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A Team is a collection of agents (or sub-teams) that work together. The team leader delegates tasks to members based on their roles. Team structure
from agno.team import Team
from agno.agent import Agent

team = Team(members=[
    Agent(name="English Agent", role="You answer questions in English"),
    Agent(name="Chinese Agent", role="You answer questions in Chinese"),
    Team(
        name="Germanic Team",
        role="You coordinate the team members to answer questions in German and Dutch",
        members=[
            Agent(name="German Agent", role="You answer questions in German"),
            Agent(name="Dutch Agent", role="You answer questions in Dutch"),
        ],
    ),
])

Why Teams?

Single agents hit limits fast. Context windows fill up, decision-making gets muddy, debugging becomes impossible. Teams distribute work across specialized agents:
BenefitDescription
SpecializationEach agent masters one domain instead of being mediocre at everything
Parallel processingMultiple agents work simultaneously on independent subtasks
MaintainabilityWhen something breaks, you know exactly which agent to fix
ScalabilityAdd capabilities by adding agents, not rewriting everything
The tradeoff: coordination overhead. Agents need to communicate and share state. Get this wrong and you’ve built a more expensive failure mode.

When to Use Teams

Use a team when:
  • A task requires multiple specialized agents with different tools or expertise
  • A single agent’s context window gets exceeded
  • You want each agent focused on a narrow scope
Use a single agent when:
  • The task fits one domain of expertise
  • Minimizing token costs matters
  • You’re not sure yet (start simple, add agents when you hit limits)

What’s New?

Team execution is modular. Message building, session handling, storage, and background managers are separated into dedicated components so coordination logic can evolve without changing the Team API. Additional Team 2.0 updates include approvals for human-in-the-loop workflows, cron-based scheduling for teams, improved HITL requirements, and LearningMachine support for persistent learning.

Team Modes

Team 2.0 introduces TeamMode to make collaboration styles explicit. Prefer mode= instead of toggling respond_directly or delegate_to_all_members directly.
from agno.team import Team, TeamMode

team = Team(
    name="Research Team",
    members=[...],
    mode=TeamMode.broadcast,
)
ModeConfigurationUse case
Coordinatemode=TeamMode.coordinate (default)Decompose work, delegate to members, synthesize results
Routemode=TeamMode.routeRoute to a single specialist and return their response directly
Broadcastmode=TeamMode.broadcastDelegate the same task to all members and synthesize
Tasksmode=TeamMode.tasksManage a shared task list and loop until the goal is complete
See Delegation for details.

Guides

Resources