Building Agents with Anthropic

pip install -U swarms

export ANTHROPIC_API_KEY="sk-ant-..."

ANTHROPIC_API_KEY="sk-ant-..."
WORKSPACE_DIR="agent_workspace"

from swarms import Agent

agent = Agent(
    agent_name="Claude-Agent",
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

print(agent.run("Summarize the history of the transformer architecture in three paragraphs."))

from swarms import Agent

agent = Agent(
    agent_name="Fable-5-Researcher",
    model_name="anthropic/claude-fable-5",
    thinking_tokens=4096,
    reasoning_effort="high",
    temperature=None,              # required
    top_p=None,                    # required
    tools_list_dictionary=None,    # required — tools not supported
    max_loops=1,
)

print(agent.run("Compare the architectural choices behind Mamba, RWKV, and Transformer++."))

from swarms import Agent

def get_weather(city: str) -> str:
    """Return the current weather for a city."""
    return f"{city}: 21°C, partly cloudy"

agent = Agent(
    agent_name="Sonnet-Assistant",
    model_name="claude-sonnet-4-6",
    tools=[get_weather],
    temperature=0.5,
    max_loops=3,
)

print(agent.run("What's the weather in Tokyo right now?"))

from swarms import Agent

agent = Agent(
    agent_name="Haiku-Classifier",
    model_name="claude-haiku-4-5",
    system_prompt="Classify each input as one of: support, sales, billing, other. Reply with the label only.",
    max_loops=1,
)

print(agent.run("My subscription renewed but I was charged twice."))

from swarms import Agent

agent = Agent(
    agent_name="Deep-Thinker",
    model_name="claude-opus-4-8",
    thinking_tokens=8192,        # private reasoning budget
    reasoning_effort="high",     # "low" | "medium" | "high"
    max_loops=1,
)

print(agent.run("Prove that the sum of the first n odd integers equals n²."))

from swarms import Agent

agent = Agent(
    agent_name="Streaming-Claude",
    model_name="claude-sonnet-4-6",
    streaming_on=True,
    max_loops=1,
)

agent.run("Write a 200-word explanation of how diffusion models work.")

def on_token(token: str) -> None:
    print(token, end="", flush=True)

agent = Agent(
    agent_name="Callback-Claude",
    model_name="claude-sonnet-4-6",
    streaming_callback=on_token,
    max_loops=1,
)

agent.run("Explain WebAssembly to a backend engineer.")

from swarms import Agent

agent = Agent(
    agent_name="Claude-Vision",
    model_name="claude-sonnet-4-6",
    max_loops=1,
)

result = agent.run(
    task="What's in this image? Be specific.",
    img="path/to/photo.jpg",
)
print(result)

import yfinance as yf
from swarms import Agent

def get_stock_price(ticker: str) -> str:
    """Fetch the current stock price for a given ticker symbol."""
    data = yf.Ticker(ticker)
    return f"{ticker}: ${data.fast_info['last_price']:.2f}"

def get_market_cap(ticker: str) -> str:
    """Fetch the market capitalization for a given ticker."""
    data = yf.Ticker(ticker)
    cap = data.fast_info.get("market_cap")
    return f"{ticker} cap: ${cap:,.0f}" if cap else f"{ticker}: unavailable"

agent = Agent(
    agent_name="Equity-Researcher",
    model_name="claude-sonnet-4-6",
    tools=[get_stock_price, get_market_cap],
    max_loops=3,
)

print(agent.run("Compare NVDA, AMD, and INTC on price and market cap."))

from swarms import Agent, SequentialWorkflow

triage = Agent(
    agent_name="Triage",
    model_name="claude-haiku-4-5",                 # cheap & fast
    system_prompt="Classify and route the user request.",
    max_loops=1,
)

researcher = Agent(
    agent_name="Researcher",
    model_name="claude-sonnet-4-6",                # balanced
    system_prompt="Gather all relevant context and data.",
    max_loops=2,
)

synthesizer = Agent(
    agent_name="Synthesizer",
    model_name="anthropic/claude-fable-5",         # frontier reasoning
    thinking_tokens=4096,
    reasoning_effort="high",
    temperature=None,
    top_p=None,
    tools_list_dictionary=None,
    system_prompt="Synthesize the research into a definitive answer.",
    max_loops=1,
)

pipeline = SequentialWorkflow(agents=[triage, researcher, synthesizer], max_loops=1)
print(pipeline.run("Evaluate the case for moving our backend from Postgres to a distributed SQL engine."))

from swarms import Agent

agent = Agent(
    agent_name="Production-Claude",
    model_name="claude-sonnet-4-6",
    max_loops=1,
    persistent_memory=True,        # survive process restarts
    context_compression=True,      # auto-summarize at 90% of context
    context_length=200_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,                # silence console output in services
)