Everything you need to use ZapLLM

curl https://api.zap.dscv.me/v1/chat/completions \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-id>",
    "messages": [{ "role": "user", "content": "Say pong" }],
    "reasoning_effort": "medium",
    "stream": true
  }'

import OpenAI from "openai"

const client = new OpenAI({
  apiKey: process.env.ZAPLLM_KEY,
  baseURL: "https://api.zap.dscv.me/v1",
})

// Streaming works: each chunk is forwarded as it arrives.
const stream = await client.chat.completions.create({
  model: "<model-id>",
  messages: [{ role: "user", content: "Explain SSE in one line" }],
  stream: true,
})

for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "")
}

from openai import OpenAI

client = OpenAI(
    api_key="sk_live_...",
    base_url="https://api.zap.dscv.me/v1",
)

response = client.chat.completions.create(
    model="<model-id>",
    messages=[{"role": "user", "content": "Say pong"}],
)

print(response.choices[0].message.content)

curl https://api.zap.dscv.me/v1/responses \
  -H "Authorization: Bearer sk_live_..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-id>",
    "input": [{ "role": "user", "content": "Prove that the square root of 2 is irrational" }],
    "reasoning": {
      "effort": "high",
      "summary": "auto"
    },
    "stream": true
  }'