Claude Opus 4.8

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

pip install swarms-client python-dotenv

import json
import os

from dotenv import load_dotenv
from swarms_client import SwarmsClient

load_dotenv()

client = SwarmsClient(
    api_key=os.getenv("SWARMS_API_KEY"),
    base_url="https://api.swarms.world",
    timeout=1000,
)

result = client.agent.run(
    agent_config={
        "agent_name": "Bloodwork Diagnosis Expert",
        "description": "An expert doctor specializing in interpreting and diagnosing blood work results.",
        "system_prompt": (
            "You are an expert medical doctor specializing in the interpretation and diagnosis of blood work. "
            "Your expertise includes analyzing laboratory results, identifying abnormal values, "
            "explaining their clinical significance, and recommending next diagnostic or treatment steps. "
            "Provide clear, evidence-based explanations and consider differential diagnoses based on blood test findings."
        ),
        "model_name": "anthropic/claude-opus-4-8",
        "max_loops": 1,
        "max_tokens": 8192,
    },
    task="Hemoglobin 10.2 g/dL, MCV 72 fL, ferritin 8 ng/mL — what's your diagnosis and next step?",
)

print(json.dumps(result, indent=4))

import json
import os

from dotenv import load_dotenv
from swarms_client import SwarmsClient

load_dotenv()

client = SwarmsClient(
    api_key=os.getenv("SWARMS_API_KEY"),
    base_url="https://api.swarms.world",
    timeout=1000,
)

result = client.swarms.run(
    name="Markets Hierarchical Swarm",
    description="Director coordinates an ETF analyst and a stocks analyst.",
    swarm_type="HierarchicalSwarm",
    task=(
        "Compare the outlook for the SPY ETF and NVDA stock for the next quarter. "
        "Highlight the strongest signal for each."
    ),
    max_loops=1,
    agents=[
        {
            "agent_name": "ETF Analyst",
            "description": "Analyzes broad-market and sector ETFs.",
            "system_prompt": (
                "You are an ETF analyst. Given a ticker, summarize the fund's "
                "exposure, recent flows, and near-term outlook in under 200 words."
            ),
            "model_name": "anthropic/claude-opus-4-8",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 4096,
        },
        {
            "agent_name": "Stocks Analyst",
            "description": "Analyzes individual equities.",
            "system_prompt": (
                "You are an equity analyst. Given a ticker, summarize the company's "
                "fundamentals, catalysts, and near-term outlook in under 200 words."
            ),
            "model_name": "anthropic/claude-opus-4-8",
            "role": "worker",
            "max_loops": 1,
            "max_tokens": 4096,
        },
    ],
)

print(json.dumps(result, indent=4))