Core Components

Compare Agent, AgentTeam, AgentFlow, and AgentOS

PraisonAI provides four core components for building AI applications. Each serves a distinct purpose in the architecture.

Quick Reference

Agent

The fundamental building block. A single AI entity with instructions and tools.

from praisonaiagents import Agent

agent = Agent(
    name="Researcher",
    instructions="You research topics thoroughly",
    tools=[search_tool]
)

result = agent.start("What is quantum computing?")

Key Features

• Single purpose, focused execution
• Direct tool access
• Memory and context support
• Works standalone or within orchestrators

AgentTeam

Coordinates multiple agents through task assignment. Think: “Who does what task.”

from praisonaiagents import Agent, AgentTeam, Task

researcher = Agent(role="Researcher", instructions="Research topics")
writer = Agent(role="Writer", instructions="Write content")

task1 = Task(description="Research AI", agent=researcher)
task2 = Task(description="Write article", agent=writer, context=[task1])

manager = AgentTeam(
    agents=[researcher, writer],
    tasks=[task1, task2],
    process="sequential"
)
result = manager.start()

Key Features

• Task-based delegation with explicit agent assignment
• Sequential, parallel, or hierarchical execution
• Task dependencies via context
• Manager LLM for hierarchical validation

AgentFlow

Executes deterministic step sequences with advanced patterns. Think: “What happens in order.”

from praisonaiagents import Agent
from praisonaiagents import AgentFlow, route, loop

researcher = Agent(instructions="Research topics")
writer = Agent(instructions="Write content")

workflow = AgentFlow(
    steps=[
        researcher,
        route({
            "positive": [writer],
            "default": [fallback]
        })
    ]
)
result = workflow.run("Research AI trends")

Key Features

• Pattern-based: route(), parallel(), loop(), repeat(), when()
• Steps can be agents OR functions
• CSV/file iteration with loop(from_csv="data.csv")
• Recipe composition with include()

AgentOS

Deploys agents, managers, or workflows as production web services.

from praisonai import AgentOS
from praisonaiagents import Agent, AgentTeam, AgentFlow

assistant = Agent(name="assistant", instructions="Be helpful")
manager = AgentTeam(agents=[...], tasks=[...])
workflow = AgentFlow(steps=[...])

app = AgentOS(
    name="My AI API",
    agents=[assistant],
    managers=[manager],
    workflows=[workflow]
)
app.serve(port=8000)

Key Features

• FastAPI-based REST endpoints
• WebSocket support
• Auto-generated API docs (/docs)
• CORS configuration
• Health checks (/health)

Endpoints

Comparison Matrix

When to Use What

1. Simple single task

   Use Agent directly:

   Agent(instructions="...").start("prompt")

2. Multi-agent with task delegation

   Use AgentTeam:

   AgentTeam(agents=[...], tasks=[...]).start()

3. Deterministic pipeline with patterns

   Use AgentFlow:

   AgentFlow(steps=[...]).run("input")

4. Production deployment

   Wrap in AgentOS:

   AgentOS(agents=[...]).serve(port=8000)

Shared Parameters

All orchestrators (AgentTeam, AgentFlow) support identical feature parameters:

Best Practices

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