Track competitor TikTok accounts

# Needs curl and jq. Step 1 is shown; repeat it with each step's query.
TOOL_ID=$(curl -s https://api.orcarouter.ai/v1/tools/discover \
  -H "Authorization: Bearer $ORCAROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"q": "tiktok user profile", "limit": 1}' | jq -r '.data.results[0].id')

# The inputs the tool takes
curl -s "https://api.orcarouter.ai/v1/tools/$TOOL_ID" \
  -H "Authorization: Bearer $ORCAROUTER_API_KEY" | jq '.data.input_schema'

# Run it with those inputs
curl -s "https://api.orcarouter.ai/v1/tools/$TOOL_ID/run" \
  -H "Authorization: Bearer $ORCAROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"input": {"username": "duolingo"}}'

# pip install requests openai
import json
import os

import requests
from openai import OpenAI

BASE = "https://api.orcarouter.ai"
KEY = os.environ["ORCAROUTER_API_KEY"]
HEADERS = {"Authorization": f"Bearer {KEY}"}

# One discover query per step of the recipe.
STEPS = ["tiktok user profile", "tiktok video search", "tiktok video comments"]
TASK = (
    "Compare the TikTok accounts @duolingo and @babbel: followers, "
    "what they posted recently and what commenters say about it."
)


def discover(query, limit=2):
    r = requests.post(f"{BASE}/v1/tools/discover", json={"q": query, "limit": limit}, headers=HEADERS, timeout=30)
    r.raise_for_status()
    return [tool["id"] for tool in r.json()["data"]["results"]]


def run(name, tool_input):
    r = requests.post(f"{BASE}/v1/tools/{name}/run", json={"input": tool_input}, headers=HEADERS, timeout=120)
    return r.text  # the tool's answer, or its error, goes back to the model


ids = [tool_id for query in STEPS for tool_id in discover(query)]
fns = requests.get(
    f"{BASE}/v1/tools/functions",
    params={"ids": ",".join(ids), "format": "openai"},
    headers=HEADERS,
    timeout=30,
)
fns.raise_for_status()
tools = fns.json()["data"]["tools"]

llm = OpenAI(base_url=f"{BASE}/v1", api_key=KEY)
messages = [{"role": "user", "content": TASK}]
for _ in range(10):
    msg = llm.chat.completions.create(model="openai/gpt-5-mini", messages=messages, tools=tools).choices[0].message
    messages.append(msg)
    if not msg.tool_calls:
        print(msg.content)
        break
    for call in msg.tool_calls:
        result = run(call.function.name, json.loads(call.function.arguments or "{}"))
        messages.append({"role": "tool", "tool_call_id": call.id, "content": result})

// npm install openai
import OpenAI from "openai";
import type { ChatCompletionMessageParam } from "openai/resources/chat/completions";

const BASE = "https://api.orcarouter.ai";
const KEY = process.env.ORCAROUTER_API_KEY!;
const headers = { Authorization: `Bearer ${KEY}`, "Content-Type": "application/json" };

// One discover query per step of the recipe.
const STEPS = ["tiktok user profile", "tiktok video search", "tiktok video comments"];
const TASK =
  "Compare the TikTok accounts @duolingo and @babbel: followers, " +
  "what they posted recently and what commenters say about it.";

async function discover(q: string, limit = 2): Promise<string[]> {
  const res = await fetch(`${BASE}/v1/tools/discover`, {
    method: "POST",
    headers,
    body: JSON.stringify({ q, limit }),
  });
  if (!res.ok) throw new Error(`discover: HTTP ${res.status}`);
  const { data } = await res.json();
  return data.results.map((tool: { id: string }) => tool.id);
}

async function run(name: string, input: unknown): Promise<string> {
  const res = await fetch(`${BASE}/v1/tools/${name}/run`, {
    method: "POST",
    headers,
    body: JSON.stringify({ input }),
  });
  return res.text(); // the tool's answer, or its error, goes back to the model
}

async function main() {
  const ids = (await Promise.all(STEPS.map((q) => discover(q)))).flat();
  const fns = await fetch(
    `${BASE}/v1/tools/functions?format=openai&ids=${encodeURIComponent(ids.join(","))}`,
    { headers },
  );
  if (!fns.ok) throw new Error(`functions: HTTP ${fns.status}`);
  const { data } = await fns.json();

  const llm = new OpenAI({ baseURL: `${BASE}/v1`, apiKey: KEY });
  const messages: ChatCompletionMessageParam[] = [{ role: "user", content: TASK }];
  for (let turn = 0; turn < 10; turn++) {
    const reply = await llm.chat.completions.create({ model: "openai/gpt-5-mini", messages, tools: data.tools });
    const msg = reply.choices[0].message;
    messages.push(msg);
    if (!msg.tool_calls?.length) {
      console.log(msg.content);
      return;
    }
    for (const call of msg.tool_calls) {
      const fn = call.type === "function" ? call.function : undefined;
      const result = fn ? await run(fn.name, JSON.parse(fn.arguments || "{}")) : "unsupported tool call";
      messages.push({ role: "tool", tool_call_id: call.id, content: result });
    }
  }
}

main();