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Python guide

Generate comics with Python and explicit status handling.

A Python integration submits JSON to the generation endpoint and checks the returned generation ID and lifecycle state.

The example is a minimal server-side starting point, not a complete retry, queue, persistence, or webhook implementation.

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Direct answer

The decision this page is for.

Last verified:

A Python integration posts JSON with the requests library, raises on HTTP errors, and reads the generation ID and status. Production workers should add retries, timeouts, and persisted job records.

Verifiable request

Python request with error handling

Python
import os
import requests

response = requests.post(
    "https://api.llamagen.ai/v1/comics/generations",
    headers={"Authorization": f"Bearer {os.environ['LLAMAGEN_API_KEY']}"},
    json={
        "prompt": "A watercolor manga about a botanist on Mars.",
        "fixPanelNum": 4,
        "size": "1024x1536",
    },
    timeout=30,
)
response.raise_for_status()
generation = response.json()
print(generation["id"], generation["status"])

Structured response

State your application can route

response.jsonPROCESSED
{
  "id": "gen_abc123",
  "status": "PROCESSED",
  "comics": [
    {
      "page": 0,
      "panels": [
        {
          "panel": 0,
          "caption": "The first Martian flower opens.",
          "assetUrl": "https://cdn.llamagen.ai/comics/panel-1.webp"
        }
      ]
    }
  ]
}

Python walkthrough

Build the smallest observable Python workflow.

The visual follows one authenticated request from server-side configuration through polling and durable asset storage.

A backend developer routes Python-style code through processing modules into a consistent four-panel archaeology comic
Editorial illustration of a server-side Python integration; it is not a generated API result.Art direction · European ligne claire

Capability and boundary facts

Facts an integration can verify.

Last verified:

Endpoint
POST /v1/comics/generations
Authentication
Bearer API key
Lifecycle
QUEUED → PROCESSING → PROCESSED or FAILED
Delivery
Poll by generation ID or use a callback URL
Runtime
Python server, worker, notebook, or pipeline

Good fit

Use it when

  • Content pipelines
  • Backend services
  • Data and automation workflows

Wrong fit

Do not use it for

  • Embedding long-lived keys in notebooks you share
  • Blocking a web request until image generation finishes
  • Ignoring HTTP errors

Production limits

Plan for the boundaries.

  • 01A production worker should use retries with backoff and persist the generation ID.
  • 02Set connection and read timeouts appropriate to your infrastructure.
  • 03Generation is asynchronous; production interfaces must handle processing and failure states.
  • 04Outputs should be reviewed against product, brand, safety, and print requirements before delivery.
  • 05You must have the rights to prompts, photos, scripts, and reference assets submitted to the API.

Authoritative sources

LlamaGen API documentationOpenAPI specificationCurrent pricingTermsService status

Questions

Direct answers before you integrate.

01Is the Python snippet production-ready?
It is a starting point. Add retries with backoff, persist the generation ID, and avoid blocking a web request until images finish.
02Can I put the key in a shared notebook?
No. Use environment variables or a secret store. Do not embed long-lived keys in notebooks you share.
03When should Python use webhooks instead of polling?
When jobs outlive the process that created them—queues, cron, or fulfillment pipelines. Keep a polling fallback for missed callbacks.

Related evaluation paths

CommercialComic API for Developers: REST, MCP, and WebhooksGuideComic API Webhooks and Async DeliveryFormatStory-to-Comic API for Scripts and Story Briefs

Next step

Move from evidence to a real API request.

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