Make your first call

This walkthrough uses a fictional returns policy so you can inspect the entire request. It requires an execution-enabled project with prepaid credits and a server-side API key. The current hosted preview does not execute these calls; check availability first.

1. Prepare the projectCopy Anchor Link

Open Overview, create your project, and visit API keys. The default Inference key is enough for a first model call. Choose Inference + learning for the optional teach-and-ask walkthrough below. Keep version-management keys separate. Save the key in a server-side secret store; it is shown only once.

When live billing is enabled, purchase at least $10 in Billing. There is no trial balance or free model execution. A preview checkout does not fund a project.

export INTERNALIZE_BASE_URL="https://convergingthought.com"
export INTERNALIZE_API_KEY="YOUR_PROJECT_KEY"

Use the origin above without /v1 at the end. All examples add their own versioned path. The environment variable is a placeholder here; do not commit your real key to source control.

2. Make a normal model callCopy Anchor Link

Install the OpenAI client with pip install openai. No subject or adapter is required:

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

Use Integrations for TypeScript, Vercel AI SDK, LangChain, LangGraph, and the Agents SDK. Keep your existing agent runtime; Internalize supplies the model and optional learning.

3. Optionally submit knowledgeCopy Anchor Link

Choose a stable subject ID. This example uses support-demo. The first admitted operation creates the subject if it does not exist; there is no separate create-subject endpoint.

curl --fail-with-body "$INTERNALIZE_BASE_URL/v1/internalizations" \
  -H "Authorization: Bearer $INTERNALIZE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: support-policy-001" \
  --data '{
    "subject_id": "support-demo",
    "content": "Northstar accepts returns within 30 days of delivery. Return labels are prepaid. Items marked final sale cannot be returned. An exchange uses the same 30-day window.",
    "max_cost_microusd": 5000000,
    "activate": true
  }'

A newly accepted request returns HTTP 202:

{ "id": "YOUR_JOB_ID", "status_url": "/v1/jobs/YOUR_JOB_ID" }

Save that job ID alongside your operation's idempotency key. Acceptance means the request is durably recorded and credits are reserved. It does not mean the knowledge has been learned yet.

4. Wait for learning and activationCopy Anchor Link

Read the returned status URL on the same origin:

curl --fail-with-body "$INTERNALIZE_BASE_URL/v1/jobs/YOUR_JOB_ID" \
  -H "Authorization: Bearer $INTERNALIZE_API_KEY"

While queued or running, poll with increasing delays. A suitable schedule starts around one second and grows to a maximum of 15 seconds. Training can take minutes or longer; no fixed completion time is promised.

Continue only when the job reports status: "ready", billing.settled: true, and result.activated: true. The candidate version is in result.candidate.version_id. Save it so the first inference can be checked against the version you intended to use.

If the candidate is ready but activation_conflict is true, another update changed the subject while training. Inspect versions and routing before deciding which version should be active. A rejected or failed job is not successful learning. For reconciliation_required, preserve the job and contact support; do not submit a replacement paid operation.

5. Ask without the policyCopy Anchor Link

Use the same subject. Notice that the inference request contains only a question, not the policy you submitted above.

curl --fail-with-body "$INTERNALIZE_BASE_URL/v1/inferences" \
  -H "Authorization: Bearer $INTERNALIZE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: support-question-001" \
  --data '{
    "subject_id": "support-demo",
    "messages": [
      { "role": "user", "content": "Can I return a final-sale item after ten days?" }
    ],
    "max_output_tokens": 2048,
    "temperature": 0
  }'

Poll this new job. On succeeded, read result.text and check adapter_version against the learned version. The expected policy behavior is to identify the final-sale exception. That expectation is a test you should run, not a fabricated response from the service.

6. Inspect usage and try a second questionCopy Anchor Link

Open Activity to inspect both jobs. Learning is billed for measured compute with no flat fee. Inference costs $12 per million input tokens and $30 per million output tokens, including reasoning. Memory hosting is $0.30/GB-month, prorated. Exact settled charges appear on each job.

Try a differently worded question, an exception, and a question the passage cannot answer. Evaluate the answer without putting the policy back into context. A useful follow-up is “Who pays for the return label?”; an unsupported question is “What is the warehouse street address?”

For an exact transport retry, reuse the original idempotency key and body. For an intentional new question, use a new key. Continue with the TypeScript client or Python HTTP guide to automate the loop.