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What This Example Shows

  • How to point a single agent at Claude Opus 4.8
  • How to run a hierarchical multi-agent swarm on Opus 4.8
  • The correct model name to use (anthropic/claude-opus-4-8)
  • Why you should omit temperature for this model
Claude Opus 4.8 is Anthropic’s most capable reasoning model. It is wired into every multi-agent primitive on the Swarms platform — sequential workflows, hierarchical swarms, agent rearrange, group chats, mixture-of-agents, and more. Switching is a one-line change in your agent config.
Claude Opus 4.8 has deprecated the temperature parameter. Do not pass temperature in your agent_config for this model — the Swarms API will omit it automatically when the field is unset, and the provider’s own default will apply. Setting temperature to a number will be rejected by Anthropic with a 400.

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Step 1: Get Your API Key

  1. Visit https://swarms.world/platform/api-keys
  2. Sign in or create an account
  3. Generate a new API key
  4. Set it as an environment variable:
export SWARMS_API_KEY="your-api-key-here"

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Step 2: Install the Swarms Python Client

pip install swarms-client python-dotenv

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Single Agent on Claude Opus 4.8

A minimal single-agent call. Note the model_name is the only thing that changes from any of your existing agent configs — temperature is intentionally absent. 
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))
What changed from a typical agent call:
FieldBeforeWith Opus 4.8
model_namegpt-4.1, claude-haiku-4-5-20251001, etc.anthropic/claude-opus-4-8
temperature0.5 (or any float)omit the field entirely
Everything else—unchanged

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Multi-Agent Swarm on Claude Opus 4.8

The same model name plugs into every multi-agent architecture. This example uses a HierarchicalSwarm where an auto-generated director coordinates two worker analysts — one for ETFs, one for individual stocks. 
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))
For HierarchicalSwarm, you only need to define the worker agents — the director is automatically created and orchestrated by the framework. The director routes the task to each worker, collects their outputs, and synthesizes a final response.

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Using Opus 4.8 in Other Swarm Types

Every other swarm architecture takes the same model_name. Drop "model_name": "anthropic/claude-opus-4-8" into any agent inside any of these swarm configs:
  • SequentialWorkflow
  • ConcurrentWorkflow
  • AgentRearrange
  • MixtureOfAgents
  • GroupChat
  • MajorityVoting
  • CouncilAsAJudge
  • MultiAgentRouter
  • HeavySwarm
  • LLMCouncil
  • DebateWithJudge
  • BatchedGridWorkflow
  • RoundRobin
  • PlannerWorkerSwarm
  • auto

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Common Pitfalls

You set temperature in your agent_config. Remove the field entirely — the Swarms API will then omit it from the upstream Anthropic call. If you have legacy code that always sends a float, set it to None and rely on the Swarms API to strip it.
Use HierarchicalSwarm (correctly spelled). Earlier versions of the API accepted the misspelled HiearchicalSwarm; the current schema only accepts the correct spelling.
Opus 4.8 pricing follows Anthropic’s published rates and is reflected in the Swarms cost-tracking endpoints. See the pricing page for current rates.

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Next Steps