Integrating LangChain with SAP Ariba Source Data via CData Connect AI

Dibyendu Datta
Dibyendu Datta
Lead Technology Evangelist
Leverage the CData Connect AI Remote MCP Server to enable LangChain agents to securely access and act on SAP Ariba Source data within automated workflows.

LangChain is a framework used by developers, data engineers, and AI practitioners for building AI-powered applications and workflows by combining reasoning models (LLMs), tools, APIs, and data connectors. By integrating LangChain with CData Connect AI through the built-in MCP Server, workflows can effortlessly access and interact with live SAP Ariba Source data in real time.

CData Connect AI offers a secure, low-code environment to connect SAP Ariba Source and other data sources, removing the need for complex ETL and enabling seamless automation across business applications with live data.

This article outlines how to configure SAP Ariba Source connectivity in CData Connect AI, register the MCP server with LangChain, and build a workflow that queries SAP Ariba Source data in real time.

Prerequisites

Step 1: Configure SAP Ariba Source Connectivity for LangChain

Before LangChain can access SAP Ariba Source, a SAP Ariba Source connection must be created in CData Connect AI. This connection is then exposed to LangChain through the remote MCP server.

  1. Log in to Connect AI click Sources, and then click + Add Connection
  2. From the available data sources, choose SAP Ariba Source
  3. Enter the necessary authentication properties to connect to SAP Ariba Source

    In order to connect with SAP Ariba Source, set the following:

    • API: Specify which API you would like the provider to retrieve SAP Ariba data from. Select the Supplier, Sourcing Project Management, or Contract API based on your business role (possible values are SupplierDataAPIWithPaginationV4, SourcingProjectManagementAPIV2, or ContractAPIV1).
    • DataCenter: The data center where your account's data is hosted.
    • Realm: The name of the site you want to access.
    • Environment: Indicate whether you are connecting to a test or production environment (possible values are TEST or PRODUCTION).

    If you are connecting to the Supplier Data API or the Contract API, additionally set the following:

    • User: Id of the user on whose behalf API calls are invoked.
    • PasswordAdapter: The password associated with the authenticating User.

    If you're connecting to the Supplier API, set ProjectId to the Id of the sourcing project you want to retrieve data from.

    Authenticating with OAuth

    After setting connection properties, you need to configure OAuth connectivity to authenticate.

    • Set AuthScheme to OAuthClient.
    • Register an application with the service to obtain the APIKey, OAuthClientId and OAuthClientSecret.

      For more information on creating an OAuth application, refer to the Help documentation.

    Automatic OAuth

    After setting the following, you are ready to connect:

      APIKey: The Application key in your app settings. OAuthClientId: The OAuth Client Id in your app settings. OAuthClientSecret: The OAuth Secret in your app settings.

    When you connect, the provider automatically completes the OAuth process:

    1. The provider obtains an access token from SAP Ariba and uses it to request data.
    2. The provider refreshes the access token automatically when it expires.
    3. The OAuth values are saved in memory relative to the location specified in OAuthSettingsLocation.
  4. Click Save & Test
  5. Once authenticated, open the Permissions tab in the SAP Ariba Source connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

LangChain authenticates to Connect AI using an account email and a Personal Access Token (PAT). Creating separate PATs for each integration is recommended to maintain access control granularity.

  1. In Connect AI, select the Gear icon in the top-right to open Settings
  2. Under Access Tokens, select Create PAT
  3. Provide a descriptive name for the token and select Create
  4. Copy the token and store it securely. The PAT will only be visible during creation

With the SAP Ariba Source connection configured and a PAT generated, LangChain is prepared to connect to SAP Ariba Source data through the CData MCP server.

Note: You can also generate a PAT from LangChain in the Integrations section of Connect AI. Simply click Connect --> Create PAT to generate it.

Step 2: Connect to the MCP server in LangChain

To connect LangChain with CData Connect AI Remote MCP Server and use OpenAI (ChatGPT) for reasoning, you need to configure your MCP server endpoint and authentication values in a config.py file. These values allow LangChain to call the MCP server tools, while OpenAI handles the natural language reasoning.

  1. Create a folder for LangChain MCP
  2. Create two Python files within the folder: config.py and langchain.py
  3. In config.py, create a class Config to define your MCP server authentication and URL. You need to provide your Base64-encoded CData Connect AI username and PAT (obtained in the prerequisites): 
     
class Config: 

      MCP_BASE_URL = "https://mcp.cloud.cdata.com/mcp"   #MCP Server URL 
      MCP_AUTH = "base64encoded(EMAIL:PAT)"   #Base64 encoded Connect AI Email:PAT

    Note: You can create the base64 encoded version of MCP_AUTH using any Base64 encoding tool.

  4. In langchain.py, set up your MCP server and MCP client to call the tools and prompts: 
     
""" 
Integrates a LangChain ReAct agent with CData Connect AI MCP server. 
The script demonstrates fetching, filtering, and using tools with an LLM for agent-based reasoning. 
""" 

import asyncio 
from langchain_mcp_adapters.client import MultiServerMCPClient 
from langchain_openai import ChatOpenAI 
from langgraph.prebuilt import create_react_agent 
from config import Config 

async def main(): 

# Initialize MCP client with one or more server URLs
mcp_client = MultiServerMCPClient( 
  connections={ 
    "default": { # you can name this anything 
    "transport": "streamable_http", 
    "url": Config.MCP_BASE_URL, 
    "headers": {"Authorization": f"Basic {Config.MCP_AUTH}"}, 
    } 
  } 
) 

# Load remote MCP tools exposed by the server 
all_mcp_tools = await mcp_client.get_tools() 
print("Discovered MCP tools:", [tool.name for tool in all_mcp_tools]) 

# Create and run the ReAct style agent 
llm = ChatOpenAI( 
  model="gpt-4o",  
  temperature=0.2, 
  api_key="YOUR_OPEN_API_KEY" #Use your OpenAI API Key here, this can be found here: https://platform.openai.com/ 
) 

agent = create_react_agent(llm, all_mcp_tools) 

user_prompt = "How many tables are available in SAPAribaSource1?" #Change prompts as per need 
print(f"
User prompt: {user_prompt}") 

# Send a prompt asking the agent to use the MCP tools 
response = await agent.ainvoke( 
  { "messages": [{ "role": "user", "content": (user_prompt),}]} 
) 

# Print out the agent's final response 
final_msg = response["messages"][-1].content 
print("Agent final response:", final_msg) 

if __name__ == "__main__": 

asyncio.run(main())

Step 3: Install the LangChain and LangGraph packages

Since this workflow uses LangChain together with CData Connect AI MCP and integrates OpenAI for reasoning, you need to install the required Python packages.

Run the following command in your project terminal:

 
pip install langchain-mcp-adapters langchain-openai langgraph

Step 4: Prompt SAP Ariba Source using LangChain (via the MCP server)

  1. When the installation finishes, run python langchain.py to execute the script
  2. The script connects to the MCP server and discovers the CData Connect AI MCP tools available for querying your connected data
  3. Supply a prompt (e.g., "How many tables are available in SAP Ariba Source?")
  4. Accordingly, the agent responds with the results

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