stackone-ai 0.3.1

agents performing actions on your SaaS

StackOne AI SDK

StackOne AI provides a unified interface for accessing various SaaS tools through AI-friendly APIs.

Features

• Unified interface for multiple SaaS tools
• AI-friendly tool descriptions and parameters
• Tool Calling: Direct method calling with tool.call() for intuitive usage
• Glob Pattern Filtering: Advanced tool filtering with patterns like "hris_*" and exclusions "!hris_delete_*"
• Meta Tools (Beta): Dynamic tool discovery and execution based on natural language queries
• Integration with popular AI frameworks:
  • OpenAI Functions
  • LangChain Tools
  • CrewAI Tools
  • LangGraph Tool Node

Installation

pip install stackone-ai

Quick Start

from stackone_ai import StackOneToolSet

# Initialize with API key
toolset = StackOneToolSet()  # Uses STACKONE_API_KEY env var
# Or explicitly: toolset = StackOneToolSet(api_key="your-api-key")

# Get HRIS-related tools with glob patterns
tools = toolset.get_tools("hris_*", account_id="your-account-id")
# Exclude certain tools with negative patterns
tools = toolset.get_tools(["hris_*", "!hris_delete_*"])

# Use a specific tool with the new call method
employee_tool = tools.get_tool("hris_get_employee")
# Call with keyword arguments
employee = employee_tool.call(id="employee-id")
# Or with traditional execute method
employee = employee_tool.execute({"id": "employee-id"})

Integration Examples

LangChain Integration

StackOne tools work seamlessly with LangChain, enabling powerful AI agent workflows:

from langchain_openai import ChatOpenAI
from stackone_ai import StackOneToolSet

# Initialize StackOne tools
toolset = StackOneToolSet()
tools = toolset.get_tools("hris_*", account_id="your-account-id")

# Convert to LangChain format
langchain_tools = tools.to_langchain()

# Use with LangChain models
model = ChatOpenAI(model="gpt-4o-mini")
model_with_tools = model.bind_tools(langchain_tools)

# Execute AI-driven tool calls
response = model_with_tools.invoke("Get employee information for ID: emp123")

# Handle tool calls
for tool_call in response.tool_calls:
    tool = tools.get_tool(tool_call["name"])
    if tool:
        result = tool.execute(tool_call["args"])
        print(f"Result: {result}")

CrewAI Integration

CrewAI uses LangChain tools natively, making integration seamless:

from crewai import Agent, Crew, Task
from stackone_ai import StackOneToolSet

# Get tools and convert to LangChain format
toolset = StackOneToolSet()
tools = toolset.get_tools("hris_*", account_id="your-account-id")
langchain_tools = tools.to_langchain()

# Create CrewAI agent with StackOne tools
agent = Agent(
    role="HR Manager",
    goal="Analyze employee data and generate insights",
    backstory="Expert in HR analytics and employee management",
    tools=langchain_tools,
    llm="gpt-4o-mini"
)

# Define task and execute
task = Task(
    description="Find all employees in the engineering department",
    agent=agent,
    expected_output="List of engineering employees with their details"
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()

Meta Tools (Beta)

Meta tools enable dynamic tool discovery and execution without hardcoding tool names:

# Get meta tools for dynamic discovery
tools = toolset.get_tools("hris_*")
meta_tools = tools.meta_tools()

# Search for relevant tools using natural language
filter_tool = meta_tools.get_tool("meta_search_tools")
results = filter_tool.call(query="manage employees", limit=5)

# Execute discovered tools dynamically
execute_tool = meta_tools.get_tool("meta_execute_tool")
result = execute_tool.call(toolName="hris_list_employees", params={"limit": 10})

Examples

For more examples, check out the examples/ directory:

• Error Handling
• StackOne Account IDs
• Available Tools
• File Uploads
• OpenAI Integration
• LangChain Integration
• CrewAI Integration
• LangGraph Tool Node
• Meta Tools

License

Apache 2.0 License