Building Agents with Gemini

pip install -U swarms

export GEMINI_API_KEY="..."

GEMINI_API_KEY="..."
WORKSPACE_DIR="agent_workspace"

from swarms import Agent

agent = Agent(
    agent_name="Gemini-Agent",
    model_name="gemini/gemini-2.5-pro",
    max_loops=1,
)

print(agent.run("Summarize the difference between RAG and fine-tuning in three paragraphs."))

from swarms import Agent

agent = Agent(
    agent_name="Gemini-Pro-Researcher",
    model_name="gemini/gemini-2.5-pro",
    system_prompt="You are a senior research analyst. Cite evidence and reason carefully.",
    context_length=1_000_000,
    max_loops=1,
)

print(agent.run("Walk me through the architectural choices behind Gemini 2.5's mixture-of-experts design."))

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="Gemini-Flash-Assistant",
    model_name="gemini/gemini-2.5-flash",
    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="Gemini-Triage",
    model_name="gemini/gemini-2.5-flash-lite",
    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="Gemini-Vision",
    model_name="gemini/gemini-2.5-pro",
    max_loops=1,
)

result = agent.run(
    task="Describe what's in this image and identify any text you see.",
    img="path/to/screenshot.png",
)
print(result)

from swarms import Agent

# Load a large document
with open("annual_report.pdf.txt") as f:
    document = f.read()

agent = Agent(
    agent_name="Document-Analyst",
    model_name="gemini/gemini-2.5-pro",
    context_length=2_000_000,
    max_loops=1,
)

print(agent.run(
    f"Here is our annual report. Identify the top three financial risks and quote the relevant sections.\n\n{document}"
))

from swarms import Agent

agent = Agent(
    agent_name="Streaming-Gemini",
    model_name="gemini/gemini-2.5-flash",
    streaming_on=True,
    max_loops=1,
)

agent.run("Write a 200-word explanation of how attention works in transformers.")

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

agent = Agent(
    agent_name="Callback-Gemini",
    model_name="gemini/gemini-2.5-flash",
    streaming_callback=on_token,
    max_loops=1,
)

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

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="gemini/gemini-2.5-flash",
    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="gemini/gemini-2.5-flash-lite",     # cheap & fast
    system_prompt="Classify and route the user request.",
    max_loops=1,
)

researcher = Agent(
    agent_name="Researcher",
    model_name="gemini/gemini-2.5-flash",          # balanced
    system_prompt="Gather all relevant context and data.",
    max_loops=2,
)

analyst = Agent(
    agent_name="Analyst",
    model_name="gemini/gemini-2.5-pro",            # frontier
    system_prompt="Reason carefully and produce the final analysis.",
    context_length=1_000_000,
    max_loops=1,
)

pipeline = SequentialWorkflow(agents=[triage, researcher, analyst], max_loops=1)
print(pipeline.run("Evaluate whether we should self-host Llama 3.3 or stay on managed APIs."))

from swarms import Agent

agent = Agent(
    agent_name="Production-Gemini",
    model_name="gemini/gemini-2.5-flash",
    max_loops=1,
    persistent_memory=True,        # survive process restarts
    context_compression=True,      # auto-summarize at 90% of context
    context_length=1_000_000,
    autosave=True,
    retry_attempts=3,
    print_on=False,
)