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.
Learn how to create and run your first autonomous agent using the Swarms framework. An Agent is the fundamental building block of a swarm—an autonomous entity powered by an LLM + Tools + Memory.
Quick Start
Here’s the simplest way to create and run an agent:
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)
Understanding the Code
Let’s break down each component:
1. Import the Agent Class
The Agent class is the core building block for creating autonomous agents in Swarms.
2. Initialize the Agent
agent = Agent(
model_name="gpt-5.4", # The language model to use
max_loops="auto", # How many times to iterate on the task
interactive=True, # Enable real-time feedback
)
model_name: The LLM to power your agent. Swarms supports OpenAI, Anthropic, Groq, Ollama, and more.max_loops: Controls iteration behavior:
"auto": Agent decides when to stop based on task completion- Integer (e.g.,
1, 5): Fixed number of iterations
interactive: When True, provides real-time feedback during execution
3. Run the Agent
response = agent.run("What are the key benefits of using a multi-agent system?")
print(response)
run() method executes the agent with your task and returns the response.
Example Output
When you run this agent, you’ll see output similar to:
🤖 Agent: Agent
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Multi-agent systems offer several key benefits:
1. **Parallel Processing**: Multiple agents can work on different aspects of a problem simultaneously, significantly reducing overall execution time.
2. **Specialization**: Each agent can be optimized for specific tasks, leading to higher quality outputs than a single generalist agent.
3. **Scalability**: Add more agents to handle increased workload without redesigning the entire system.
4. **Resilience**: If one agent fails, others can continue working, making the system more fault-tolerant.
5. **Modularity**: Agents can be developed, tested, and deployed independently, simplifying maintenance and updates.
6. **Complex Problem Solving**: Different agents can approach problems from different angles, leading to more comprehensive solutions.
Customizing Your Agent
Here’s a more customized example with additional configuration:
from swarms import Agent
# Create a specialized research agent
research_agent = Agent(
agent_name="Research-Agent",
agent_description="An expert research agent specializing in technical analysis",
system_prompt="You are a research expert. Provide detailed, well-sourced analysis on any topic.",
model_name="gpt-5.4",
max_loops=3,
interactive=True,
verbose=True, # Show detailed logging
output_type="str", # Return as string
)
# Run the agent
response = research_agent.run(
"Explain the concept of attention mechanisms in transformers"
)
print(response)
Additional Configuration Options
agent_name: A unique identifier for your agentagent_description: Describes the agent’s purpose and capabilitiessystem_prompt: Instructions that define the agent’s behavior and personalityverbose: When True, shows detailed execution logsoutput_type: Controls the format of the response ("str", "json", "dict", etc.)
Working with Different Models
Swarms supports multiple LLM providers:
# OpenAI
agent_openai = Agent(model_name="claude-sonnet-4-6")
# Anthropic Claude
agent_claude = Agent(model_name="claude-sonnet-4-5")
# Groq
agent_groq = Agent(model_name="groq/llama-3.3-70b-versatile")
# Local with Ollama
agent_ollama = Agent(model_name="ollama/llama3.2")
Environment Setup
Make sure you have the required API keys set in your environment:
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export GROQ_API_KEY="your-groq-key"
.env file:
OPENAI_API_KEY="your-openai-key"
ANTHROPIC_API_KEY="your-anthropic-key"
WORKSPACE_DIR="agent_workspace"
Next Steps
Now that you’ve created your first agent, explore these advanced topics:
Common Patterns
Task-Specific Agent
# Create an agent specialized for code review
code_reviewer = Agent(
agent_name="Code-Reviewer",
system_prompt="You are an expert code reviewer. Analyze code for bugs, performance issues, and best practices.",
model_name="gpt-5.4",
max_loops=1,
)
review = code_reviewer.run("""
Review this Python function:
def calculate_total(items):
total = 0
for item in items:
total = total + item['price']
return total
""")
Autonomous Agent with Auto Loops
# Agent that iterates until task is complete
autonomous_agent = Agent(
agent_name="Autonomous-Researcher",
model_name="gpt-5.4",
max_loops="auto", # Will iterate until completion
system_prompt="Research thoroughly and provide comprehensive analysis.",
)
result = autonomous_agent.run(
"Research the latest developments in quantum computing and summarize the key findings"
)
Tips and Best Practices
- Start Simple: Begin with basic configurations and add complexity as needed
- Use Descriptive Names: Clear agent names and descriptions improve debugging
- Set Appropriate Loop Limits: Use
max_loops=1 for simple tasks, higher values or "auto" for complex ones
- Monitor Costs: Be mindful of API costs when using
max_loops="auto"
- Test Incrementally: Test your agent with simple tasks before moving to complex ones
Troubleshooting
Agent Not Responding
# Enable verbose mode to see detailed logs
agent = Agent(
model_name="gpt-5.4",
verbose=True, # This will show what's happening
)
API Key Errors
import os
# Verify your API key is set
if not os.getenv("OPENAI_API_KEY"):
raise ValueError("Please set OPENAI_API_KEY environment variable")
agent = Agent(model_name="gpt-5.4")
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