from swarms import Agent

# Initialize a new agent
agent = Agent(
    model_name="gpt-4o-mini", # Specify the LLM
    max_loops=1,              # Set the number of interactions
    interactive=True,         # Enable interactive mode for real-time feedback
)

# Run the agent with a task
agent.run("What are the key benefits of using a multi-agent system?")

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)

from swarms.structs.auto_swarm_builder import AutoSwarmBuilder
import json

# Initialize the AutoSwarmBuilder
swarm = AutoSwarmBuilder(
    name="My Swarm",
    description="A swarm of agents",
    verbose=True,
    max_loops=1,
    return_agents=True,
    model_name="gpt-4o-mini",
)

# Let the builder automatically create agents and workflows
result = swarm.run(
    task="Create an accounting team to analyze crypto transactions, "
         "there must be 5 agents in the team with extremely extensive prompts. "
         "Make the prompts extremely detailed and specific and long and comprehensive. "
         "Make sure to include all the details of the task in the prompts."
)

# The result contains the generated agents and their configurations
print(json.dumps(result, indent=4))

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)

from swarms import Agent, ConcurrentWorkflow

# Create agents for different analysis tasks
market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="Analyze market trends and provide insights on the given topic.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

financial_analyst = Agent(
    agent_name="Financial-Analyst", 
    system_prompt="Provide financial analysis and recommendations on the given topic.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="Assess risks and provide risk management strategies for the given topic.",
    model_name="gpt-4o-mini", 
    max_loops=1,
)

# Create concurrent workflow
concurrent_workflow = ConcurrentWorkflow(
    agents=[market_analyst, financial_analyst, risk_analyst],
    max_loops=1,
)

# Run all agents concurrently on the same task
results = concurrent_workflow.run(
    "Analyze the potential impact of AI technology on the healthcare industry"
)

print(results)

from swarms import Agent, AgentRearrange

# Define agents
researcher = Agent(agent_name="researcher", model_name="gpt-4o-mini")
writer = Agent(agent_name="writer", model_name="gpt-4o-mini")
editor = Agent(agent_name="editor", model_name="gpt-4o-mini")

# Define a flow: researcher sends work to both writer and editor simultaneously
# This is a one-to-many relationship
flow = "researcher -> writer, editor"

# Create the rearrangement system
rearrange_system = AgentRearrange(
    agents=[researcher, writer, editor],
    flow=flow,
)

# Run the swarm
outputs = rearrange_system.run("Analyze the impact of AI on modern cinema.")
print(outputs)

from swarms import Agent
from swarms.structs.swarm_router import SwarmRouter, SwarmType

# Define a few generic agents
writer = Agent(agent_name="Writer", system_prompt="You are a creative writer.", model_name="gpt-4o-mini")
editor = Agent(agent_name="Editor", system_prompt="You are an expert editor for stories.", model_name="gpt-4o-mini")
reviewer = Agent(agent_name="Reviewer", system_prompt="You are a final reviewer who gives a score.", model_name="gpt-4o-mini")

# The agents and task will be the same for all examples
agents = [writer, editor, reviewer]
task = "Write a short story about a robot who discovers music."

# --- Example 1: SequentialWorkflow ---
# Agents run one after another in a chain: Writer -> Editor -> Reviewer.
print("Running a Sequential Workflow...")
sequential_router = SwarmRouter(swarm_type=SwarmType.SequentialWorkflow, agents=agents)
sequential_output = sequential_router.run(task)
print(f"Final Sequential Output:\n{sequential_output}\n")

# --- Example 2: ConcurrentWorkflow ---
# All agents receive the same initial task and run at the same time.
print("Running a Concurrent Workflow...")
concurrent_router = SwarmRouter(swarm_type=SwarmType.ConcurrentWorkflow, agents=agents)
concurrent_outputs = concurrent_router.run(task)
# This returns a dictionary of each agent's output
for agent_name, output in concurrent_outputs.items():
    print(f"Output from {agent_name}:\n{output}\n")

