Name: Riverflow 2.5 Pro API: Agentic Image Generation and Editing
Brand: magicapi
SKU: api-magicapi-top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro
Availability: InStock
Author: API Market

Shashank Agarwal

# 🎨 Image Generation and Editing API (Sourceful Riverflow 2.5 Pro)

## Overview
Riverflow 2.5 Pro is an image generation and editing model from Sourceful. Its developer describes it as a top-quality agentic image model with multi-step reasoning, candidate scoring and adjustable thinking effort.

Send a written `instruction` to generate a new image, or add up to 10 `init_images` to edit them. You choose the output `resolution` (1K, 2K or 4K), `aspect_ratio`, `output_format`, how the `background` is handled and how long the model thinks with `thinking_level`. Each request returns the URL of one image.

## Features
- **Generate or edit**: an `instruction` alone generates an image; with `init_images` (up to 10 URLs) the model edits them.
- **Resolution**: `resolution` is `1K` (default), `2K` or `4K`.
- **Aspect ratios**: `auto` (default, the model chooses), `21:9`, `16:9`, `3:2`, `4:3`, `5:4`, `1:1`, `4:5`, `3:4`, `2:3`, `9:16`.
- **Backgrounds**: `background` is `original` (default), `transparent` or `solid`; for `solid`, set the color with `background_color` (6-digit hex, default `#ffffff`).
- **Thinking level**: `thinking_level` is `low`, `medium` (default), `high` or `xhigh`. Higher levels let the model reason longer and use more output tokens.
- **Candidate scoring**: guide how candidates are judged with `scoring_prompt`, or a `scoring_rubric` of 1 to 8 weighted dimensions encoded as a JSON array string.
- **Font control**: up to 2 HTTPS font URLs in `font_urls`, with the matching text strings in `font_texts`.
- **Safe retries**: send a stable `idempotency_key` when you retry the same request.
- **Output formats**: `webp` (default), `png` or `jpg`.

## Use Cases
1. **Product renders**: premium studio images of packaging and products, as in the example.
2. **Cut-out assets**: images with a `transparent` background for catalogs, packaging mock-ups or web pages.
3. **Editing existing images**: change or restyle up to 10 input images with one instruction.
4. **Brand-checked visuals**: score candidates against your own rubric and render text in your typeface with `font_urls` and `font_texts`.

## Visual Examples
### Example Input
Instruction: "Generate a premium studio render of a white ceramic skincare bottle with soft reflections."

Settings: `resolution` `1K`, `output_format` `webp`, `thinking_level` `medium`. No input images; all other fields were left at their defaults.

### Example Output
![Riverflow 2.5 Pro output: a studio render of a white ceramic skincare bottle with soft reflections](https://blog.api.market/wp-content/uploads/2026/10/riverflow-v2-5-pro-output-acjysg5c-api-market.webp)

## Input Parameters
| Field | Type | Required | Default | Allowed values | Description |
| --- | --- | --- | --- | --- | --- |
| `instruction` | string | Yes |  | at least 2 characters | Generation or edit instruction. Minimum 2 characters |
| `init_images` | array of strings (URLs) | No | none |  | Optional input image URLs for image-to-image edits. Up to 10 |
| `resolution` | string | No | `1K` | `1K`, `2K`, `4K` | Target resolution: 1K, 2K or 4K |
| `aspect_ratio` | string | No | `auto` | `auto`, `21:9`, `16:9`, `3:2`, `4:3`, `5:4`, `1:1`, `4:5`, `3:4`, `2:3`, `9:16` | Aspect ratio of the output. `auto` lets the model choose |
| `output_format` | string | No | `webp` | `webp`, `png`, `jpg` | Final output image format. |
| `background` | string | No | `original` | `original`, `transparent`, `solid` | Final background handling. |
| `background_color` | string | No | `#ffffff` |  | 6-digit hex color used when `background` is `solid` |
| `thinking_level` | string | No | `medium` | `low`, `medium`, `high`, `xhigh` | Thinking budget the model uses before generating |
| `scoring_prompt` | string | No | none |  | Optional judging instructions for scoring candidate images |
| `scoring_rubric` | string | No | none |  | Optional scoring rubric, encoded as a JSON array string: 1 to 8 dimensions, each with key, label, description, weight, optional passingScore and optional scoreGuidance |
| `font_urls` | array of strings | No | none |  | Optional HTTPS font URLs for font control. Up to 2 |
| `font_texts` | array of strings | No | none |  | Text strings matching `font_urls`, for font control |
| `idempotency_key` | string | No | none |  | Optional stable key for safe retries of the same request |
| `safety_checker` | boolean | No | `true` |  | Enable the safety checker |
| `enhance_prompt` | boolean | No | `false` |  | Let the model enhance the instruction |

