Configuration

Praxis uses LLMs for several features-semantic operations, tool discovery during recon, traffic summarization. You'll need to configure at least one provider to use these capabilities.

LLM Providers

Open Settings (Ctrl+S) → LLM in the praxis TUI.

Adding a Model

In Settings (Ctrl+S), stay on the LLM tab, use ↑/↓ to select Add Model, and press Enter. In the form:

1. Choose a Provider
2. Enter your API Key (optional for local providers — Ollama and Custom)
3. For Custom, and optionally for Ollama, set a Base URL
4. Refresh the provider model list when supported, or enter a model name manually
5. Save with Ctrl+S

Use Tab / Shift+Tab to move between Settings tabs. The TUI status bar lists the available form controls.

Supported Providers

Anthropic, OpenAI, Google (Gemini), Groq, Cerebras, Mistral, xAI, NVIDIA, MiniMax, Moonshot, Fireworks AI, OpenRouter, Ollama (local), Custom (OpenAI-compatible).

Local Model Providers

Two providers are designed for local or self-hosted inference:

Ollama — defaults to http://localhost:11434/v1, so if you are running a stock Ollama install nothing else is needed. API key is optional. Model discovery uses Ollama's native /api/tags endpoint, so the model-refresh action works even though Ollama is strictly OpenAI-API compatible for inference. Override the base URL on the model definition if Ollama is listening elsewhere.

Custom (OpenAI-Compatible) — for vLLM, llama.cpp, LM Studio, Text-Generation-Inference, or any endpoint that implements /v1/chat/completions. You must set a base URL on the model definition; API key is optional. Model discovery probes /models on the configured base URL.

Feature Assignment

Once you've added models, assign them to features under Feature Selection:

Orchestrator - Powers an interactive AI agent that can autonomously manage operations. It will drive target agents through multi-turn conversations to achieve a given task, so it needs a capable model that follows tool-calling instructions reliably. A companion Max Tokens setting caps how long its responses can run.

Semantic Operations - Used when executing operations through agents. Pick something capable.

Semantic Parser - Used during semantic recon to extract tool definitions from config files. Speed matters here since it runs multiple times; a fast model like Haiku or GPT-4o-mini works well.

Traffic Parser - Summarizes intercepted traffic. Again, speed is valuable; you don't need the most powerful model.

Documentation Helper - Powers the Help Assistant (Ctrl+H), which answers questions about using Praxis from the bundled documentation. Falls back to the Orchestrator model when unset.

Speed vs. Capability

For parser features (Semantic Parser, Traffic Parser), we recommend providers with fast inference:

• Cerebras and Groq have very fast time-to-first-token and overall throughput
• This matters when you're running recon across multiple agents or parsing lots of traffic

For Orchestrator and Semantic Operations, capability matters more than raw speed. Use a model that's good at reasoning and tool use.

Environment Variables

Most configuration is done through the praxis TUI, but some things are set via environment variables:

Service

Node

Database

By default, Praxis uses SQLite stored at ~/.praxis/operations.db. For PostgreSQL and production deployments, see Database Configuration.

Model Reference Format

When specifying models in operations or chains, use the format:

provider::model

For example:

• anthropic::claude-sonnet-4-20250514
• openai::gpt-4o
• groq::llama-3.3-70b-versatile

This lets you override the default model for specific operations that might need more (or less) capability.

Next Steps

With LLMs configured, you're ready to:

• Run through the quick start
• Enable semantic recon for deeper tool discovery
• Execute semantic operations