Kye Gomez
3 weeks ago
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# Swarms x Browser Use
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# Sequential Workflow Example
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- Import required modules like `Agent` `SequentialWorkflow`
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!!! abstract "Overview"
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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.
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- Configure your agents first with their model provider, name, description, role, and more!
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## Prerequisites
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- Set your api keys for your model provider in the `.env` file such as `OPENAI_API_KEY="sk-"` etc
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!!! info "Before You Begin"
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Make sure you have:
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- Conigure your `SequentialWorkflow`
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- Python 3.7+ installed
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## Install
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- A valid API key for your model provider
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- The Swarms package installed
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## Installation
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```bash
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pip3 install -U swarms
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```
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--------
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## Environment Setup
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!!! tip "API Key Configuration"
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Set your API key in the `.env` file:
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```bash
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OPENAI_API_KEY="your-api-key-here"
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```
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## Main Code
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## Code Implementation
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### Import Required Modules
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```python
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from swarms import Agent, SequentialWorkflow
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```
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### Configure Agents
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# Core Legal Agent Definitions with enhanced system prompts
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litigation_agent = Agent(
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agent_name="Alex Johnson", # Human name for the Litigator Agent
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!!! example "Legal Agent Configuration"
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Here's how to set up your specialized legal agents:
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```python
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# Litigation Agent
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litigation_agent = Agent(
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agent_name="Alex Johnson",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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)
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corporate_agent = Agent(
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agent_name="Emily Carter", # Human name for the Corporate Attorney Agent
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# Corporate Attorney Agent
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corporate_agent = Agent(
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agent_name="Emily Carter",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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)
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ip_agent = Agent(
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agent_name="Michael Smith", # Human name for the IP Attorney Agent
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# IP Attorney Agent
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ip_agent = Agent(
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agent_name="Michael Smith",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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)
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```
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### Initialize Sequential Workflow
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!!! example "Workflow Setup"
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Configure the SequentialWorkflow with your agents:
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swarm = SequentialWorkflow(
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```python
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swarm = SequentialWorkflow(
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agents=[litigation_agent, corporate_agent, ip_agent],
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name="litigation-practice",
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description="Handle all aspects of litigation with a focus on thorough legal analysis and effective case management.",
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)
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)
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```
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swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.")
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```
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### Run the Workflow
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!!! example "Execute the Workflow"
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Start the sequential workflow:
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```python
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swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.")
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```
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## Complete Example
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!!! success "Full Implementation"
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Here's the complete code combined:
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```python
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from swarms import Agent, SequentialWorkflow
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# Core Legal Agent Definitions with enhanced system prompts
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litigation_agent = Agent(
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agent_name="Alex Johnson",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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corporate_agent = Agent(
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agent_name="Emily Carter",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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ip_agent = Agent(
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agent_name="Michael Smith",
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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.",
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model_name="gpt-4o-mini",
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max_loops=1,
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)
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# Initialize and run the workflow
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swarm = SequentialWorkflow(
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agents=[litigation_agent, corporate_agent, ip_agent],
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name="litigation-practice",
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description="Handle all aspects of litigation with a focus on thorough legal analysis and effective case management.",
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)
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swarm.run("Create a report on how to patent an all-new AI invention and what platforms to use and more.")
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```
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## Agent Roles
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!!! info "Specialized Legal Agents"
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| Agent | Role | Expertise |
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|-------|------|-----------|
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| Alex Johnson | Litigator | Lawsuit navigation, case strategy |
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| Emily Carter | Corporate Attorney | Business law, compliance |
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| Michael Smith | IP Attorney | Patents, trademarks, copyrights |
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## Configuration Options
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!!! info "Key Parameters"
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `agent_name` | Human-readable name for the agent | Required |
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| `system_prompt` | Detailed role description and expertise | Required |
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| `model_name` | LLM model to use | "gpt-4o-mini" |
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| `max_loops` | Maximum number of processing loops | 1 |
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## Next Steps
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!!! tip "What to Try Next"
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1. Experiment with different agent roles and specializations
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2. Modify the system prompts to create different expertise areas
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3. Add more agents to the workflow for complex tasks
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4. Try different model configurations
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## Troubleshooting
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!!! warning "Common Issues"
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- Ensure your API key is correctly set in the `.env` file
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- Check that all required dependencies are installed
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- Verify that your model provider's API is accessible
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- Monitor agent responses for quality and relevance
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