Understanding Agents Agents are the core building blocks of PraisonAI. Each agent is an autonomous AI entity with specific roles, goals, and capabilities.
Single Agent vs Multi-Agent Use Case Class Code One agent, one task AgentAgent(instructions="...").start()Multiple agents working together AgentTeamAgentTeam(agents=[...]).start()
Agent Architecture Key Components
Role & Goal Defines the agent’s purpose and objectives through role definition and specific goals
Capabilities Tools and functions available to the agent for task execution
Memory Context retention and learning capabilities across interactions
Language Model The underlying AI model powering the agent’s intelligence
Component Details Role and Goal Clear role and goal definitions are crucial for optimal agent performance.
Component Description Example Role Agent’s function and expertise Research Analyst, Code Developer Goal Specific objectives to achieve Analyze market trends, Generate reports Backstory Contextual background Expert with 10 years of experience
Capabilities agent = Agent ( name = " Researcher " , role = " Senior Research Analyst " , # Part of System Prompt goal = " Uncover cutting-edge developments in AI " , # Part of System Prompt backstory = " You are an expert at a technology research group " , # Part of System Prompt llm = " gpt-4o " )
Install PraisonAI
Install the core package: pip install praisonaiagents
Configure Environment
export OPENAI_API_KEY = your_openai_key
Generate your OpenAI API key from OpenAI
Use other LLM providers like Ollama, Anthropic, Groq, Google, etc. Please refer to the Models for more information.
Create Agent
Create app.py: from praisonaiagents import Agent agent = Agent ( instructions = " Your are a helpful AI assistant " ) # System Prompt: defines WHO the agent is agent . start ( " Write a movie script about a robot in Mars " ) # User Prompt: defines WHAT to do
from praisonaiagents import Agent , AgentTeam research_agent = Agent ( instructions = " Research about AI " ) # System Prompt summarise_agent = Agent ( instructions = " Summarise research agent's findings " ) # System Prompt agents = AgentTeam ( agents =[ research_agent , summarise_agent ]) agents . start () # User Prompt (uses task descriptions)
Start Agents
Execute your script: You should see:
Agent initialization
Agents progress
Final results
Generated report
Agent Types
Basic Agent
Perfect for straightforward tasks and direct interactions
Single-purpose focus
Direct user interaction
Limited tool set
Ideal for simple tasks
Specialized Agent
Experts in specific domains with advanced capabilities
Domain expertise
Advanced capabilities
Custom tools
Deep knowledge base
Collaborative Agent
Designed for team-based operations and complex workflows
Team interaction
Task delegation
Shared context
Coordinated actions
Best Practices Always implement proper error handling and resource management in your agent configurations.
Agent Design
Role Definition
Define clear, specific roles for each agent
Goal Setting
Set specific, measurable goals
Tool Selection
Choose relevant tools for the task
Memory Setup
Configure appropriate memory settings
Agent Interaction
Communication
Establish clear communication protocols
Delegation
Define explicit delegation rules
Error Handling
Implement robust error handling
Resource Management
Set up efficient resource allocation
Async Capabilities
Key Features Async Support
Parallel Execution
Integration
async def main (): agent = Agent ( name = " AsyncAgent " ) result = await agent . aprocess_task ()
Full async/await support
Non-blocking operations
Enhanced performance
async def parallel_tasks (): tasks = [ agent1 . task (), agent2 . task ()] results = await asyncio . gather ( * tasks )
Parallel task execution
Efficient resource usage
Improved throughput
@ agent . async_tool async def custom_tool (): async with aiohttp . ClientSession () as session : # Async operations pass
Async tool integration
Callback support
Mixed sync/async operations
Advanced Features
Memory Management
Short-term conversation memory
Long-term knowledge retention
Context preservation
Tool Integration
Custom tool development
External API integration
Resource access control
Async Support Agents now support asynchronous operations through the following methods:
achat: Async version of the chat methodastart: Async version of start methodaexecute_task: Async version of execute_task methodarun_task: Async version of run_task methodarun_all_tasks: Async version of run_all_tasks method
Example Usage: import asyncio from praisonaiagents import Agent , Task , AgentTeam async def main (): # Create an async agent async_agent = Agent ( name = " AsyncAgent " , role = " Async Task Specialist " , goal = " Perform async operations " , backstory = " Expert in async operations " , tools =[ async_search_tool ], # Your async tool ) # Create an async task async_task = Task ( description = " Perform async operation " , expected_output = " Async result " , agent = async_agent , async_execution = True # Enable async execution ) # Create and start agents with async support agents = AgentTeam ( agents =[ async_agent ], tasks =[ async_task ], ) # Start async execution result = await agents . astart () print ( result ) # Run the async main function if __name__ == " __main__ " : asyncio . run ( main ())
Key Features:
Full async/await support
Parallel task execution
Async tool integration
Async callback support
Mixed sync/async operations
Next Steps
Create Your First Agent Follow our quickstart guide to create your first agent
API Reference Explore the complete Agent API documentation
