Creating Agents
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 theAgent class with minimal configuration:
Agent Initialization Patterns
Pattern 1: Simple Agent
For quick prototyping and simple tasks:Pattern 2: Named Agent with Description
For better organization and clarity:Pattern 3: Agent with System Prompt
For specialized behavior and domain expertise:Pattern 4: Interactive Agent
For conversational interfaces:Pattern 5: Autonomous Agent
For complex, multi-step reasoning:Pattern 6: Agent with Fallback Models
For reliability and cost optimization:Pattern 7: Agent from Marketplace Prompt
Load prompts from the Swarms Marketplace:Best Practices
1. Always Name Your Agents
2. Use Descriptive System Prompts
3. Set Appropriate Max Loops
4. Enable Verbose Mode During Development
5. Use Autosave for Important Work
6. Set Environment Variables
Common Patterns
Research Agent
Writing Agent
Code Generation Agent
Next Steps
Agent Configuration
Learn about all configuration parameters
Agent Memory
Configure memory and conversation history
Agent Tools
Add tools to extend agent capabilities
Structured Outputs
Get structured responses from agents
Reference
For the complete API reference, see the Agent class documentation. Location in source:swarms/structs/agent.py:205