Adding Tasks to Other Tasks as Context

Using task outputs as context for other tasks in the Upsonic framework

In Upsonic, you can use the output of one task as context for another task through the context parameter. This enables task chaining and complex workflows.

Basic Task Chaining

from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")

# First task
task1 = Task(description="What is 2 + 2?")
result1 = agent.print_do(task1)
print(f"Result 1: {result1}")

# Second task using context from first task
task2 = Task(
    description="Based on the previous result, what is that number multiplied by 3?",
    context=[task1]
)
result2 = agent.print_do(task2)
print(f"Result 2: {result2}")

Multiple Context Sources

from upsonic import Agent, Task

agent = Agent(model="anthropic/claude-sonnet-4-5")

# First two tasks
task1 = Task(description="What is 10 + 5?")
result1 = agent.print_do(task1)

task2 = Task(description="What is 20 - 8?", context=[task1])
result2 = agent.print_do(task2)

# Task with multiple context sources
task3 = Task(
    description="Summarize all the previous mathematical results",
    context=[task1, task2, "Additional context: These were all math problems"]
)
result3 = agent.print_do(task3)
print(result3)

Complex Workflow Example

from upsonic import Agent, Task
from upsonic.tools import tool
from pydantic import BaseModel
from typing import List

@tool
def data_collector(source: str) -> str:
    """Collect data from a source."""
    return f"Collected data from {source}"

@tool
def data_analyzer(data: str) -> str:
    """Analyze data."""
    return f"Analysis of: {data}"

class ReportResult(BaseModel):
    title: str
    summary: str
    insights: List[str]

agent = Agent(model="anthropic/claude-sonnet-4-5")

# Task 1: Data collection
task1 = Task(
    description="Collect data from market research sources",
    tools=[data_collector],
    enable_cache=True
)
result1 = agent.print_do(task1)

# Task 2: Data analysis
task2 = Task(
    description="Analyze the collected market data",
    tools=[data_analyzer],
    context=[task1],
    enable_thinking_tool=True
)
result2 = agent.print_do(task2)

# Task 3: Report generation
task3 = Task(
    description="Generate a comprehensive market report",
    context=[task1, task2],
    response_format=ReportResult
)
result3 = agent.print_do(task3)
print(f"Report: {result3.title}")
print(f"Summary: {result3.summary}")

Context Types

Tasks can use various types of context:

• Other Tasks: Previous task results for chaining
• Strings: Direct text context
• Knowledge Bases: RAG-enabled knowledge sources
• Mixed: Combination of different context types

from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import OpenAIEmbedding
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Setup knowledge base
embedding_provider = OpenAIEmbedding()
vectordb = ChromaProvider(config=ChromaConfig(collection_name="kb", vector_size=1536, connection=ConnectionConfig(mode=Mode.IN_MEMORY)))
knowledge_base = KnowledgeBase(sources=["company_data.txt"], embedding_provider=embedding_provider, vectordb=vectordb)

agent = Agent(model="anthropic/claude-sonnet-4-5")

# Previous data collection task
data_collection_task = Task(description="Collect Q4 sales data")
agent.do(data_collection_task)

# Mixed context example
task = Task(
    description="Analyze the data and provide insights",
    context=[
        data_collection_task,  # Previous task
        "Focus on Q4 performance",  # String context
        knowledge_base  # Knowledge base context
    ]
)
result = agent.print_do(task)
print(result)

Best Practices

• Logical Flow: Design task chains with clear logical progression
• Context Relevance: Only include context that’s relevant to the current task
• Performance: Consider caching for expensive context processing
• Error Handling: Handle cases where context tasks might fail
• Documentation: Clearly document the expected context flow in your workflows