# --- Example 3: MixtureOfAgents ---
# All agents run in parallel, and a special 'aggregator' agent synthesizes their outputs.
print("Running a Mixture of Agents Workflow...")
aggregator = Agent(
    agent_name="Aggregator",
    system_prompt="Combine the story, edits, and review into a final document.",
    model_name="gpt-4o-mini"
)
moa_router = SwarmRouter(
    swarm_type=SwarmType.MixtureOfAgents,
    agents=agents,
    aggregator_agent=aggregator, # MoA requires an aggregator
)
aggregated_output = moa_router.run(task)
print(f"Final Aggregated Output:\n{aggregated_output}\n")

from swarms import Agent, MixtureOfAgents

# Define expert agents
financial_analyst = Agent(agent_name="FinancialAnalyst", system_prompt="Analyze financial data.", model_name="gpt-4o-mini")
market_analyst = Agent(agent_name="MarketAnalyst", system_prompt="Analyze market trends.", model_name="gpt-4o-mini")
risk_analyst = Agent(agent_name="RiskAnalyst", system_prompt="Analyze investment risks.", model_name="gpt-4o-mini")

# Define the aggregator agent
aggregator = Agent(
    agent_name="InvestmentAdvisor",
    system_prompt="Synthesize the financial, market, and risk analyses to provide a final investment recommendation.",
    model_name="gpt-4o-mini"
)

# Create the MoA swarm
moa_swarm = MixtureOfAgents(
    agents=[financial_analyst, market_analyst, risk_analyst],
    aggregator_agent=aggregator,
)

# Run the swarm
recommendation = moa_swarm.run("Should we invest in NVIDIA stock right now?")
print(recommendation)

from swarms import Agent, GroupChat

# Define agents for a debate
tech_optimist = Agent(agent_name="TechOptimist", system_prompt="Argue for the benefits of AI in society.", model_name="gpt-4o-mini")
tech_critic = Agent(agent_name="TechCritic", system_prompt="Argue against the unchecked advancement of AI.", model_name="gpt-4o-mini")

# Create the group chat
chat = GroupChat(
    agents=[tech_optimist, tech_critic],
    max_loops=4, # Limit the number of turns in the conversation
)

# Run the chat with an initial topic
conversation_history = chat.run(
    "Let's discuss the societal impact of artificial intelligence."
)

# Print the full conversation
for message in conversation_history:
    print(f"[{message['agent_name']}]: {message['content']}")

from swarms import Agent, HierarchicalSwarm

# Define specialized worker agents
content_strategist = Agent(
    agent_name="Content-Strategist",
    system_prompt="You are a senior content strategist. Develop comprehensive content strategies, editorial calendars, and content roadmaps.",
    model_name="gpt-4o-mini"
)

creative_director = Agent(
    agent_name="Creative-Director", 
    system_prompt="You are a creative director. Develop compelling advertising concepts, visual directions, and campaign creativity.",
    model_name="gpt-4o-mini"
)

seo_specialist = Agent(
    agent_name="SEO-Specialist",
    system_prompt="You are an SEO expert. Conduct keyword research, optimize content, and develop organic growth strategies.",
    model_name="gpt-4o-mini"
)

brand_strategist = Agent(
    agent_name="Brand-Strategist",
    system_prompt="You are a brand strategist. Develop brand positioning, identity systems, and market differentiation strategies.",
    model_name="gpt-4o-mini"
)

# Create the hierarchical swarm with a director
marketing_swarm = HierarchicalSwarm(
    name="Marketing-Team-Swarm",
    description="A comprehensive marketing team with specialized agents coordinated by a director",
    agents=[content_strategist, creative_director, seo_specialist, brand_strategist],
    max_loops=2,  # Allow for feedback and refinement
    verbose=True
)

# Run the swarm on a complex marketing challenge
result = marketing_swarm.run(
    "Develop a comprehensive marketing strategy for a new SaaS product launch. "
    "The product is a project management tool targeting small to medium businesses. "
    "Coordinate the team to create content strategy, creative campaigns, SEO optimization, "
    "and brand positioning that work together cohesively."
)

print(result)

from swarms import HeavySwarm

# Initialize the HeavySwarm with configuration
swarm = HeavySwarm(
    worker_model_name="gpt-4o-mini",  # Model for worker agents
    question_agent_model_name="gpt-4o-mini",  # Model for question generation
    loops_per_agent=1,  # Number of loops per agent
    show_dashboard=True,  # Enable real-time dashboard
)

# Run complex analysis task
result = swarm.run(
    "Provide 3 publicly traded biotech companies that are currently trading below their cash value. "
    "For each company identified, provide available data or projections for the next 6 months, "
    "including any relevant financial metrics, upcoming catalysts, or events that could impact valuation. "
    "Present your findings in a clear, structured format with ticker symbols, current prices, "
    "cash values, and percentage differences."
)

print(result)