)
{"action": "exec_command", "params": {"cmd": "nmap ..."}}

The fix belongs on the inference server: it must be configured to parse the model's tool tokens into structured tool_calls. A correctly configured endpoint either returns a structured call or rejects the request outright — it never leaks the call as text.

Fixes by server

llama.cpp (llama-server)

• Run with --jinja and a correct tool-use chat template (--chat-template / --chat-template-file matching the model). Recent builds enable --jinja by default — upgrade if yours doesn't.
• For thinking models, align or disable reasoning (--reasoning-format, -rea off) so it doesn't break tool-call parsing.
• A low temperature (e.g. --temp 0.2) improves tool-call reliability.

Ollama

• Use a recent Ollama and a model whose template wires tools. Modern Ollama refuses tools (tools param requires --jinja flag) if the template lacks tool support.
• For reasoning models (e.g. qwen3), disable the model's thinking mode — thinking left on frequently pushes the tool call into the text content instead of the structured tool_calls field. Turn it off on the Ollama side (a non-thinking model variant, or think: false in the model's parameters / Modelfile).
• Raise num_ctx to at least 16k–32k. Strix sends a large system prompt plus many tool schemas; at Ollama's small default context the tool definitions are truncated out of the prompt and the model stops emitting valid calls. A short test prompt can look fine while a real scan fails, so set this explicitly rather than inferring it from a quick check.

vLLM

• Start with --enable-auto-tool-choice, a matching --tool-call-parser (hermes, qwen3_xml, or llama3_json), and a matching --reasoning-parser for reasoning models.

A low sampling temperature (roughly 0.2–0.6, depending on the family) also measurably reduces malformed tool calls on open-weight models. Set it on the server or in your model's parameters.