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

# Task Context Control

> Manage context size and information flow in long workflows

Control how much prior task output each step receives — pass only what the next task needs instead of the full conversation history.

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import Agent, Task, AgentTeam

processor = Agent(name="Processor", instructions="Process data efficiently")

extract = Task(
    name="extract",
    description="Extract data from source files",
    expected_output="Extracted data summary",
    agent=processor,
    retain_full_context=False,  # Only this task's output flows forward
)

analyse = Task(
    name="analyse",
    description="Analyse trends across all previous outputs",
    expected_output="Trend analysis",
    agent=processor,
    retain_full_context=True,  # Include all prior task outputs
)

team = AgentTeam(agents=[processor], tasks=[extract, analyse], process="sequential")
team.start()
```

The user runs a sequential team workflow; each step receives either just the prior task output or the full history, depending on retain\_full\_context.

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
graph LR
    A[Task 1<br/>Full Context] -->|Only Output| B[Task 2<br/>Minimal Context]
    B -->|Only Output| C[Task 3<br/>Minimal Context]
    C -->|All Outputs| D[Task 4<br/>Full Context]

    classDef agent fill:#8B0000,stroke:#7C90A0,color:#fff
    classDef tool fill:#189AB4,stroke:#7C90A0,color:#fff

    class A,D agent
    class B,C tool
```

## How It Works

```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
sequenceDiagram
    participant User
    participant Agent
    participant Feature as Task Context Control

    User->>Agent: Request
    Agent->>Feature: Process request
    Feature-->>Agent: Result    Agent-->>User: Response
```

## Quick Start

<Steps>
  <Step title="Create tasks with context control">
    ```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
    from praisonaiagents import Agent, Task, AgentTeam

    processor = Agent(
        name="DataProcessor",
        instructions="Process and analyse data efficiently",
    )

    task1 = Task(
        name="extract_data",
        description="Extract data from source files",
        expected_output="Extracted data in JSON format",
        agent=processor,
        retain_full_context=False,
    )

    task2 = Task(
        name="analyse_trends",
        description="Analyse trends across all previous data",
        expected_output="Comprehensive trend analysis",
        agent=processor,
        retain_full_context=True,
    )

    team = AgentTeam(agents=[processor], tasks=[task1, task2], process="sequential")
    team.start()
    ```
  </Step>
</Steps>

## Configuration Options

| Option                | Type   | Default | Description                                                                                          |
| --------------------- | ------ | ------- | ---------------------------------------------------------------------------------------------------- |
| `retain_full_context` | `bool` | `False` | When `True`, pass all prior task outputs; when `False`, pass only the immediate predecessor's output |

## Common Patterns

### Pipeline pattern

Each step passes only its own output forward — ideal for large intermediate data:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pipeline_tasks = [
    Task(name="fetch", description="Fetch data", agent=fetcher, retain_full_context=False),
    Task(name="validate", description="Validate format", agent=validator, retain_full_context=False),
    Task(name="transform", description="Transform data", agent=transformer, retain_full_context=False),
    Task(name="store", description="Store in database", agent=storer, retain_full_context=False),
]
```

### Checkpoint pattern

Periodically retain full context so a later task can see the full history:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
checkpoint_interval = 5
tasks = []

for i in range(20):
    is_checkpoint = (i % checkpoint_interval == 0)
    tasks.append(Task(
        name=f"task_{i}",
        description=f"Process item {i}",
        agent=processor,
        retain_full_context=is_checkpoint,
    ))
```

### Summarisation checkpoints

Insert a summary task that reads full context, then continue with minimal context:

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
summary_task = Task(
    name="summarise_progress",
    description="Summarise all previous outputs concisely",
    agent=summariser,
    retain_full_context=True,
)

next_task = Task(
    name="continue",
    description="Continue from the summary",
    agent=processor,
    retain_full_context=False,
)
```

## Best Practices

<AccordionGroup>
  <Accordion title="Plan context flow upfront">
    Map which tasks need full history versus only the previous output before running long workflows. Data-loading steps usually need `retain_full_context=False`; final synthesis steps often need `True`.
  </Accordion>

  <Accordion title="Use checkpoints in long pipelines">
    For workflows with many steps, add periodic summary or checkpoint tasks with `retain_full_context=True` so downstream tasks can recover context without carrying every intermediate payload.
  </Accordion>

  <Accordion title="Avoid token limit errors">
    If later tasks hit context limits, set `retain_full_context=False` on early heavy tasks and pass summaries instead of raw data.
  </Accordion>

  <Accordion title="Combine with context compaction">
    Pair selective retention with [Context Window Management](/docs/features/context-window-management) for automatic overflow protection on agents.
  </Accordion>
</AccordionGroup>

## Related

<CardGroup cols={2}>
  <Card title="Context Window Management" icon="window-maximize" href="/docs/features/context-window-management">
    Automatic token management for agents
  </Card>

  <Card title="Workflow Validation" icon="circle-check" href="/docs/features/workflow-validation">
    Validation loops with controlled context
  </Card>
</CardGroup>