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Teams orchestrate multiple agents working together to accomplish complex tasks. AgentChat provides several built-in team patterns.

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RoundRobinGroupChat

Agents take turns speaking in a fixed sequence. Simple and predictable. 
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.teams import RoundRobinGroupChat
from autogen_agentchat.conditions import MaxMessageTermination
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

# Create agents
planner = AssistantAgent(
    "planner", 
    model_client=model_client,
    system_message="You create plans."
)
executor = AssistantAgent(
    "executor",
    model_client=model_client, 
    system_message="You execute plans."
)
reviewer = AssistantAgent(
    "reviewer",
    model_client=model_client,
    system_message="You review results."
)

# Create team
team = RoundRobinGroupChat(
    participants=[planner, executor, reviewer],
    termination_condition=MaxMessageTermination(max_messages=10)
)

# Run the team
result = await team.run(task="Create a simple web app")

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When to use

  • Fixed workflow with clear stages
  • Each agent has a distinct role
  • Order of operations matters

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SelectorGroupChat

An LLM selects the next speaker based on the conversation context. 
from autogen_agentchat.teams import SelectorGroupChat
from autogen_agentchat.conditions import TextMentionTermination

team = SelectorGroupChat(
    participants=[researcher, writer, critic],
    model_client=model_client,  # Model for selecting next speaker
    selector_prompt="Select the most appropriate agent to speak next.",
    termination_condition=TextMentionTermination("TERMINATE")
)

result = await team.run(task="Write a research report on AI")

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Parameters

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participants
List[ChatAgent]
required
List of agents in the team
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model_client
ChatCompletionClient
required
LLM used to select the next speaker
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selector_prompt
str
Custom prompt for the selector model
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allow_repeated_speaker
bool
default:"False"
Whether the same agent can speak twice in a row

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When to use

  • Dynamic conversations where next speaker depends on context
  • Agents with overlapping capabilities
  • Flexible, adaptive workflows

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Swarm

Agents hand off tasks to each other dynamically using handoff messages. 
from autogen_agentchat.teams import Swarm
from autogen_agentchat.base import Handoff

# Define handoffs
triage_agent = AssistantAgent(
    "triage",
    model_client=model_client,
    handoffs=[
        Handoff(target="sales", message="Transfer to sales"),
        Handoff(target="support", message="Transfer to support")
    ]
)

sales_agent = AssistantAgent("sales", model_client=model_client)
support_agent = AssistantAgent("support", model_client=model_client)

team = Swarm(
    participants=[triage_agent, sales_agent, support_agent],
    termination_condition=TextMentionTermination("RESOLVED")
)

result = await team.run(task="I need help with my account")

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When to use

  • Customer service and triage scenarios
  • Specialized agents for different domains
  • Dynamic routing based on task content

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MagenticOneGroupChat

A powerful multi-agent team designed for complex web and file-based tasks. 
from autogen_agentchat.teams import MagenticOneGroupChat
from autogen_ext.models.openai import OpenAIChatCompletionClient

model_client = OpenAIChatCompletionClient(model="gpt-4o")

team = MagenticOneGroupChat(
    model_client=model_client
)

result = await team.run(
    task="Research the latest AI papers and create a summary"
)
MagenticOne includes specialized agents:
  • Orchestrator - Coordinates the team
  • WebSurfer - Browses the web
  • FileSurfer - Reads and analyzes files
  • Coder - Writes and reviews code
  • ComputerTerminal - Executes commands
See Magentic-One documentation for details.

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GraphFlow

Define custom orchestration logic using a directed graph. 
from autogen_agentchat.teams import GraphFlow, DiGraphBuilder

# Build a graph
builder = DiGraphBuilder()
builder.add_node("start", planner)
builder.add_node("execute", executor)
builder.add_node("review", reviewer)

# Define transitions
builder.add_edge("start", "execute")
builder.add_edge("execute", "review")

# Conditional edge based on review result
def route_from_review(state) -> str:
    last_message = state.messages[-1]
    if "approved" in last_message.content.lower():
        return "end"
    return "execute"  # Re-execute if not approved

builder.add_conditional_edge("review", route_from_review)

graph = builder.build()
team = GraphFlow(graph=graph, initial_state="start")

result = await team.run(task="Build a feature")

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When to use

  • Complex workflows with conditional logic
  • Loops and retries
  • Multiple exit conditions
  • Fine-grained control over flow
See Graph Orchestration example for a complete example.

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Termination conditions

All teams require a termination condition to know when to stop: 
from autogen_agentchat.conditions import (
    MaxMessageTermination,
    TextMentionTermination,
    TokenUsageTermination,
    TimeoutTermination,
    AndTerminationCondition,
    OrTerminationCondition
)

# Stop after 20 messages
max_msg = MaxMessageTermination(max_messages=20)

# Stop when "TERMINATE" appears
text_term = TextMentionTermination("TERMINATE")

# Stop after 10000 tokens
token_term = TokenUsageTermination(max_total_token=10000)

# Stop after 5 minutes
timeout = TimeoutTermination(timeout_seconds=300)

# Combine conditions (stop if ANY condition is met)
combined = OrTerminationCondition([max_msg, text_term])

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Team comparison

Team TypeRoutingUse Case
RoundRobinGroupChatFixed sequenceSequential workflows
SelectorGroupChatLLM selectsDynamic conversations
SwarmAgent handoffsTriage and routing
MagenticOneGroupChatOrchestrator + specialistsComplex web/file tasks
GraphFlowCustom graphComplex conditional logic

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Nested teams

You can nest teams using SocietyOfMindAgent: 
from autogen_agentchat.agents import SocietyOfMindAgent

# Create inner team
inner_team = RoundRobinGroupChat([agent1, agent2])

# Wrap as single agent
team_agent = SocietyOfMindAgent(
    name="inner_team",
    team=inner_team
)

# Use in outer team
outer_team = SelectorGroupChat(
    participants=[team_agent, other_agent]
)

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Best practices

Each team should have a clear purpose. Don’t create overly complex teams with too many agents.
Always set termination conditions to prevent infinite loops. Combine conditions for safety.
Agent descriptions help LLM-based selectors choose the right agent. Be specific.
Multi-agent conversations can use many tokens. Use TokenUsageTermination to control costs.

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Next steps

Multi-Agent Workflows

Learn to design effective multi-agent systems

Swarm Example

See Swarm pattern in action

Graph Orchestration

Build custom graph-based workflows

Orchestration Patterns

Explore advanced patterns