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:
langchainandlangchain-openaipackages - OpenAI:
openaipackage - CrewAI:
crewaipackage - Anthropic:
anthropicpackage (for Claude)
- LangChain:
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
Custom Authorization (paywithextend 2.0+)
When you upgrade to paywithextend>=2.0.0, the SDK accepts reusable authorization
strategies. You can pass any implementation (including the new BearerAuth) to
the toolkit:
from extend.auth import BearerAuth
from extend_ai_toolkit.shared import ExtendAPI
extend_api = ExtendAPI.from_auth(BearerAuth(jwt_token="your-jwt-token"))
If you're still using paywithextend 1.x, continue providing api_key and
api_secret; the toolkit adapts automatically.
Available Tools
The toolkit provides a comprehensive set of tools organized by functionality:
Virtual Cards
get_virtual_cards: Fetch virtual cards with optional filtersget_virtual_card_detail: Get detailed information about a specific virtual card
Credit Cards
get_credit_cards: List all credit cardsget_credit_card_detail: Get detailed information about a specific credit card
Transactions
get_transactions: Fetch transactions with various filtersget_transaction_detail: Get detailed information about a specific transactionupdate_transaction_expense_data: Update expense-related data for a transaction
Expense Management
get_expense_categories: List all expense categoriesget_expense_category: Get details of a specific expense categoryget_expense_category_labels: Get labels for an expense categorycreate_expense_category: Create a new expense categorycreate_expense_category_label: Add a label to an expense categoryupdate_expense_category: Modify an existing expense categorycreate_receipt_attachment: Upload a receipt (and optionally attach to a transaction)automatch_receipts: Initiate async job to automatch uploaded receipts to transactionsget_automatch_status: Get the status of an automatch jobsend_receipt_reminder: Send a reminder (via email) for a transaction missing a receipt
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.default_instance(
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.default_instance(
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.default_instance(
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.