Documentation Index
Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
Use this file to discover all available pages before exploring further.
Tasks can be equipped with various tools to extend their capabilities. Tools are added through the tools parameter and can include custom functions, built-in tools, or tool collections.
from upsonic import Agent, Task
from upsonic.tools import tool
@tool
def web_search(query: str) -> str:
"""Search the web for information."""
return f"Search results for: {query}"
task = Task(
description="Search for information about AI",
tools=[web_search]
)
agent = Agent("anthropic/claude-sonnet-4-5")
agent.print_do(task)
from upsonic import Agent, Task
from upsonic.tools import tool
@tool
def web_search(query: str) -> str:
"""Search the web for information."""
return f"Search results for: {query}"
@tool
def data_processor(data: str) -> str:
"""Process and analyze data."""
return f"Processed: {data}"
task = Task(
description="Search for information about AI and process the results",
tools=[web_search, data_processor]
)
agent = Agent("anthropic/claude-sonnet-4-5")
agent.print_do(task)
from upsonic import Agent, Task
from upsonic.tools.common_tools.financial_tools import YFinanceTools
# Create a tool collection
finance_tools = YFinanceTools(
stock_price=True,
company_info=True,
analyst_recommendations=True
)
task = Task(
description="Get stock information for AAPL",
tools=finance_tools.functions()
)
agent = Agent(model="anthropic/claude-sonnet-4-5")
agent.print_do(task)
from upsonic import Agent, Task
from upsonic.tools import tool
@tool(requires_confirmation=True)
def sensitive_operation(data: str) -> str:
"""Perform a sensitive operation that requires confirmation."""
return f"Processed sensitive data: {data}"
task = Task(
description="Process sensitive information with data 'Upsonic'",
tools=[sensitive_operation]
)
agent = Agent(model="anthropic/claude-sonnet-4-5")
agent.print_do(task)
| Configuration | Type | Description |
|---|
| requires_confirmation | bool | Pause for user confirmation before execution |
| requires_user_input | bool | Prompt user for input during execution |
| user_input_fields | List[str] | Fields to prompt user for when requires_user_input is True |
| external_execution | bool | Handle execution externally (advanced use-cases) |
| show_result | bool | Display result directly to user without LLM processing |
| stop_after_tool_call | bool | Terminate agent after this tool call |
| sequential | bool | Require sequential execution (no parallelization) |
| cache_results | bool | Cache the result based on arguments |
| cache_dir | Optional[str] | Directory to store cache files |
| cache_ttl | Optional[int] | Time-to-live for cache entries in seconds |
| tool_hooks | Optional[ToolHooks] | Custom functions to run before/after tool execution |
| max_retries | Optional[int] | Maximum number of retries allowed for this tool (default: 5) |
| timeout | Optional[float] | Timeout for tool execution in seconds (default: 30.0) |
| strict | Optional[bool] | Enforce strict JSON schema validation on tool parameters |
| docstring_format | str | Format of the docstring: ‘google’, ‘numpy’, ‘sphinx’, or ‘auto’ |
| require_parameter_descriptions | bool | Raise error if required parameter descriptions are missing |
from upsonic import Agent, Task
from upsonic.tools import tool
@tool
def web_search(query: str) -> str:
"""Search the web for information."""
return f"Search results for: {query}"
@tool
def calculator(a: float, b: float) -> float:
"""Add two numbers."""
return a + b
task = Task(description="Search for AI news and calculate 10 + 20")
agent = Agent("anthropic/claude-sonnet-4-5")
# Add tools dynamically
task.add_tools([web_search, calculator])
# Execute task with dynamically added tools
agent.print_do(task)
# Remove tools (for subsequent executions)
task.remove_tools([web_search], agent=agent)
registered_task_tools attribute:
from upsonic import Agent, Task
from upsonic.tools import tool
@tool
def calculator(a: float, b: float) -> float:
"""Add two numbers."""
return a + b
task = Task(description="Calculate 5 + 3", tools=[calculator])
agent = Agent("anthropic/claude-sonnet-4-5")
agent.print_do(task)
# Access registered tools
print(task.registered_task_tools) # Dict mapping tool names to wrapped tools
Best Practices
- Tool Validation: All tools must have type hints and docstrings
- Single Responsibility: Each tool should have a clear, focused purpose
- Error Handling: Implement proper error handling within tool functions
- Configuration: Use tool configurations appropriately for security and user experience
- Documentation: Write clear docstrings that explain the tool’s purpose to the LLM
