extend-ai-toolkit 1.1.0

Extend AI Toolkit

Extend AI Toolkit

Overview

The Extend AI Toolkit provides a python based implementation of tools to integrate with Extend APIs for multiple AI frameworks including Anthropic's Model Context Protocol (MCP), OpenAI, LangChain, and CrewAI. It enables users to delegate certain actions in the spend management flow to AI agents or MCP-compatible clients like Claude desktop.

These tools are designed for existing Extend users with API keys. If you are not signed up with Extend and would like to learn more about our modern, easy-to-use virtual card and spend management platform for small- and medium-sized businesses, you can check us out at paywithextend.com.

Features

• Multiple AI Framework Support: Works with Anthropic Model Context Protocol, OpenAI Agents, LangChain LangGraph & ReAct, and CrewAI frameworks
• Comprehensive Tool Set: Supports all of Extend's major API functionalities, spanning our Credit Card, Virtual Card, Transaction & Expense Management endpoints

Installation

You don't need this source code unless you want to modify the package. If you just want to use the package run:

pip install extend_ai_toolkit

Requirements

• Python: Version 3.10 or higher
• Extend API Key: Sign up at paywithextend.com to obtain an API key
• Framework-specific Requirements:
  • LangChain: langchain and langchain-openai packages
  • OpenAI: openai package
  • CrewAI: crewai package
  • Anthropic: anthropic package (for Claude)

Configuration

The library needs to be configured with your Extend API key and API, either through environment variables or command line arguments:

--api-key=your_api_key_here --api-secret=your_api_secret_here 

or via environment variables:

EXTEND_API_KEY=your_api_key_here
EXTEND_API_SECRET=your_api_secret_here

Available Tools

The toolkit provides a comprehensive set of tools organized by functionality:

Virtual Cards

• get_virtual_cards: Fetch virtual cards with optional filters
• get_virtual_card_detail: Get detailed information about a specific virtual card

Credit Cards

• get_credit_cards: List all credit cards
• get_credit_card_detail: Get detailed information about a specific credit card

Transactions

• get_transactions: Fetch transactions with various filters
• get_transaction_detail: Get detailed information about a specific transaction
• update_transaction_expense_data: Update expense-related data for a transaction

Expense Management

• get_expense_categories: List all expense categories
• get_expense_category: Get details of a specific expense category
• get_expense_category_labels: Get labels for an expense category
• create_expense_category: Create a new expense category
• create_expense_category_label: Add a label to an expense category
• update_expense_category: Modify an existing expense category
• create_receipt_attachment: Upload a receipt (and optionally attach to a transaction)
• automatch_receipts: Initiate async job to automatch uploaded receipts to transactions
• get_automatch_status: Get the status of an automatch job

Usage Examples

Model Context Protocol

The toolkit provides resources in the extend_ai_toolkit.modelcontextprotocol package to help you build an MCP server.

Development

Test Extend MCP server locally using MCP Inspector:

npx @modelcontextprotocol/inspector python extend_ai_toolkit/modelcontextprotocol/main.py --tools=all

Claude Desktop Integration

Add this tool as an MCP server to Claude Desktop by editing the config file:

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

if you want to use the create_receipt_attachment tool with claude desktop you'll need to install the filesystem mcp server via npm install @modelcontextprotocol/server-filesystem add then add to the config file as well.

Please note: due to current limitations images uploaded directly to the Claude Desktop cannot be uploaded to Extend due to the fact that the Claude Desktop app does not have access to the underlying image data. This is why the Filesystem MCP Server is necessary.

With the addition of Filesystem, you can setup a dedicated folder for receipts, and tell Claude it to upload the receipt and automatch it to the most likely transaction. Alternatively, if you know the transaction you want to attach the receipt to then you can tell Claude to upload the receipt for that transaction (and skip the automatch process.

{
  "extend-mcp": {
    "command": "python",
    "args": [
      "-m",
      "extend_ai_toolkit.modelcontextprotocol.main",
      "--tools=all"
    ],
    "env": {
      "EXTEND_API_KEY": "apik_XXXX",
      "EXTEND_API_SECRET": "XXXXX"
    }
  },
  // optional: if you want to use the create_receipt_attachment tool
  "filesystem": { 
    "command": "npx",
    "args": [
      "-y",
      "@modelcontextprotocol/server-filesystem",
      "/path/to/receipts/folder"
    ]
  }
}

Remote Execution

You can also run your server remotely and communicate via SSE transport:

python -m extend_ai_toolkit.modelcontextprotocol.main_sse --tools=all --api-key="apikey" --api-secret="apisecret"

and optionally connect using the MCP terminal client:

python -m extend_ai_toolkit.modelcontextprotocol.client.mcp_client --mcp-server-host localhost --mcp-server-port 8000 --llm-provider=anthropic --llm-model=claude-3-5-sonnet-20241022

OpenAI

import os
from langchain_openai import ChatOpenAI
from extend_ai_toolkit.openai.toolkit import ExtendOpenAIToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the OpenAI toolkit
extend_openai_toolkit = ExtendOpenAIToolkit(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Create an agent with the tools
extend_agent = Agent(
    name="Extend Agent",
    instructions="You are an expert at integrating with Extend",
    tools=extend_openai_toolkit.get_tools()
)

LangChain

import os
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from extend_ai_toolkit.langchain.toolkit import ExtendLangChainToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the LangChain toolkit
extend_langchain_toolkit = ExtendLangChainToolkit(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Create tools for the agent
tools = extend_langchain_toolkit.get_tools()

# Create the agent executor
langgraph_agent_executor = create_react_agent(
    ChatOpenAI(model="gpt-4"),
    tools
)

CrewAI

import os
from extend_ai_toolkit.crewai.toolkit import ExtendCrewAIToolkit
from extend_ai_toolkit.shared import Configuration, Scope, Product, Actions

# Initialize the CrewAI toolkit
toolkit = ExtendCrewAIToolkit(
    api_key=os.environ.get("EXTEND_API_KEY"),
    api_secret=os.environ.get("EXTEND_API_SECRET"),
    configuration=Configuration(
        scope=[
            Scope(Product.VIRTUAL_CARDS, actions=Actions(read=True)),
            Scope(Product.CREDIT_CARDS, actions=Actions(read=True)),
            Scope(Product.TRANSACTIONS, actions=Actions(read=True)),
        ]
    )
)

# Configure the LLM (using Claude)
toolkit.configure_llm(
    model="claude-3-opus-20240229",
    api_key=os.environ.get("ANTHROPIC_API_KEY")
)

# Create the Extend agent
extend_agent = toolkit.create_agent(
    role="Extend Integration Expert",
    goal="Help users manage virtual cards, view credit cards, and check transactions efficiently",
    backstory="You are an expert at integrating with Extend, with deep knowledge of virtual cards, credit cards, and transaction management.",
    verbose=True
)

# Create a task for handling user queries
query_task = toolkit.create_task(
    description="Process and respond to user queries about Extend services",
    agent=extend_agent,
    expected_output="A clear and helpful response addressing the user's query",
    async_execution=True
)

# Create a crew with the agent and task
crew = toolkit.create_crew(
    agents=[extend_agent],
    tasks=[query_task],
    verbose=True
)

# Run the crew
result = crew.kickoff()

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.