Praison AI

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
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.

Diagram

Only Output

Only Output

All Outputs

Task 1
Full Context

Task 2
Minimal Context

Task 3
Minimal Context

Task 4
Full Context

How It Works

Diagram
Task Context ControlAgentUserTask Context ControlAgentUserRequestProcess requestResult Agent–>>User: Response

Quick Start

  1. Create tasks with context control
    python
    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()

Configuration Options

OptionTypeDefaultDescription
retain_full_contextboolFalseWhen 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
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
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
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

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.

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.

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.

Combine with context compaction

Pair selective retention with Context Window Management for automatic overflow protection on agents.

Related

Context Window Management

Automatic token management for agents

 Workflow Validation

Validation loops with controlled context