> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# 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.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart LR
subgraph AgentTeam["AgentTeam"]
direction TB
A1["Agent 1"]
A2["Agent 2"]
A3["Agent 3"]
end
Input["Input"] --> AgentTeam
AgentTeam --> Output["Output"]
A1 --> A2
A2 --> A3
classDef input fill:#6366F1,stroke:#7C90A0,color:#fff
classDef process fill:#F59E0B,stroke:#7C90A0,color:#fff
classDef output fill:#10B981,stroke:#7C90A0,color:#fff
class Input input
class A1,A2,A3 process
class Output output
```
## Quick Start
Create a simple team with multiple agents:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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)
```
Execute tasks in parallel for faster processing:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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()
```
Use a manager agent to coordinate work:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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()
```
Enable shared memory and planning for the team:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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
For sequential tasks, AgentTeam automatically passes context from previous tasks:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
task1 = Task(description="Research topic", agent=researcher)
task2 = Task(description="Write based on research", agent=writer)
# task2 automatically receives task1's output as context
```
Create agents with distinct roles for better task delegation:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
researcher = Agent(role="Research Specialist", tools=[search_tool])
analyst = Agent(role="Data Analyst", tools=[analysis_tool])
writer = Agent(role="Content Writer")
```
Enable memory for teams that need to share information:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 |
## Related
Learn about individual AI agents
Deterministic workflow pipelines
Task configuration and management
Execution process patterns