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Agent Patterns

Common patterns and architectures for building effective AI agents across different use cases.

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Single Agent Patterns

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ReAct Agent

Reason and Act pattern where agent thinks through problems step-by-step. 
from agno import Agent, OpenAI

agent = Agent(
    model=OpenAI(id="gpt-4o"),
    tools=[search_tool, calculator_tool],
    reasoning=True,
    markdown=True
)

response = agent.run("What is the GDP of Japan in 2023?")

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Function Calling Agent

Agent with structured tool use via function calling. 
from openai import OpenAI

client = OpenAI()

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "parameters": {"location": {"type": "string"}}
    }
}]

response = client.chat.completions.create(
    model="gpt-4o",
    messages=messages,
    tools=tools
)

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Multi-Agent Patterns

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Sequential Coordination

Agents work in sequence, each building on previous outputs. 
from agno import Agent

# Define agents
researcher = Agent(name="Researcher", role="Research")
writer = Agent(name="Writer", role="Write")
editor = Agent(name="Editor", role="Edit")

# Sequential execution
research = researcher.run(topic)
article = writer.run(research)
final = editor.run(article)

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Team Coordination

Coordinator agent delegates to specialist agents. 
team_agent = Agent(
    team=[web_agent, finance_agent],
    model=OpenAI(id="gpt-4o")
)

response = team_agent.run("Analyze Tesla stock")

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Swarm Orchestration

Circular handoff between specialized agents using AG2. 
from autogen.ext.swarm import Swarm

client = Swarm()

response = client.run(
    agent=initial_agent,
    messages=[{"role": "user", "content": query}],
    context_variables={"phase": "summary"}
)

AI Agents

Learn about different agent types

Multi-Agent Teams

Build coordinated agent teams

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