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Sequential Workflow Example

Overview

Learn how to create a sequential workflow with multiple specialized AI agents using the Swarms framework. This example demonstrates how to set up a legal practice workflow with different types of legal agents working in sequence.

Prerequisites

Before You Begin

Make sure you have:

  • Python 3.7+ installed

  • A valid API key for your model provider

  • The Swarms package installed

Installation

pip3 install -U swarms

Environment Setup

API Key Configuration

Set your API key in the .env file: 

OPENAI_API_KEY="your-api-key-here"

Code Implementation

Import Required Modules

from swarms import Agent, SequentialWorkflow

Configure Agents

Legal Agent Configuration

Here's how to set up your specialized legal agents:

# Litigation Agent
litigation_agent = Agent(
    agent_name="Alex Johnson",
    system_prompt="As a Litigator, you specialize in navigating the complexities of lawsuits. Your role involves analyzing intricate facts, constructing compelling arguments, and devising effective case strategies to achieve favorable outcomes for your clients.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Corporate Attorney Agent
corporate_agent = Agent(
    agent_name="Emily Carter",
    system_prompt="As a Corporate Attorney, you provide expert legal advice on business law matters. You guide clients on corporate structure, governance, compliance, and transactions, ensuring their business operations align with legal requirements.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# IP Attorney Agent
ip_agent = Agent(
    agent_name="Michael Smith",
    system_prompt="As an IP Attorney, your expertise lies in protecting intellectual property rights. You handle various aspects of IP law, including patents, trademarks, copyrights, and trade secrets, helping clients safeguard their innovations.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

Initialize Sequential Workflow

Workflow Setup

Configure the SequentialWorkflow with your agents:

swarm = SequentialWorkflow(
    agents=[litigation_agent, corporate_agent, ip_agent],
    name="litigation-practice",
    description="Handle all aspects of litigation with a focus on thorough legal analysis and effective case management.",
)

Run the Workflow

Execute the Workflow

Start the sequential workflow:

swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.")

Complete Example

Full Implementation

Here's the complete code combined:

from swarms import Agent, SequentialWorkflow

# Core Legal Agent Definitions with enhanced system prompts
litigation_agent = Agent(
    agent_name="Alex Johnson",
    system_prompt="As a Litigator, you specialize in navigating the complexities of lawsuits. Your role involves analyzing intricate facts, constructing compelling arguments, and devising effective case strategies to achieve favorable outcomes for your clients.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

corporate_agent = Agent(
    agent_name="Emily Carter",
    system_prompt="As a Corporate Attorney, you provide expert legal advice on business law matters. You guide clients on corporate structure, governance, compliance, and transactions, ensuring their business operations align with legal requirements.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

ip_agent = Agent(
    agent_name="Michael Smith",
    system_prompt="As an IP Attorney, your expertise lies in protecting intellectual property rights. You handle various aspects of IP law, including patents, trademarks, copyrights, and trade secrets, helping clients safeguard their innovations.",
    model_name="gpt-4o-mini",
    max_loops=1,
)

# Initialize and run the workflow
swarm = SequentialWorkflow(
    agents=[litigation_agent, corporate_agent, ip_agent],
    name="litigation-practice",
    description="Handle all aspects of litigation with a focus on thorough legal analysis and effective case management.",
)

swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.")

Agent Roles

Specialized Legal Agents

Agent Role Expertise
Alex Johnson Litigator Lawsuit navigation, case strategy
Emily Carter Corporate Attorney Business law, compliance
Michael Smith IP Attorney Patents, trademarks, copyrights

Configuration Options

Key Parameters

Parameter Description Default
agent_name Human-readable name for the agent Required
system_prompt Detailed role description and expertise Required
model_name LLM model to use "gpt-4o-mini"
max_loops Maximum number of processing loops 1

Next Steps

What to Try Next

  1. Experiment with different agent roles and specializations
  2. Modify the system prompts to create different expertise areas
  3. Add more agents to the workflow for complex tasks
  4. Try different model configurations

Troubleshooting

Common Issues

  • Ensure your API key is correctly set in the .env file

  • Check that all required dependencies are installed

  • Verify that your model provider's API is accessible

  • Monitor agent responses for quality and relevance