Examples for using runway through RunAPI from agent tools or API calls. Copy a prompt, then use it in Claude Code, Codex, Cursor, Windsurf, or your backend.
1. claude mcp add runapi -s user -- npx -y @runapi.ai/mcp
2. Restart Claude Code
3. Paste this prompt: Generate a video: "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
1. codex plugin install runapi-mcp@agents
2. Restart Codex
3. Paste this prompt: Generate a video: "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
1. npx @runapi.ai/mcp init cursor
2. Restart Cursor
3. Paste this prompt: Generate a video: "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
1. npx @runapi.ai/mcp init windsurf
2. Restart Windsurf
3. Paste this prompt: Generate a video: "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
curl -X POST https://runapi.ai/api/v1/runway/text_to_video \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "runway",
"prompt": "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
}
JSON
import { RunwayClient } from "@runapi.ai/runway";
const client = new RunwayClient({
apiKey: process.env.RUNAPI_API_KEY,
});
const result = await client.textToVideo.run({
"model": "runway",
"prompt": "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
});
console.log(result.id);
require "runapi/runway"
client = RunApi::Runway::Client.new
result = client.text_to_video.run(
model: "runway",
prompt: "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
)
puts result.id
package main
import (
"context"
"fmt"
"log"
"net/http"
"os"
"strings"
)
func main() {
body := strings.NewReader("{\"model\":\"runway\",\"prompt\":\"A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean.\"}")
req, err := http.NewRequestWithContext(context.Background(), http.MethodPost, "https://runapi.ai/api/v1/runway/text_to_video", body)
if err != nil {
log.Fatal(err)
}
req.Header.Set("Authorization", "Bearer "+os.Getenv("RUNAPI_API_KEY"))
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)
if err != nil {
log.Fatal(err)
}
defer resp.Body.Close()
fmt.Println(resp.Status)
}
A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean.
curl -X POST https://runapi.ai/api/v1/runway/text_to_video \
-H "Authorization: Bearer $RUNAPI_KEY" \
-H "Content-Type: application/json" \
--data-binary @- <<'JSON'
{
"model": "runway",
"prompt": "A glowing ocean at night time with bioluminescent creatures under water. The camera starts with a macro close-up of a glowing jellyfish and then expands to reveal the entire ocean lit up with various glowing colors under a starry sky. Camera Movement: Begin with a macro shot of the jellyfish, then gently pull back and up to showcase the glowing ocean."
}
JSON
FAQ
Using runway prompts
What is %{model}?
%{model} is available through RunAPI as part of the unified model catalog. These prompts show practical input patterns that agents and backend services can reuse.
How do I use these prompts?
Copy any prompt and paste it into Claude Code, Codex, Cursor, or Windsurf after installing the RunAPI MCP Server. Developers can also copy the API example and send the prompt directly.
Do these prompts cost money to browse?
Browsing and copying prompt examples is free. Generation requests only cost money when you call a RunAPI model with your API key.
Can I adapt the prompts for production?
Yes. Treat each prompt as a starting point, then add your brand rules, output dimensions, safety constraints, and application-specific context before using it in production.