Provider Model

Overview

nxusKit presents LLMs, local inference servers, rule engines, solvers, and test adapters through one provider interface. Your application chooses a provider_type, sends a ChatRequest, and receives a structured response. The same request/response pattern works whether the backend is OpenAI, Anthropic, Ollama, CLIPS, Z3, Mock, or Loopback.

The goal is portability: application code can switch providers without changing its surrounding orchestration, logging, retry, or licensing logic.

For Python users, Provider is the factory that creates configured provider instances. LLMProvider is the protocol/type contract implemented by provider classes. Treat them as separate concepts rather than aliases.

Provider Types

Category	Provider types	Use when
Cloud LLMs	openai, claude, groq, mistral, fireworks, together, openrouter, perplexity	You need hosted models, managed scaling, or provider-specific model quality.
Local LLMs	ollama, lmstudio, local	You need local inference, data locality, lower latency, or offline development.
Reasoning engines	clips, z3	You need deterministic rule evaluation, constraint solving, or explainable decisions.
Utility providers	mock, loopback, mcp	You need tests, integration checks, or model-context tooling.

Configuration Shape

All providers use the same high-level configuration fields:

{
  "provider_type": "openai",
  "model": "gpt-4o",
  "api_key": "sk-...",
  "base_url": "https://api.openai.com/v1",
  "timeout_ms": 30000,
  "options": {}
}

Only provider_type is always required. API keys can come from explicit configuration, environment variables, or the credential store. See Authentication for the precedence rules.

Request Flow

1. Create a provider from configuration.
2. Build a chat request with a model name and messages.
3. Call chat() for a complete response or chat_stream() for incremental chunks.
4. Read response content and provider metadata.

use nxuskit::{ChatRequest, Message, NxuskitProvider, ProviderConfig};

let provider = NxuskitProvider::new(ProviderConfig {
    provider_type: "openai".into(),
    ..Default::default()
})?;

let request = ChatRequest::new("gpt-4o")
    .with_message(Message::user("Summarize the provider model."));

let response = provider.chat(request)?;
println!("{}", response.content);

Capabilities

Providers advertise capabilities such as streaming, vision, JSON mode, tool calling, deterministic seeds, and local model discovery. Use the capability metadata to choose a provider at runtime instead of hard-coding assumptions.

For provider-specific details, see:

• Cloud LLM Providers
• Local LLM Providers
• Expert System & Utility Providers
• Z3 Constraint Satisfaction Provider

Choosing a Provider

Need	Start with
General hosted chat, tools, and JSON output	Cloud LLM provider
Local development without external API calls	Ollama or LM Studio
Embedded local inference with direct model lifecycle control	In-process local provider
Deterministic business rules	CLIPS
Constraint solving and optimization	Z3
Unit tests with predictable responses	Mock or Loopback