Python Toolset

Build connected agents with StackOne’s Python AI SDK & Toolset

StackOne Agent Tools provide a powerful way to build AI agents that can interact with various business systems through our unified APIs. The Python SDK offers rich integrations with popular AI frameworks like LangChain, and CrewAI.

Why StackOne for AI Agents?

StackOne simplifies building B2B SaaS agents by providing:

1. Unified API Access - Standardized interfaces across different SaaS providers
2. AI-First Design - Tools designed specifically for AI agent development
3. Multi-Tenant Support - Built-in handling of customer accounts and permissions
4. Standardized Data Models - Consistent data structures across providers

Architecture Overview

StackOne Agent Tools are built on three core components:

1. Unified API

Our standardized API layer that normalizes data across different platforms, ensuring more accurate and consistent results for your AI agents.

2. Toolsets

Language-specific SDKs that wrap StackOne API endpoints into AI-friendly tools with native support for popular AI agent frameworks. Each toolset requires an API key to authenticate with StackOne API endpoints.

3. Linked Accounts

Account identifiers that enable your agents to access the correct customer-specific tenant data securely and efficiently.

Key Concepts

• Initialize a ToolSet: Create a StackOneToolSet instance to access unified business APIs
• Filter Tools: Use glob patterns like "hris_*" to get specific tool categories
• Execute Tools: Call business system APIs using tool.call() or tool.execute()
• Multi-Tenant: Each tool execution requires an account_id for customer isolation

Python Framework Integrations

LangChain

Build sophisticated agents with LangChain

CrewAI

Multi-agent workflows and collaboration

OpenAI

Direct OpenAI function calling

Python Core Features

Tool Filtering

Advanced glob pattern filtering for tool selection

Meta Tools

Dynamic tool discovery and execution (Beta)

Installation

Set up the Python SDK

Basic Usage

Core concepts and fundamental usage patterns

Next Steps

1. Learn about tool filtering
2. Explore Meta Tools

Resources

• GitHub Repository: Complete source code and examples
• Examples: LangChain, CrewAI, and meta tools examples
• Issues: Report bugs or request features
• PyPI Package: Package details and version history