> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Sequential

> Linear workflow where tasks flow from one agent to the next

## What is Sequential Team mode

Sequential mode is the default and most straightforward team operation mode. Tasks flow from one agent to the next in a linear pipeline.

There are two ways to assign agents to tasks in sequential mode:

* **Automatic Selection** — The team intelligently analyzes each task and selects the best-suited agent based on their roles and capabilities. This is the default behavior.
* **Manual Assignment** — You explicitly assign a specific agent to a task using the `agent` parameter. This gives you full control over which agent handles which task.

## Automatic Selection

By default, the team automatically picks the best agent for each task. You simply define your tasks and let the team decide.

```python theme={null}
from upsonic import Agent, Task, Team

# Create specialized agents
researcher = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Researcher",
    role="Research Specialist",
    goal="Find accurate information and data"
)

writer = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Writer",
    role="Content Writer",
    goal="Create clear and engaging content"
)

# Create sequential team
team = Team(
    agents=[researcher, writer],
    mode="sequential"
)

# Define tasks — the team will automatically select the best agent for each
tasks = [
    Task(description="Research the latest developments in quantum computing"),
    Task(description="Write a blog post about quantum computing for general audience")
]

# Execute team workflow
result = team.print_do(tasks)
```

## Manual Assignment

You can explicitly assign a specific agent to a task by passing the `agent` parameter. This overrides the automatic selection and ensures the designated agent handles that task.

```python theme={null}
from upsonic import Agent, Task, Team

# Create specialized agents
writer = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Writer",
    role="Content Writer",
    goal="Create engaging content"
)

editor = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Editor",
    role="Content Editor",
    goal="Polish and improve content"
)

# Create sequential team
team = Team(agents=[writer, editor], mode="sequential")

# Explicitly assign agents to tasks
task1 = Task(description="Write a product description", agent=writer)
task2 = Task(description="Edit and polish the description", agent=editor)

# Execute with manual assignments
result = team.print_do([task1, task2])
```

## Streaming with mixed Agent and Team entities

You can stream output with a mix of Agent and nested Team entities. Each entity's output is preceded by a header (e.g. `--- [Agent] Researcher ---`).

```python theme={null}
from upsonic import Agent, Task, Team

researcher = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Researcher",
    role="Research Specialist",
    goal="Find accurate information and data",
)

editor = Agent(
    model="anthropic/claude-sonnet-4-5",
    name="Editor",
    role="Editor",
    goal="Polish and refine content",
)

writer_team = Team(
    entities=[
        Agent(
            model="anthropic/claude-sonnet-4-5",
            name="Writer",
            role="Content Writer",
            goal="Create clear and engaging content",
        ),
        editor,
    ],
    mode="sequential",
    name="WriterTeam",
)

team = Team(
    entities=[researcher, writer_team],
    mode="sequential",
)

tasks = [
    Task(description="Research the latest developments in quantum computing"),
    Task(description="Write a blog post about quantum computing for general audience"),
]

for chunk in team.stream(tasks):
    print(chunk, end="", flush=True)
```

## Params

* `agents`: List of Agent instances
* `mode`: Set to `"sequential"`
* `response_format`: Optional, defaults to `str`
* `ask_other_team_members`: Optional, enables inter-agent communication