Agents

Understanding Agents in PraisonAI

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

Agent Architecture

Key Components

Component Details

Role and Goal

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"
)

1. Install PraisonAI

   Install the core package:

   pip install praisonaiagents

2. 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.

3. 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)

4. Start Agents

   Execute your script:

   python app.py

   You should see: – Agent initialization – Agents progress – Final results – Generated report

Agent Types

Best Practices

Async Capabilities

async def main():
    agent = Agent(name="AsyncAgent")
    result = await agent.aprocess_task()

async def parallel_tasks():
    tasks = [agent1.task(), agent2.task()]
    results = await asyncio.gather(*tasks)

@agent.async_tool
async def custom_tool():
    async with aiohttp.ClientSession() as session:
        # Async operations
        pass

Advanced Features

Async Support

Agents now support asynchronous operations through the following methods:

• achat: Async version of the chat method
• astart: Async version of start method
• aexecute_task: Async version of execute_task method
• arun_task: Async version of run_task method
• arun_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