> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/Shubhamsaboo/awesome-llm-apps/llms.txt
> Use this file to discover all available pages before exploring further.

# Agent Patterns

> Common architectural patterns for building AI agents

# Agent Patterns

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

## Single Agent Patterns

### ReAct Agent

Reason and Act pattern where agent thinks through problems step-by-step.

```python theme={null}
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?")
```

### Function Calling Agent

Agent with structured tool use via function calling.

```python theme={null}
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
)
```

## Multi-Agent Patterns

### Sequential Coordination

Agents work in sequence, each building on previous outputs.

```python theme={null}
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)
```

### Team Coordination

Coordinator agent delegates to specialist agents.

```python theme={null}
team_agent = Agent(
    team=[web_agent, finance_agent],
    model=OpenAI(id="gpt-4o")
)

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

### Swarm Orchestration

Circular handoff between specialized agents using AG2.

```python theme={null}
from autogen.ext.swarm import Swarm

client = Swarm()

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

## Related Resources

<CardGroup cols={2}>
  <Card title="AI Agents" icon="robot" href="/ai-agents/overview">
    Learn about different agent types
  </Card>

  <Card title="Multi-Agent Teams" icon="users" href="/ai-agents/multi-agent-teams">
    Build coordinated agent teams
  </Card>
</CardGroup>