## Pricing
- **Pay As You Go (PAYG) pricing**: you only pay for what you use.
- **Price**: $0.0013 per API unit.
- **1 API unit = 100 weighted tokens.** An output token counts 1, an input token counts 0, so your instruction and input images are free.
- That works out to **$13 per million output tokens**. The model reports how many output tokens each request used; the count depends on the request, for example the output `resolution` and `thinking_level`.
- Each request is rounded up to a whole API unit.
- If the model reports no output token count for a request, the request is billed as 200 API units ($0.26).
- **Example cost**: the example request (1K, `thinking_level` `medium`) used 24,785 output tokens, rounded up to 248 API units = **$0.3224**.
- **Failed or cancelled predictions are not billed.**
- **GET requests to check prediction status** are free and don't count toward API unit usage.
- **No monthly subscription fee.**

## API Documentation

### Base URL
All API requests go to: `https://prod.api.market/api/v1/magicapi/top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro`

### Authentication
Send your api.market API key in the `x-api-market-key` header:
```
x-api-market-key: YOUR_API_MARKET_KEY
```

### Asynchronous Processing
**IMPORTANT**: This API is asynchronous. The flow is:
1. Send a POST request to `/predictions`. A successful request returns **201 Created** with a prediction `id` and a `status` of `starting` or `processing`.
2. Poll GET `/predictions/{id}` with that ID.
3. Keep polling while `status` is `starting` or `processing`.
4. Stop when `status` is `succeeded` (read `output`), `failed` or `canceled` (read `error`).

A polling interval of 1 second and an overall timeout of 600 seconds (10 minutes) work well.

### Endpoints

#### Create a Prediction
```bash
curl -X POST "https://prod.api.market/api/v1/magicapi/top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro/predictions" \
  -H "Content-Type: application/json" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY" \
  -d '{
      "version": "sourceful/riverflow-v2.5-pro",
      "input": {
        "resolution": "1K",
        "instruction": "Generate a premium studio render of a white ceramic skincare bottle with soft reflections.",
        "output_format": "webp",
        "thinking_level": "medium"
      }
    }'
```

Response (201 Created):
```json
{
  "id": "acjysg5c6hrmt0d1c6vayzzwtg",
  "version": "sourceful/riverflow-v2.5-pro",
  "status": "starting",
  "created_at": "2026-10-22T03:45:24.171Z"
}
```

#### Check Prediction Status
```bash
curl -X GET "https://prod.api.market/api/v1/magicapi/top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro/predictions/{id}" \
  -H "x-api-market-key: YOUR_API_MARKET_KEY"
```

Response when the prediction has finished (200 OK):
```json
{
  "id": "acjysg5c6hrmt0d1c6vayzzwtg",
  "status": "succeeded",
  "output": "https://blog.api.market/wp-content/uploads/2026/10/riverflow-v2-5-pro-output-acjysg5c-api-market.webp",
  "error": null,
  "created_at": "2026-10-22T03:45:24.171Z",
  "started_at": "2026-10-22T03:45:24.195Z",
  "completed_at": "2026-10-22T03:47:15.399Z"
}
```

`output` is a single URL string pointing to the generated image.

### Status Codes
- **201 Created**: prediction created (POST `/predictions`). This is the normal success response.
- **200 OK**: prediction details returned (GET `/predictions/{id}`).
- **400 Bad Request**: invalid input, for example a missing `instruction`, an instruction shorter than 2 characters or a `resolution` that is not in the list.
- **401 Unauthorized**: missing or invalid API key.
- **404 Not Found**: no prediction with that ID.
- **429 Too Many Requests**: rate limit exceeded.
- **500 Internal Server Error** or **503 Service Unavailable**: temporary server problem; retry later.

## Rate Limits
All plans allow 10 requests per second. Requests above that return `429 Too Many Requests`; back off before retrying and honour any `Retry-After` header. Polling a prediction with GET is free but still counts toward the per-second limit, so poll every 1 to 5 seconds rather than in a tight loop.

## Error Handling
- Check the HTTP status code of every request and read the response body for the error message.
- A prediction can also end with `status` set to `failed`; the `error` field then explains why. Failed predictions are not billed.
- Retry 429, 500 and 503 responses with exponential backoff. Fix the input before retrying a 400.

