> ## Documentation Index
> Fetch the complete documentation index at: https://docs.swarms.world/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart Guide

> Create your first agent and swarm in minutes

## Get Started in Minutes

This guide will walk you through creating your first autonomous agent and multi-agent swarm using Swarms. By the end of this tutorial, you'll have a working agent and understand how to orchestrate multiple agents to work together.

<Note>
  Before you begin, make sure you've [installed Swarms](/installation) and [configured your environment](/environment-setup).
</Note>

## Your First Agent

An **Agent** is the fundamental building block of a swarm—an autonomous entity powered by an LLM + Tools + Memory.

<Steps>
  <Step title="Import the Agent class">
    Start by importing the `Agent` class from the swarms package:

    ```python theme={null}
    from swarms import Agent
    ```
  </Step>

  <Step title="Initialize your agent">
    Create a new agent with basic configuration:

    ```python theme={null}
    # Initialize a new agent
    agent = Agent(
        model_name="gpt-5.4",  # Specify the LLM
        max_loops="auto",          # Set the number of interactions
        interactive=True,          # Enable interactive mode for real-time feedback
    )
    ```

    **Key Parameters:**

    * `model_name`: The language model to use (e.g., "gpt-5.4", "claude-sonnet-4-5")
    * `max_loops`: Number of reasoning iterations ("auto" for automatic determination)
    * `interactive`: Enable real-time feedback and conversation
  </Step>

  <Step title="Run your agent">
    Execute a task with your agent:

    ```python theme={null}
    # Run the agent with a task
    response = agent.run(
        "What are the key benefits of using a multi-agent system?"
    )
    print(response)
    ```
  </Step>
</Steps>

<Accordion title="Complete code example">
  ```python theme={null}
  from swarms import Agent

  # Initialize a new agent
  agent = Agent(
      model_name="gpt-5.4",  # Specify the LLM
      max_loops="auto",          # Set the number of interactions
      interactive=True,          # Enable interactive mode for real-time feedback
  )

  # Run the agent with a task
  response = agent.run(
      "What are the key benefits of using a multi-agent system?"
  )
  print(response)
  ```
</Accordion>

## Your First Swarm: Multi-Agent Collaboration

A **Swarm** consists of multiple agents working together. Let's create a two-agent workflow for researching and writing a blog post.

<Steps>
  <Step title="Import required classes">
    Import both `Agent` and `SequentialWorkflow`:

    ```python theme={null}
    from swarms import Agent, SequentialWorkflow
    ```
  </Step>

  <Step title="Create specialized agents">
    Define two agents with specific roles:

    ```python theme={null}
    # 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-5.4",
    )

    # 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-5.4",
    )
    ```

    <Note>
      The `system_prompt` defines each agent's role and responsibilities. Be specific about what you want each agent to do.
    </Note>
  </Step>

  <Step title="Create a sequential workflow">
    Connect the agents in a pipeline where the researcher's output feeds into the writer's input:

    ```python theme={null}
    # Create a sequential workflow where the researcher's output feeds into the writer's input
    workflow = SequentialWorkflow(agents=[researcher, writer])
    ```
  </Step>

  <Step title="Run the workflow">
    Execute the workflow with a task:

    ```python theme={null}
    # Run the workflow on a task
    final_post = workflow.run(
        "The history and future of artificial intelligence"
    )
    print(final_post)
    ```

    The workflow will:

    1. Send the task to the Researcher agent
    2. The Researcher analyzes the topic and provides a summary
    3. The Writer receives the research and creates a blog post
    4. The final blog post is returned
  </Step>
</Steps>

<Accordion title="Complete swarm example">
  ```python theme={null}
  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-5.4",
  )

  # 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-5.4",
  )

  # 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)
  ```
</Accordion>

## Understanding the Workflow

The `SequentialWorkflow` executes agents in order, creating a pipeline where each agent builds upon the work of the previous one. This is ideal for:

* **Research → Analysis → Writing** workflows
* **Data Collection → Processing → Reporting** pipelines
* **Planning → Execution → Review** processes
* Any task with clear sequential dependencies

<Info>
  Want to run agents in parallel instead? Check out [ConcurrentWorkflow](https://docs.swarms.world/en/latest/swarms/structs/concurrent_workflow/) for simultaneous execution.
</Info>

## What You Can Do Next

<CardGroup cols={2}>
  <Card title="Add Tools to Your Agent" icon="wrench" href="https://docs.swarms.world/en/latest/swarms/examples/agent_with_tools/">
    Extend your agent's capabilities with external tools and APIs
  </Card>

  <Card title="Explore Multi-Agent Architectures" icon="diagram-project">
    Learn about HierarchicalSwarm, ConcurrentWorkflow, MixtureOfAgents, and more
  </Card>

  <Card title="Use Different Model Providers" icon="microchip" href="https://docs.swarms.world/en/latest/swarms/examples/model_providers/">
    Connect to OpenAI, Anthropic, Groq, Cohere, and other LLM providers
  </Card>

  <Card title="Build Production Applications" icon="rocket" href="https://docs.swarms.world/en/latest/swarms/structs/aop/">
    Deploy agents as distributed services with Agent Orchestration Protocol
  </Card>
</CardGroup>

## Common Use Cases

### Single Agent Examples

```python theme={null}
# Customer Support Agent
customer_support = Agent(
    agent_name="Customer-Support",
    system_prompt="You are a helpful customer support agent. Respond professionally and empathetically to customer inquiries.",
    model_name="gpt-5.4",
    max_loops=1,
)

response = customer_support.run(
    "I haven't received my order yet. What should I do?"
)
```

### Multi-Agent Examples

```python theme={null}
# Financial Analysis Swarm
from swarms import Agent, ConcurrentWorkflow

market_analyst = Agent(
    agent_name="Market-Analyst",
    system_prompt="Analyze market trends and provide insights.",
    model_name="gpt-5.4",
)

financial_analyst = Agent(
    agent_name="Financial-Analyst",
    system_prompt="Provide financial analysis and recommendations.",
    model_name="gpt-5.4",
)

risk_analyst = Agent(
    agent_name="Risk-Analyst",
    system_prompt="Assess risks and provide risk management strategies.",
    model_name="gpt-5.4",
)

# Run all analysts concurrently
concurrent_workflow = ConcurrentWorkflow(
    agents=[market_analyst, financial_analyst, risk_analyst]
)

results = concurrent_workflow.run(
    "Analyze the potential impact of AI technology on the healthcare industry"
)
```

## Troubleshooting

<AccordionGroup>
  <Accordion title="Agent not responding">
    Make sure your API keys are properly configured in your `.env` file. Check the [environment setup guide](/environment-setup) for details.
  </Accordion>

  <Accordion title="Model not found error">
    Verify that you're using a valid model name. See the [Model Providers documentation](https://docs.swarms.world/en/latest/swarms/examples/model_providers/) for supported models.
  </Accordion>

  <Accordion title="Rate limit errors">
    Consider using `max_loops=1` to reduce API calls, or implement retry logic with exponential backoff.
  </Accordion>
</AccordionGroup>

## Next Steps

Now that you've created your first agent and swarm, you're ready to:

1. **Customize your agents** with specific system prompts and configurations
2. **Add tools** to extend agent capabilities
3. **Explore different workflows** like HierarchicalSwarm and MixtureOfAgents
4. **Build production applications** with advanced features

<Info>
  Join our [Discord community](https://discord.gg/EamjgSaEQf) to get help, share your projects, and connect with other Swarms developers!
</Info>