Auto Swarm Builder: Describe a Goal, Get a Swarm

pip install requests python-dotenv

export SWARMS_API_KEY="your-api-key-here"

import json
import os

import requests
from dotenv import load_dotenv

load_dotenv()

API_KEY = os.getenv("SWARMS_API_KEY")
BASE_URL = "https://api.swarms.world"

headers = {"x-api-key": API_KEY, "Content-Type": "application/json"}


def auto_swarm(name: str, description: str, task: str, max_loops: int = 1) -> dict:
    payload = {
        "name": name,
        "description": description,
        "max_loops": max_loops,
        "model_name": "gpt-4.1",
        "task": task,
    }
    response = requests.post(
        f"{BASE_URL}/v1/auto-swarm-builder/completions",
        headers=headers,
        json=payload,
        timeout=600,
    )
    response.raise_for_status()
    return response.json()

result = auto_swarm(
    name="Competitive Teardown Swarm",
    description="Multi-angle competitive analysis of a set of companies.",
    task=(
        "Produce a competitive teardown comparing Stripe, Adyen, Checkout.com, "
        "Block, and Marqeta. For each, cover: target customer, pricing model, "
        "differentiation, and the single biggest threat to their position. "
        "End with a 5x5 head-to-head matrix and a 'who wins which segment' summary."
    ),
)
print(json.dumps(result, indent=2))

result = auto_swarm(
    name="Content Pipeline Swarm",
    description="Brief in, publish-ready piece out.",
    task=(
        "Produce a publish-ready 1,500-word blog post on 'how mid-market CFOs "
        "should think about AI agent ROI in 2026.' Include: an SEO-optimized "
        "title and meta description, an executive summary, three concrete "
        "case studies (real or representative), a skeptic's counter-argument "
        "with rebuttal, and a 5-bullet CFO-ready takeaway box. Output in "
        "markdown ready for a CMS paste."
    ),
)
print(json.dumps(result, indent=2))

result = auto_swarm(
    name="Investment Memo Swarm",
    description="Generate a board-ready investment memo from a single ticker.",
    task=(
        "Produce a board-ready investment memo on PLTR (Palantir Technologies). "
        "Sections: 1) Business model and revenue mix, 2) Competitive moat and "
        "key competitors, 3) Financial health (revenue growth, margins, cash), "
        "4) Top 5 risks with mitigations, 5) Buy/Hold/Sell recommendation with "
        "a 12-month price view. Cite the specific metrics that drive each "
        "conclusion. Maximum 1,200 words."
    ),
    max_loops=2,
)
print(json.dumps(result, indent=2))

result = auto_swarm(
    name="Architecture Security Review Swarm",
    description="Threat-model a proposed system from a written description.",
    task=(
        "Threat-model this proposed architecture: a multi-tenant SaaS that "
        "ingests customer data via signed S3 uploads, processes it through a "
        "FastAPI service backed by Postgres, and exposes results via a "
        "React SPA. Auth is via Auth0 with per-tenant RBAC. Identify the top "
        "10 threats using STRIDE categories, rank each by likelihood and "
        "impact, and propose a concrete mitigation. End with a prioritized "
        "two-week remediation plan."
    ),
)
print(json.dumps(result, indent=2))

def show_result(result: dict) -> None:
    print(f"Job ID: {result.get('job_id')}")
    print(f"Status: {result.get('status')}")

    outputs = result.get("outputs") or result.get("output") or {}
    print("\n--- Swarm Output ---")
    print(json.dumps(outputs, indent=2)[:2000])

    usage = result.get("usage") or {}
    cost = usage.get("token_cost") or usage.get("billing_info", {}).get("total_cost")
    if cost is not None:
        print(f"\nCost: ${cost}")


show_result(result)