Run an OpenAI image job

This guide sends one image request through Harn's model-job lifecycle. The OpenAI Responses API returns the image; Harn verifies and stores it by SHA-256.

Set the credential

Make OPENAI_API_KEY available to the Harn process. Do not put the value in a script, checked-in configuration, command argument, or receipt.

The API organization may need verification before it can use GPT Image models.

Run the example

From the Harn repository root:

MODEL_JOB_IMAGE_PROMPT='A flat blue flower emblem on a cream background, no text' \
harn run --no-sandbox examples/model-jobs/openai-image.harn

The example uses gpt-5.6-sol with the Responses API image-generation tool and requests low-quality PNG output. Set OPENAI_IMAGE_RESPONSE_MODEL to choose a different Responses API model that supports the tool. OpenAI selects the GPT Image model behind that tool.

The OpenAI image-generation guide documents supported inputs, output controls, and current pricing. It recommends the Responses API for conversational or multi-step image editing. Use the Image API when the application needs a direct, single-request GPT Image model choice.

The receipt is printed to stdout. Its first assets entry contains the output path and asset://sha256/... identity.

Confirm the result

This PNG came from the example's low-quality hosted path on August 1, 2026. Harn stored 898,270 bytes with SHA-256 ba1826d69bb02712af24ced31800cc1197b37f963a034a4a8671142407917469.

Cornflower-blue botanical emblem generated through the OpenAI model-job adapter

Check job.state == "succeeded", then call media_asset_verify_result on the first asset before using it as an edit input or copying it to a product-owned location.

Edit an image

Set the request task to image.edit and pass one or more verified MediaAsset values in request.inputs. The adapter sends each asset as a base64 data URL. It rejects changed asset bytes before the network request.

For a continued Responses API edit, set request.params.previous_response_id to the response ID stored in the first output asset's metadata. This keeps the prior image in API context.

Read the model-job reference for lifecycle, error, and replay behavior.