> ## 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. # Creating Agents > Learn how to create and initialize agents in the Swarms framework Agents are the fundamental building blocks of the Swarms framework. An agent is an autonomous entity powered by an LLM with tools, memory, and the ability to execute complex tasks. ## Basic Agent Creation The simplest way to create an agent is to instantiate the `Agent` class with minimal configuration: ```python theme={null} from swarms import Agent # Create a basic agent agent = Agent( agent_name="my-agent", model_name="gpt-5.4", max_loops=1, ) # Run the agent response = agent.run("What are the key benefits of using a multi-agent system?") print(response) ``` ## Agent Initialization Patterns ### Pattern 1: Simple Agent For quick prototyping and simple tasks: ```python theme={null} from swarms import Agent agent = Agent( model_name="gpt-5.4", max_loops=1, ) response = agent.run("Generate a report on the financials.") print(response) ``` ### Pattern 2: Named Agent with Description For better organization and clarity: ```python theme={null} agent = Agent( agent_name="Financial-Analyst", agent_description="An expert financial analyst specializing in market analysis and reporting", model_name="claude-sonnet-4-6", max_loops=1, verbose=True, ) ``` ### Pattern 3: Agent with System Prompt For specialized behavior and domain expertise: ```python theme={null} SYSTEM_PROMPT = """ You are a senior financial analyst with expertise in: - Financial statement analysis - Market research and competitor analysis - Investment recommendations - Risk assessment Provide detailed, data-driven insights with proper citations. """ agent = Agent( agent_name="Financial-Expert", system_prompt=SYSTEM_PROMPT, model_name="claude-sonnet-4-6", max_loops=1, ) ``` ### Pattern 4: Interactive Agent For conversational interfaces: ```python theme={null} agent = Agent( agent_name="Assistant", model_name="gpt-5.4", max_loops=5, interactive=True, # Enable interactive mode user_name="User", custom_exit_command="exit", ) # Agent will prompt for user input in a loop agent.run("Hello! How can I help you today?") ``` ### Pattern 5: Autonomous Agent For complex, multi-step reasoning: ```python theme={null} agent = Agent( agent_name="Research-Agent", model_name="claude-sonnet-4-6", max_loops="auto", # Autonomous loop mode reasoning_prompt_on=True, dynamic_temperature_enabled=True, verbose=True, ) response = agent.run( "Research the latest trends in AI and create a comprehensive report" ) ``` ### Pattern 6: Agent with Fallback Models For reliability and cost optimization: ```python theme={null} agent = Agent( agent_name="Reliable-Agent", fallback_models=[ "claude-sonnet-4-6", # Primary model "gpt-5.4", # First fallback "gpt-3.5-turbo" # Final fallback ], max_loops=1, ) # Agent will automatically try fallback models if primary fails response = agent.run("Generate a report") ``` ### Pattern 7: Agent from Marketplace Prompt Load prompts from the Swarms Marketplace: ```python theme={null} import os # Set your Swarms API key os.environ["SWARMS_API_KEY"] = "your-api-key" agent = Agent( model_name="claude-sonnet-4-6", marketplace_prompt_id="550e8400-e29b-41d4-a716-446655440000", # UUID from marketplace max_loops=1, ) # System prompt is automatically loaded from the marketplace response = agent.run("Execute the marketplace prompt task") ``` ## Best Practices ### 1. Always Name Your Agents ```python theme={null} # Good agent = Agent( agent_name="Financial-Analyst", agent_description="Expert in financial analysis", model_name="claude-sonnet-4-6", ) # Avoid agent = Agent(model_name="claude-sonnet-4-6") # Uses default name ``` ### 2. Use Descriptive System Prompts ```python theme={null} # Good - Specific and detailed system_prompt = """ You are a healthcare data analyst specializing in: 1. Patient data analysis 2. Medical coding (ICD-10, CPT) 3. HIPAA compliance 4. Clinical research metrics Always maintain patient privacy and follow HIPAA guidelines. """ # Avoid - Too vague system_prompt = "You are a helpful assistant." ``` ### 3. Set Appropriate Max Loops ```python theme={null} # For simple tasks agent = Agent(max_loops=1) # Single response # For multi-step reasoning agent = Agent(max_loops=5) # Fixed iterations # For autonomous tasks agent = Agent(max_loops="auto") # Dynamic execution ``` ### 4. Enable Verbose Mode During Development ```python theme={null} agent = Agent( model_name="gpt-5.4", verbose=True, # See detailed logs print_on=True, # Print agent responses ) ``` ### 5. Use Autosave for Important Work ```python theme={null} agent = Agent( agent_name="Research-Agent", model_name="claude-sonnet-4-6", autosave=True, # Automatically save state saved_state_path="./agent_states/", ) ``` ### 6. Set Environment Variables ```python theme={null} import os # Set API keys os.environ["OPENAI_API_KEY"] = "your-api-key" os.environ["WORKSPACE_DIR"] = "agent_workspace" # Create agent agent = Agent( model_name="claude-sonnet-4-6", max_loops=1, ) ``` ## Common Patterns ### Research Agent ```python theme={null} research_agent = Agent( agent_name="Researcher", system_prompt="You are an expert researcher. Provide detailed, cited information.", model_name="claude-sonnet-4-6", max_loops=3, temperature=0.5, verbose=True, ) ``` ### Writing Agent ```python theme={null} writer_agent = Agent( agent_name="Writer", system_prompt="You are a professional writer. Create engaging, well-structured content.", model_name="claude-sonnet-4-6", max_loops=1, temperature=0.7, ) ``` ### Code Generation Agent ```python theme={null} code_agent = Agent( agent_name="Code-Generator", system_prompt="You are an expert software engineer. Write clean, documented code.", model_name="claude-sonnet-4-6", max_loops=2, temperature=0.3, # Lower temperature for more deterministic output ) ``` ## Next Steps Learn about all configuration parameters Configure memory and conversation history Add tools to extend agent capabilities Get structured responses from agents ## Reference For the complete API reference, see the [Agent class documentation](/api/agent). Location in source: `swarms/structs/agent.py:205`