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
• MCP-backed Dynamic Discovery: Fetch tools at runtime via fetch_tools() with provider, action, and account filtering
• Advanced Tool Filtering:
  • Glob pattern filtering with patterns like "hris_*" and exclusions "!hris_delete_*"
  • Provider and action filtering
  • Multi-account support
• 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

Requirements

• Python 3.10+ (MCP extra required for fetch_tools())

Installation

Using Nix (Recommended for Development)

If you have Nix installed with flakes enabled:

# Enter development environment (auto-installs dependencies and git hooks)
nix develop

# Format code
nix fmt

# Run checks
nix flake check

The Nix development environment includes:

• Python with uv package manager
• Automatic dependency installation
• Git hooks (treefmt + ty) auto-configured
• Consistent environment across all platforms

Using pip/uv

pip install 'stackone-ai[mcp]'

# Or with uv
uv add 'stackone-ai[mcp]'

Optional Features

# Install with CrewAI examples
pip install 'stackone-ai[mcp,examples]'
# or
uv add 'stackone-ai[mcp,examples]'

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.fetch_tools(actions=["hris_*"], account_ids=["your-account-id"])

# Use a specific tool with the 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"})

Tool Filtering

StackOne AI SDK provides powerful filtering capabilities to help you select the exact tools you need.

Filtering with fetch_tools()

The fetch_tools() method provides filtering by providers, actions, and account IDs:

from stackone_ai import StackOneToolSet

toolset = StackOneToolSet()

# Filter by account IDs
tools = toolset.fetch_tools(account_ids=["acc-123", "acc-456"])

# Filter by providers (case-insensitive)
tools = toolset.fetch_tools(providers=["hibob", "bamboohr"])

# Filter by action patterns with glob support
tools = toolset.fetch_tools(actions=["*_list_employees"])

# Combine multiple filters
tools = toolset.fetch_tools(
    account_ids=["acc-123"],
    providers=["hibob"],
    actions=["*_list_*"]
)

# Use set_accounts() for chaining
toolset.set_accounts(["acc-123", "acc-456"])
tools = toolset.fetch_tools(providers=["hibob"])

Filtering Options:

• account_ids: Filter tools by account IDs. Tools will be loaded for each specified account.
• providers: Filter by provider names (e.g., ["hibob", "bamboohr"]). Case-insensitive matching.
• actions: Filter by action patterns with glob support:
  • Exact match: ["hris_list_employees"]
  • Glob pattern: ["*_list_employees"] matches all tools ending with _list_employees
  • Provider prefix: ["hris_*"] matches all HRIS tools

Implicit Feedback (Beta)

The Python SDK can emit implicit behavioral feedback to LangSmith so you can triage low-quality tool results without manually tagging runs.

Automatic configuration

Set LANGSMITH_API_KEY in your environment and the SDK will initialize the implicit feedback manager on first tool execution. You can optionally fine-tune behavior with:

• STACKONE_IMPLICIT_FEEDBACK_ENABLED (true/false, defaults to true when an API key is present)
• STACKONE_IMPLICIT_FEEDBACK_PROJECT to pin a LangSmith project name
• STACKONE_IMPLICIT_FEEDBACK_TAGS with a comma-separated list of tags applied to every run

Manual configuration

If you want custom session or user resolvers, call configure_implicit_feedback during start-up:

from stackone_ai import configure_implicit_feedback

configure_implicit_feedback(
    api_key="/path/to/langsmith.key",
    project_name="stackone-agents",
    default_tags=["python-sdk"],
)

Providing your own session_resolver/user_resolver callbacks lets you derive identifiers from the request context before events are sent to LangSmith.

Attaching session context to tool calls

Both tool.execute and tool.call accept an options keyword that is excluded from the API request but forwarded to the feedback manager:

tool.execute(
    {"id": "employee-id"},
    options={
        "feedback_session_id": "chat-42",
        "feedback_user_id": "user-123",
        "feedback_metadata": {"conversation_id": "abc"},
    },
)

When two calls for the same session happen within a few seconds, the SDK emits a refinement_needed event, and you can inspect suitability scores directly in LangSmith.

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.fetch_tools(actions=["hris_*"], account_ids=["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}")

LangGraph Integration

StackOne tools convert to LangChain tools, which LangGraph consumes via its prebuilt nodes:

Prerequisites:

pip install langgraph langchain-openai

from langchain_openai import ChatOpenAI
from typing import Annotated
from typing_extensions import TypedDict

from langgraph.graph import StateGraph, START, END
from langgraph.graph.message import add_messages
from langgraph.prebuilt import tools_condition

from stackone_ai import StackOneToolSet
from stackone_ai.integrations.langgraph import to_tool_node, bind_model_with_tools

# Prepare tools
toolset = StackOneToolSet()
tools = toolset.fetch_tools(actions=["hris_*"], account_ids=["your-account-id"])
langchain_tools = tools.to_langchain()

class State(TypedDict):
    messages: Annotated[list, add_messages]

# Build a small agent loop: LLM -> maybe tools -> back to LLM
graph = StateGraph(State)
graph.add_node("tools", to_tool_node(langchain_tools))

def call_llm(state: dict):
    llm = ChatOpenAI(model="gpt-4o-mini")
    llm = bind_model_with_tools(llm, langchain_tools)
    resp = llm.invoke(state["messages"])  # returns AIMessage with optional tool_calls
    return {"messages": state["messages"] + [resp]}

graph.add_node("llm", call_llm)
graph.add_edge(START, "llm")
graph.add_conditional_edges("llm", tools_condition)
graph.add_edge("tools", "llm")
app = graph.compile()

_ = app.invoke({"messages": [("user", "Get employee with id emp123") ]})

CrewAI Integration (Python 3.10+)

CrewAI uses LangChain tools natively, making integration seamless:

Note: CrewAI requires Python 3.10+. Install with pip install 'stackone-ai[mcp,examples]'

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

# Get tools and convert to LangChain format
toolset = StackOneToolSet()
tools = toolset.fetch_tools(actions=["hris_*"], account_ids=["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()

Feedback Collection

The SDK includes a feedback collection tool (meta_collect_tool_feedback) that allows users to submit feedback about their experience with StackOne tools. This tool is automatically included in the toolset and is designed to be invoked by AI agents after user permission.

from stackone_ai import StackOneToolSet

toolset = StackOneToolSet()

# Get the feedback tool (included with "meta_*" pattern or all tools)
tools = toolset.fetch_tools(actions=["meta_*"])
feedback_tool = tools.get_tool("meta_collect_tool_feedback")

# Submit feedback (typically invoked by AI after user consent)
result = feedback_tool.call(
    feedback="The HRIS tools are working great! Very fast response times.",
    account_id="acc_123456",
    tool_names=["hris_list_employees", "hris_get_employee"]
)

Important: The AI agent should always ask for user permission before submitting feedback:

• "Are you ok with sending feedback to StackOne? The LLM will take care of sending it."
• Only call the tool after the user explicitly agrees.

Meta Tools (Beta)

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

Basic Usage

# Get meta tools for dynamic discovery
tools = toolset.fetch_tools(actions=["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:

• StackOne Account IDs
• File Uploads
• OpenAI Integration
• LangChain Integration
• CrewAI Integration
• Meta Tools

License

Apache 2.0 License