Documentation Index
Fetch the complete documentation index at: https://docs.kymaapi.com/llms.txt
Use this file to discover all available pages before exploring further.
Best Model
Qwen 3.6 Plus (qwen-3.6-plus) — Best overall quality for varied automation tasks. ~$0.75 per 1K requests.
For high-volume, low-cost automation: Gemini 2.5 Flash (gemini-2.5-flash) or Qwen 3 32B (qwen-3-32b) depending on context needs.
Python — Email Automation
from openai import OpenAI
client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-your-key")
def draft_reply(email_body: str) -> str:
response = client.chat.completions.create(
model="qwen-3.6-plus",
messages=[
{"role": "system", "content": "Draft a professional reply to this email. Be concise and friendly."},
{"role": "user", "content": email_body}
]
)
return response.choices[0].message.content
# Process incoming emails
emails = ["Can we reschedule our meeting?", "Please send the Q2 report"]
for email in emails:
print(draft_reply(email))
Python — Batch Data Processing
import json
from openai import OpenAI
client = OpenAI(base_url="https://kymaapi.com/v1", api_key="ky-your-key")
def classify_ticket(ticket: str) -> dict:
response = client.chat.completions.create(
model="qwen-3-32b", # fast + structured classification
messages=[
{"role": "system", "content": "Classify this support ticket. Return JSON: {\"category\": \"...\", \"priority\": \"low|medium|high\", \"summary\": \"...\"}"},
{"role": "user", "content": ticket}
],
response_format={"type": "json_object"}
)
return json.loads(response.choices[0].message.content)
tickets = [
"My payment failed and I can't access my account",
"How do I change my email address?",
"Your API has been down for 2 hours, this is critical"
]
for ticket in tickets:
result = classify_ticket(ticket)
print(f"[{result['priority']}] {result['category']}: {result['summary']}")
JavaScript — Scheduled Reports
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://kymaapi.com/v1",
apiKey: process.env.KYMA_API_KEY,
});
async function generateReport(data: string): Promise<string> {
const response = await client.chat.completions.create({
model: "qwen-3.6-plus",
messages: [
{ role: "system", content: "Analyze this data and write a brief executive summary with key insights and action items." },
{ role: "user", content: data },
],
});
return response.choices[0].message.content!;
}
// Run daily via cron
const metrics = "Users: 1,200 (+15%), Revenue: $5,400 (+8%), Churn: 2.1% (-0.3%)";
console.log(await generateReport(metrics));
Tips
- Use
qwen-3-32b for high-volume structured tasks (classification, extraction)
- Use
qwen-3.6-plus when quality matters (emails, reports, summaries)
- Add
response_format: {"type": "json_object"} for structured output
- Batch requests where possible to reduce latency overhead
Cost Estimate
| Workflow | Volume | Model | Daily Cost |
|---|
| Email drafts | 50/day | qwen-3.6-plus | ~$0.04 |
| Ticket classification | 500/day | qwen-3-32b | ~$0.18 |
| Daily reports | 5/day | qwen-3.6-plus | ~$0.004 |
Next Steps