OpenAI SDKs

Use the standard OpenAI client on your server. Select glm-5.3 and set the Internalize base URL, including /v1. An inference-only key is sufficient for these examples and for reading jobs it submitted.

PythonCopy Anchor Link

pip install openai

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://convergingthought.com/v1",
    api_key=os.environ["INTERNALIZE_API_KEY"],
    max_retries=0,
)
response = client.chat.completions.create(
    model="glm-5.3",
    messages=[{"role": "user", "content": "Explain test-time training simply."}],
    max_completion_tokens=2048,
)
print(response.choices[0].message.content)

For learned weights, pass extra_body={"subject_id": "authorized-customer-42"} to create. Pin a retained version with adapter_version in the same object. Omit the version to resolve the active adapter at admission.

TypeScriptCopy Anchor Link

npm install openai

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://convergingthought.com/v1",
  apiKey: process.env.INTERNALIZE_API_KEY,
  maxRetries: 0,
});
const response = await client.chat.completions.create({
  model: "glm-5.3",
  messages: [{ role: "user", content: "Hello!" }],
});
console.log(response.choices[0].message.content);

For a subject scoped to one trusted client, add defaultHeaders: { "X-Internalize-Subject": authorizedSubject } when constructing that client. For a shared client, pass headers in the per-request options. Never mutate shared default headers between concurrent customers.

Multiple customersCopy Anchor Link

Pair X-Internalize-Tenant: authorizedCustomerId with X-Internalize-Subject: memoryName. In TypeScript, pass both in per-request headers; in Python use extra_headers. Body tenant_id and header scope must agree. Repeat the tenant on job reads and learning calls. See Serve multiple customers for a complete flow and restricted keys.

Function callsCopy Anchor Link

Declare function tools with name, description, and parameters. An assistant response can contain tool_calls. Execute authorized functions in your runtime and append one role: "tool" result for each call ID before continuing. Preserve the assistant's calls in history. The next request may end with the completed tool-result block.

tool_choice supports auto, none, required, or a named function. The service validates the resulting choice before delivering it. It does not execute your tools. Strict constrained tool schemas are not supported; omit strict or set it to false, and validate arguments in your own handler.

Stream responsesCopy Anchor Link

Set stream: true and optionally stream_options: { include_usage: true }. Consume the result with the SDK's usual stream iterator. Internalize emits keepalive comments while the durable job runs, then validated content or function-call chunks and [DONE].

This is buffered SSE, not live token generation. Raw reasoning is not returned. completion_tokens includes the reasoning that the model generated and billed, even though only the final answer is visible.

Errors and retriesCopy Anchor Link

The SDK exposes normal HTTP errors for admission failures such as invalid keys, insufficient funds, and invalid input. A non-streaming post-admission failure or timeout returns 409 with error.job_id and error.status_url. A stream already started returns an error object in its data frames with those same recovery fields.

Keep maxRetries: 0 / max_retries=0 until your application persists operation identities. For a deliberate retry, supply the same Idempotency-Key and exact body. A new key means a new potential charge. See Chat Completions and the official request reference.