from swarms import Agent, SequentialWorkflow

# Agent 1: The Researcher
researcher = Agent(
    agent_name="Researcher",
    system_prompt="Your job is to research the provided topic and provide a detailed summary.",
    model_name="gpt-4o-mini",
)

# Agent 2: The Writer
writer = Agent(
    agent_name="Writer",
    system_prompt="Your job is to take the research summary and write a beautiful, engaging blog post about it.",
    model_name="gpt-4o-mini",
)

# Create a sequential workflow where the researcher's output feeds into the writer's input
workflow = SequentialWorkflow(agents=[researcher, writer])

# Run the workflow on a task
final_post = workflow.run("The history and future of artificial intelligence")
print(final_post)

use std::env;
use anyhow::Result;
use swarms_rs::llm::provider::openai::OpenAI;
use swarms_rs::structs::concurrent_workflow::ConcurrentWorkflow;

#[tokio::main]
async fn main() -> Result<()> {
    let client = OpenAI::from_url(base_url, api_key)
        .set_model("deepseek-chat");

    // Create specialized trading agents
    let market_analysis_agent = client
        .agent_builder()
        .agent_name("Market Analysis Agent")
        .system_prompt("You are a market analysis specialist...")
        .max_loops(1)
        .temperature(0.2)
        .build();

    // Create concurrent workflow
    let workflow = ConcurrentWorkflow::builder()
        .name("Trading Strategy Workflow")
        .agents(vec![
            Box::new(market_analysis_agent),
            Box::new(trade_strategy_agent),
            Box::new(risk_assessment_agent),
        ])
        .build();

    let result = workflow.run("BTC/USD analysis...").await?;
    println!("{}", serde_json::to_string_pretty(&result)?);
    Ok(())
}