Team

The Team class enables multi-agent operations using the Upsonic client. It supports three operational modes: sequential, coordinate, and route.

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Parameters

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Operational Modes

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Sequential Mode

Tasks are assigned to agents sequentially based on task-agent matching. The system:

1. Builds a selection context from current task, all tasks, task index, agents, and previous results
2. Selects the best agent for each task using intelligent task assignment
3. Enhances task context with information from previous tasks and results
4. Executes tasks one by one
5. Combines results if multiple tasks are executed (or returns single result if only one task)

Memory Behavior: If memory is provided, it is automatically shared with all agents that don’t already have memory configured. Result Handling: If multiple tasks are executed, results are combined using a result combiner. If only one task is executed, the single result is returned directly.

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Coordinate Mode

A leader agent coordinates task delegation to team members. This mode:

1. Creates a leader agent with a system prompt containing team roster and mission objectives
2. Provides a delegation tool that allows the leader to assign tasks to team members
3. The leader agent analyzes the mission and delegates tasks to appropriate team members
4. Team members execute delegated tasks with shared memory context
5. Returns the final response from the leader agent

Requirements:

• model must be set (raises ValueError if not provided)
• Creates an internal leader_agent (accessible via team.leader_agent)
• Automatically creates memory if not provided (uses InMemoryStorage with full_session_memory=True)

Memory Behavior: If no memory is provided, a new Memory instance is automatically created with InMemoryStorage for the coordinator session.

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Route Mode

A router agent selects the best specialist for the entire request. This mode:

1. Creates a router agent with a system prompt containing team roster and mission objectives
2. Provides a routing tool that allows the router to select a single team member
3. The router analyzes the full mission and selects the best specialist
4. All tasks are consolidated into a single task with all attachments and tools
5. The selected agent executes the consolidated task
6. Returns the response from the selected agent

Requirements:

• model must be set (raises ValueError if not provided)
• Creates an internal leader_agent (accessible via team.leader_agent)

Task Consolidation: All tasks are merged into a single task with:

• Combined descriptions
• All attachments from all tasks
• All unique tools from all tasks
• The specified response_format

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Functions

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complete

Execute multi-agent operations with the predefined agents and tasks (alias for do). Parameters:

• tasks (list[Task] | Task | None): Optional list of tasks or single task to execute. If not provided, uses tasks from initialization

Returns:

• The response from the multi-agent operation

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print_complete

Execute the multi-agent operation and print the result (alias for print_do). Parameters:

• tasks (list[Task] | Task | None): Optional list of tasks or single task to execute. If not provided, uses tasks from initialization

Returns:

• The response from the multi-agent operation

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do

Execute multi-agent operations with the predefined agents and tasks. This is the main entry point for team execution. Parameters:

• tasks (list[Task] | Task | None): Optional list of tasks or single task to execute. If not provided, uses tasks from initialization

Returns:

• The response from the multi-agent operation

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multi_agent

Execute multi-agent operations with agent configurations and tasks. This method automatically handles event loop detection - if a loop is already running, it uses run_coroutine_threadsafe, otherwise it creates a new event loop. Parameters:

• agent_configurations (List[Agent]): List of agent configurations
• tasks (Any): Tasks to execute

Returns:

• The response from the multi-agent operation

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multi_agent_async

Asynchronous version of the multi_agent method. Supports three operational modes:

• sequential: Tasks are assigned to agents sequentially based on task-agent matching. Results are combined if multiple tasks are executed.
• coordinate: A leader agent coordinates task delegation to team members. Requires a model to be set.
• route: A router agent selects the best specialist for the task. Requires a model to be set.

Parameters:

• agent_configurations (List[Agent]): List of agent configurations
• tasks (Any): Tasks to execute

Returns:

• The response from the multi-agent operation

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print_do

Execute the multi-agent operation and print the result. Parameters:

• tasks (list[Task] | Task | None): Optional list of tasks or single task to execute. If not provided, uses tasks from initialization

Returns:

• The response from the multi-agent operation

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add_tool

Add agents as tools to each Task object in self.tasks. This method is automatically called when ask_other_team_members is set to True during initialization. Behavior:

• Iterates through all tasks in self.tasks
• Adds all team agents to each task’s tools list
• If a task doesn’t have a tools attribute, it creates an empty list first
• If tools is not a list, it replaces it with the agents list

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Internal Attributes

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leader_agent

An internal Agent instance created for ‘coordinate’ and ‘route’ modes. This is the leader/router agent that manages task delegation or routing. Accessible via team.leader_agent after execution in these modes. Type: Optional[Agent]