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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.

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Basic Task Chaining

from upsonic import Agent, Task

agent = Agent(model="openai/gpt-4o")

# First task
task1 = Task(description="What is 2 + 2?")
result1 = agent.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.do(task2)
print(f"Result 2: {result2}")

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Multiple Context Sources

from upsonic import Agent, Task

agent = Agent(model="openai/gpt-4o")

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

task2 = Task(description="What is 20 - 8?", context=[task1])
result2 = agent.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.do(task3)
print(result3)

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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="openai/gpt-4o")

# Task 1: Data collection
task1 = Task(
    description="Collect data from market research sources",
    tools=[data_collector],
    enable_cache=True
)
result1 = agent.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.do(task2)

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

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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="openai/gpt-4o")

# 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.do(task)
print(result)

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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