Agent Architecture

Understanding how AI agents are structured

Every PraisonAI agent has the same core structure. Understanding it helps you build better agents.

The 4 Core Components

Building an Agent

1. Instructions – Define Purpose

   from praisonaiagents import Agent
   
   agent = Agent(
       instructions="You are a helpful coding assistant"
   )

2. LLM – Choose the Brain

   agent = Agent(
       instructions="You are a helpful assistant",
       llm="gpt-4o"  # Or "claude-3", "gemini-pro", etc.
   )

3. Tools – Add Capabilities

   def calculate(expression: str) -> str:
       """Calculate a math expression"""
       return str(eval(expression))
   
   agent = Agent(
       instructions="You help with math",
       tools=[calculate]
   )

4. Memory – Remember Context

   agent = Agent(
       instructions="You are a personal assistant",
       memory=True  # Remember conversations
   )

Complete Example

from praisonaiagents import Agent

def search_web(query: str) -> str:
    """Search the web"""
    return f"Results for: {query}"

# Full agent with all components
agent = Agent(
    name="ResearchAssistant",
    instructions="You research topics and provide summaries",
    llm="gpt-4o",
    tools=[search_web],
    memory=True
)

agent.start("Research the latest AI trends")

The Agent Loop

Every agent follows this cycle:

Multi-Agent Architecture

Multiple agents can work together:

from praisonaiagents import Agent, AgentTeam

researcher = Agent(instructions="Research topics")
writer = Agent(instructions="Write content")
editor = Agent(instructions="Edit and polish")

team = AgentTeam(agents=[researcher, writer, editor])
team.start()

Key Takeaways