Image generation

image_generate turns a text prompt into an image (and can edit existing images). It routes through the Hive image service to OpenAI's gpt-image-2; the cost is billed to the user's Hive credits exactly like an LLM call, so there is no API key to configure. Each generated image is also saved to disk.

The call

image_generate(
    prompt: str,                       # required — what to draw
    reference_images: list[str] = None,# local paths or http(s) URLs to edit/condition on
    size: str = "1024x1024",           # 1024x1024 | 1536x1024 (landscape) | 1024x1536 (portrait) | auto
    quality: str = "low",              # low only (medium & high disabled)
    n: int = 1,                        # 1–4; each image is billed separately
    output_format: str = "png",        # png | jpeg | webp
    model: str = "gpt-image-2",
)

Defaults are deliberately cheap and fast. quality is locked to low — medium and high are disabled for cost control, and any request for a higher tier is automatically forced to low. Only raise n when the user explicitly wants variations.

Writing the prompt

Be concrete: name the subject, style (photo, flat vector, 3D, watercolor…), composition/framing, color palette, mood, and any text to render (gpt-image-2 renders text well — quote it exactly, e.g. the words "Launch Day" in bold).

Reference-image editing

Pass reference_images to edit, restyle, or compose from existing images — restyle a product photo, place a logo on a mockup, keep a character's identity across images, or merge elements. Provide up to 10 local file paths or http(s) URLs; the model conditions on them at high fidelity. Example:

image_generate(prompt="Put this product on a marble kitchen counter, soft morning light",
               reference_images=["data/uploads/bottle.png"])

A good source of reference images is something the user attached (read it from the path in their message) or an image you generated earlier (use its saved path).

How it runs — start, then collect (it's asynchronous)

Image generation can take a couple of minutes, so image_generate runs in the background: it returns immediately with {"status":"started","handle":"bg_…"}. You then poll the generic collect_result tool with that handle until the image is ready:

start = image_generate(prompt="A minimalist bee logo, flat vector, amber on white")
# start.handle == "bg_1"
res = collect_result(handle="bg_1", wait_seconds=30)
#   → {"status":"pending", ...}   ← not done yet; call collect_result again
#   → eventually the real result: {"images":[{"path": …}], "usage": …, …}

collect_result waits up to wait_seconds (≤45) per call and returns {"status":"pending"} until generation finishes — just call it again with the same handle until you get the real result. It's fine to do other small things between polls. Don't start a second image while one is pending unless the user asked for several.

Show the user

The finished result's JSON has images (each with a path) plus model, n, and usage; one image is previewed inline. Call attach_file(path) on the image path to surface a downloadable chip in chat. Do not paste base64 or write ![](...) markdown.

Failure modes

Errors surface in the collect_result result as {"error": ...} (the tool never raises). Handle these:

• Out of credits / subscription inactive (status: 402) — tell the user they're out of Hive credits; do not retry.
• Model unavailable / org verification (status: 403) — report that image generation is currently unavailable; do not loop.
• Request rejected / moderated (status: 400) — the prompt was likely refused; rephrase it (less explicit, no real-person likeness) and try once.
• Rate limited (status: 429) — wait a moment and retry once.
• Still pending after several minutes — collect_result keeps returning pending well past ~4 min: the job likely failed. Tell the user and start once more. ({"error":"Unknown … handle"} means it was already collected or never started — just start a fresh image_generate.)

End-to-end example

User: "make us a logo — a friendly robot, simple and modern."

1. image_generate(prompt="A friendly modern robot mascot logo, simple flat vector, rounded shapes, teal and white, centered, plain background", quality="low") → {"status":"started","handle":"bg_1"}
2. collect_result(handle="bg_1", wait_seconds=30) — repeat until it returns the real result (not {"status":"pending"}).
3. Take result.images[0].path, call attach_file(that_path).
4. Reply briefly: "Here's a first take — want it bolder, a different color, or any tweaks?"