AgentTeam

Multi-agent coordinator for orchestrating teams of AI agents

AgentTeam is the multi-agent coordinator that manages and delegates work to multiple Agent instances, supporting sequential, parallel, and hierarchical execution patterns.

Diagram

Quick Start

Basic Team

Create a simple team with multiple agents:

python

from praisonaiagents import Agent, AgentTeam, Task

# Create agents
researcher = Agent(
    name="Researcher",
    role="Research Specialist",
    instructions="Research and gather information on topics"
)

writer = Agent(
    name="Writer", 
    role="Content Writer",
    instructions="Write clear, engaging content"
)

# Create tasks
task1 = Task(description="Research AI trends", agent=researcher)
task2 = Task(description="Write article", agent=writer)

# Create team
team = AgentTeam(
    agents=[researcher, writer],
    tasks=[task1, task2]
)

result = team.start()
print(result)

Parallel Execution

Execute tasks in parallel for faster processing:

python

from praisonaiagents import Agent, AgentTeam, Task

agents = [
    Agent(name="Analyst1", instructions="Analyze market data"),
    Agent(name="Analyst2", instructions="Analyze competitor data"),
    Agent(name="Analyst3", instructions="Analyze customer data"),
]

tasks = [
    Task(description="Market analysis", agent=agents[0]),
    Task(description="Competitor analysis", agent=agents[1]),
    Task(description="Customer analysis", agent=agents[2]),
]

team = AgentTeam(
    agents=agents,
    tasks=tasks,
    process="parallel"  # Execute tasks in parallel
)

result = team.start()

Hierarchical Process

Use a manager agent to coordinate work:

python

from praisonaiagents import Agent, AgentTeam, Task

team = AgentTeam(
    agents=[researcher, writer, editor],
    tasks=[research_task, write_task, edit_task],
    process="hierarchical",  # Manager coordinates agents
    manager_llm="gpt-4o"     # Manager model
)

result = team.start()

With Memory & Planning

Enable shared memory and planning for the team:

python

from praisonaiagents import Agent, AgentTeam

team = AgentTeam(
    agents=[agent1, agent2, agent3],
    memory=True,      # Shared memory across agents
    planning=True,    # Enable collaborative planning
    output="verbose"  # Detailed output
)

result = team.start("Analyze and report on Q4 sales data")

Process Types

Process	Description	Best For
`sequential`	Tasks execute one after another	Dependent tasks, pipelines
`parallel`	Tasks execute simultaneously	Independent analysis
`hierarchical`	Manager coordinates agents	Complex delegation

Configuration Options

Parameter	Type	Default	Description
`agents`	List[Agent]	Required	List of Agent instances
`tasks`	List[Task]	Auto-generated	Tasks to execute
`process`	str	`"sequential"`	Execution pattern
`manager_llm`	str	`"gpt-4o-mini"`	Manager model (hierarchical)
`memory`	bool/Config	`False`	Shared memory system
`planning`	bool/Config	`False`	Collaborative planning
`context`	bool/Config	`False`	Context management
`output`	str/Config	None	Output configuration
`execution`	str/Config	None	Execution limits
`hooks`	Config	None	Lifecycle callbacks

State Management

AgentTeam provides shared state across all agents:

python

team = AgentTeam(agents=[agent1, agent2])

# Set shared state
team.set_state("analysis_complete", True)
team.set_state("total_records", 1500)

# Get state from any agent
value = team.get_state("analysis_complete")

# Increment counters
team.increment_state("processed_count", 1)

# Append to lists
team.append_to_state("findings", "New insight discovered")

Best Practices

Task Dependencies

For sequential tasks, AgentTeam automatically passes context from previous tasks:

python

task1 = Task(description="Research topic", agent=researcher)
task2 = Task(description="Write based on research", agent=writer)
# task2 automatically receives task1's output as context

Agent Specialization

Create agents with distinct roles for better task delegation:

python

researcher = Agent(role="Research Specialist", tools=[search_tool])
analyst = Agent(role="Data Analyst", tools=[analysis_tool])
writer = Agent(role="Content Writer")

Memory Sharing

Enable memory for teams that need to share information:

python

team = AgentTeam(
    agents=[...],
    memory=True  # All agents share memory
)

Key Methods

Method	Description
`start()`	Execute the team workflow
`run()`	Alias for start()
`astart()`	Async execution
`set_state()`	Set shared state value
`get_state()`	Get shared state value
`add_task()`	Add task dynamically
`get_task_status()`	Check task status