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After task execution, you can access various aspects of the task results and metadata through the task object properties and methods.

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Basic Result Access

# Execute task
task = Task(description="What is the capital of France?")
result = agent.do(task)

# Access the response
print(f"Task result: {result}")
print(f"Task response: {task.response}")

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Task Metadata

# Access task metadata
print(f"Task ID: {task.task_id}")
print(f"Price ID: {task.price_id}")
print(f"Duration: {task.duration}")
print(f"Start time: {task.start_time}")
print(f"End time: {task.end_time}")

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Cost Information

# Access cost information
total_cost = task.total_cost
input_tokens = task.total_input_token
output_tokens = task.total_output_token

print(f"Total cost: ${total_cost}")
print(f"Input tokens: {input_tokens}")
print(f"Output tokens: {output_tokens}")

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Tool Call History

# Access tool call history
tool_calls = task.tool_calls
for i, tool_call in enumerate(tool_calls):
    print(f"Tool call {i+1}:")
    print(f"  Tool: {tool_call.get('tool_name')}")
    print(f"  Parameters: {tool_call.get('params')}")
    print(f"  Result: {tool_call.get('tool_result')}")

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Cache Information

# Access cache statistics
cache_stats = task.get_cache_stats()
print(f"Cache hit: {cache_stats.get('cache_hit')}")
print(f"Cache method: {cache_stats.get('cache_method')}")
print(f"Cache threshold: {cache_stats.get('cache_threshold')}")

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Task State Information

# Check task state
print(f"Task is paused: {task.is_paused}")
print(f"Tools awaiting execution: {len(task.tools_awaiting_external_execution)}")

# For paused tasks, access external tool calls
if task.is_paused:
    for tool_call in task.tools_awaiting_external_execution:
        print(f"Tool: {tool_call.tool_name}")
        print(f"Arguments: {tool_call.tool_args}")
        print(f"Result: {tool_call.result}")

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Complete Example

from upsonic import Agent, Task
from pydantic import BaseModel

class ReportResult(BaseModel):
    title: str
    summary: str
    key_points: list[str]
    confidence: float

# Create and execute task
agent = Agent(name="Analysis Agent")
task = Task(
    description="Generate a market analysis report",
    response_format=ReportResult,
    enable_cache=True
)

result = agent.do(task)

# Access all available information
print("=== TASK EXECUTION SUMMARY ===")
print(f"Task ID: {task.get_task_id()}")
print(f"Duration: {task.duration:.2f} seconds")
print(f"Cost: ${task.total_cost}")
print(f"Tokens: {task.total_input_token} in, {task.total_output_token} out")
print(f"Tool calls made: {len(task.tool_calls)}")
print(f"Cache hit: {task._cache_hit}")

print("\n=== TASK RESULT ===")
print(f"Result: {result}")
print(f"Response type: {type(task.response)}")

print("\n=== CACHE STATISTICS ===")
cache_stats = task.get_cache_stats()
for key, value in cache_stats.items():
    print(f"{key}: {value}")

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Available Properties and Methods

Property/MethodTypeDescription
responseAnyThe task’s response output
task_idstrUnique task identifier
price_idstrPrice tracking identifier
durationfloatTask execution duration in seconds
total_costfloatEstimated cost in USD
total_input_tokenintNumber of input tokens used
total_output_tokenintNumber of output tokens generated
tool_callsList[Dict]History of tool calls made
get_cache_stats()DictCache statistics and configuration
get_task_id()strFormatted task ID for display

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Best Practices

  • Result Validation: Always check if results are None before processing
  • Error Handling: Handle cases where metadata might not be available
  • Cost Monitoring: Track costs for budget management
  • Performance Analysis: Use duration metrics for optimization
  • Tool Debugging: Review tool call history for debugging complex workflows
  • Cache Optimization: Monitor cache hit rates to optimize caching strategies
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