Custom models

neuro-ts defaults to gpt-4o because it is cheap and fast enough for one-shot built-in simulation. You can change this globally or per call.

Change the default model

import { configureClient } from 'neuro-ts';

configureClient({
  apiKey: process.env.OPENAI_API_KEY,
  model: 'gpt-4o',
  temperature: 0.1,
  maxTokens: 2048,
});

Per-call override (low level)

The generated wrappers do not yet accept a per-call model parameter (kept intentionally tight to mirror the original signatures). To override per call, drop down to the client:

import { getClient } from 'neuro-ts';

const result = await getClient().executeFunction({
  functionId: 'Array.prototype.map',
  prompt: 'classify each item as fruit or vegetable',
  instance: ['apple', 'carrot', 'banana'],
  args: { callbackfn: undefined },
  signatureHint: [],
  systemPrompt:
    'You are simulating Array.prototype.map. Output a JSON array of the same length.',
  model: 'gpt-4o',
});

OpenAI-compatible endpoints

Anything OpenAI-Chat-Completions-compatible works (vLLM, Ollama with the OpenAI shim, Azure OpenAI, OpenRouter, Together). Pass baseURL:

configureClient({
  apiKey: process.env.OPENROUTER_API_KEY,
  baseURL: 'https://openrouter.ai/api/v1',
  model: 'meta-llama/llama-3.3-70b-instruct',
});

configureClient({
  apiKey: process.env.OLLAMA_KEY ?? 'ollama',
  baseURL: 'http://localhost:11434/v1',
  model: 'llama3.3',
});

Smaller or weaker models will struggle to follow the JSON-only output contract embedded in every system prompt. If you see prose coming back, raise maxTokens, lower temperature, or use a stronger model.