## Implementation Guide
### Example Code

#### JavaScript
```javascript
const apiKey = "YOUR_API_MARKET_KEY";
const baseUrl = "https://prod.api.market/api/v1/magicapi/top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro";

async function createAndWaitForPrediction(input) {
  const response = await fetch(`${baseUrl}/predictions`, {
    method: "POST",
    headers: {
      "Content-Type": "application/json",
      "x-api-market-key": apiKey
    },
    body: JSON.stringify({ version: "sourceful/riverflow-v2.5-pro", input })
  });

  if (response.status !== 201) {
    throw new Error(`API error: ${response.status} ${await response.text()}`);
  }

  const prediction = await response.json();
  return pollForResults(prediction.id);
}

const delay = (ms) => new Promise((resolve) => setTimeout(resolve, ms));

async function pollForResults(predictionId) {
  const maxTimeout = 600000;
  const pollInterval = 1000;
  const startTime = Date.now();

  while (Date.now() - startTime < maxTimeout) {
    const response = await fetch(`${baseUrl}/predictions/${predictionId}`, {
      headers: { "x-api-market-key": apiKey }
    });

    if (!response.ok) {
      throw new Error(`API error: ${response.status} ${await response.text()}`);
    }

    const prediction = await response.json();

    if (prediction.status === "succeeded") {
      return prediction.output;
    }

    if (prediction.status === "failed" || prediction.status === "canceled") {
      throw new Error(`Prediction ${prediction.status}: ${prediction.error || "Unknown error"}`);
    }

    await delay(pollInterval);
  }

  throw new Error("Prediction timed out after 10 minutes");
}

createAndWaitForPrediction({
    "resolution": "1K",
    "instruction": "Generate a premium studio render of a white ceramic skincare bottle with soft reflections.",
    "output_format": "webp",
    "thinking_level": "medium"
  }).then((output) => console.log(output));
```

#### Python
```python
import time
import requests

API_KEY = "YOUR_API_MARKET_KEY"
BASE_URL = "https://prod.api.market/api/v1/magicapi/top-quality-agentic-image-model-sourceful-riverflow-v2-5-pro"


def create_and_wait_for_prediction(input_data):
    headers = {"Content-Type": "application/json", "x-api-market-key": API_KEY}
    response = requests.post(
        f"{BASE_URL}/predictions",
        headers=headers,
        json={"version": "sourceful/riverflow-v2.5-pro", "input": input_data},
    )

    if response.status_code != 201:
        raise Exception(f"API error: {response.status_code} {response.text}")

    return poll_for_results(response.json()["id"])


def poll_for_results(prediction_id):
    max_timeout = 600
    poll_interval = 1
    headers = {"x-api-market-key": API_KEY}
    start_time = time.time()

    while time.time() - start_time < max_timeout:
        response = requests.get(f"{BASE_URL}/predictions/{prediction_id}", headers=headers)

        if response.status_code != 200:
            raise Exception(f"API error: {response.status_code} {response.text}")

        prediction = response.json()

        if prediction["status"] == "succeeded":
            return prediction["output"]

        if prediction["status"] in ("failed", "canceled"):
            raise Exception(f"Prediction {prediction['status']}: {prediction.get('error') or 'Unknown error'}")

        time.sleep(poll_interval)

    raise Exception("Prediction timed out after 10 minutes")


output = create_and_wait_for_prediction({
    "resolution": "1K",
    "instruction": "Generate a premium studio render of a white ceramic skincare bottle with soft reflections.",
    "output_format": "webp",
    "thinking_level": "medium"
})
print(output)
```

## FAQ
- **How do I edit an existing image?**
  - Pass up to 10 image URLs in `init_images` and describe the change in `instruction`.
- **Can I get a transparent background?**
  - Yes. Set `background` to `transparent`. For a flat color, set `background` to `solid` and `background_color` to a 6-digit hex value such as `#ffffff`.
- **What does `thinking_level` change?**
  - How long the model reasons before producing the image: `low`, `medium` (default), `high` or `xhigh`. Higher levels can produce more output tokens, so they cost more and take longer.
- **Why does the price differ between requests?**
  - Billing follows the output tokens the model reports, which depend on the request (for example a 4K image at `xhigh` versus a 1K image at `low`). The 1K `medium` example used 24,785 output tokens, $0.3224.
- **How many images does one request return?**
  - One. `output` is a single image URL.
- **Is the 201 response from POST an error?**
  - No. 201 Created means the prediction was accepted. Use the returned `id` to poll GET `/predictions/{id}` until `status` is `succeeded`, `failed` or `canceled`.

## Contact Us
If you have any questions, reach out to us through the chat on the bottom right of the page. You can also email our support team at hello@magicapi.com.


Generate and edit images from text instructions with Sourceful Riverflow 2.5 Pro: multi-step reasoning, 1K to 4K output and transparent backgrounds.