Nano Banana Pro

Nano Banana Pro

Generate or edit images through the NanoPhoto.AI Nano Banana Pro API.

Prerequisites

1. Obtain an API key at: https://nanophoto.ai/settings/apikeys
2. Configure NANOPHOTO_API_KEY before using the skill.

Preferred OpenClaw setup:

• Open the skill settings for this skill
• Add an environment variable named NANOPHOTO_API_KEY
• Paste the API key as its value

Equivalent config shape:

{
  "skills": {
    "entries": {
      "nano-banana-pro": {
        "enabled": true,
        "env": {
          "NANOPHOTO_API_KEY": "your_api_key_here"
        }
      }
    }
  }
}

Other valid ways to provide the key:

• Shell: export NANOPHOTO_API_KEY="your_api_key_here"
• Tool-specific env config: any runtime that injects NANOPHOTO_API_KEY

Choose the mode

• Use generate for text-to-image.
• Use edit for image-to-image edits.
• For edit, provide one or more public image URLs.
• Do not use local image files or base64 images with the API. The API only accepts inputImageUrls.

Recommended workflow

1. Collect the prompt.
2. Decide mode: generate or edit.
3. Choose aspectRatio and imageQuality.
4. For edit, collect up to 8 public image URLs.
5. Submit the generation request.
6. Poll the status endpoint every 5 seconds until completed or failed.
7. Return the final imageUrl and progress/result details.
8. Expect real-world wait time to vary from roughly 30 seconds to 300 seconds depending on queue/load and prompt complexity; avoid short timeouts.

Preferred command

Use the bundled script for reliable submission + polling:

Text to image

macOS / Linux:

python3 scripts/nano_banana_generate.py \
  --prompt "A futuristic cityscape at sunset with flying cars and neon lights" \
  --mode generate \
  --aspect-ratio 16:9 \
  --image-quality 2K

Windows:

python scripts/nano_banana_generate.py ^
  --prompt "A futuristic cityscape at sunset with flying cars and neon lights" ^
  --mode generate ^
  --aspect-ratio 16:9 ^
  --image-quality 2K

Image to image

macOS / Linux:

python3 scripts/nano_banana_generate.py \
  --prompt "Transform this photo into a watercolor painting style" \
  --mode edit \
  --input-image-url https://static.nanophoto.ai/demo/nano-banana-pro.webp \
  --aspect-ratio 16:9 \
  --image-quality 1K

Windows (cmd.exe):

python scripts/nano_banana_generate.py ^
  --prompt "Transform this photo into a watercolor painting style" ^
  --mode edit ^
  --input-image-url https://static.nanophoto.ai/demo/nano-banana-pro.webp ^
  --aspect-ratio 16:9 ^
  --image-quality 1K

Windows (PowerShell):

python scripts/nano_banana_generate.py `
  --prompt "Transform this photo into a watercolor painting style" `
  --mode edit `
  --input-image-url https://static.nanophoto.ai/demo/nano-banana-pro.webp `
  --aspect-ratio 16:9 `
  --image-quality 1K

The script reads NANOPHOTO_API_KEY from the environment, submits the task, polls automatically, and prints the final JSON result.

Manual API calls

Submit generation

curl -X POST "https://nanophoto.ai/api/nano-banana-pro/generate" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $NANOPHOTO_API_KEY" \
  --data-raw '{
    "prompt": "A futuristic cityscape at sunset with flying cars and neon lights",
    "mode": "generate",
    "aspectRatio": "16:9",
    "imageQuality": "2K"
  }'

Check status

curl -X POST "https://nanophoto.ai/api/nano-banana-pro/check-status" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $NANOPHOTO_API_KEY" \
  --data-raw '{"generationId": "abc123xyz"}'

Parameter guidance

• aspectRatio
  • 16:9
  • 9:16
  • 4:3
  • 3:4
• imageQuality
  • 1K: lowest cost
  • 2K: balanced quality/cost
  • 4K: highest cost
• inputImageUrls
  • Max 8 URLs
  • Must be publicly reachable

Error handling

Bundled files

• scripts/nano_banana_generate.py: submit a generation/edit task and poll until completion.
• references/api.md: condensed API reference, costs, and response shapes. erate.py`: submit a generation/edit task and poll until completion.
• references/api.md: condensed API reference, costs, and response shapes. eference, costs, and response shapes.