Integrating LangGraph with Azure Data Lake Storage Data via CData Connect AI

Dibyendu Datta
Lead Technology Evangelist

Leverage CData Connect AI to enable LangGraph agents to securely access and act on Azure Data Lake Storage data within automated workflows.

LangGraph is a framework for building and visualizing intelligent, graph-based AI workflows that combine reasoning models (LLMs) with tool integrations and data operations. By integrating it with the CData Connect AI, you can enable agents to securely access, query, and act on live enterprise data in real-time via a standardized toolset.

CData Connect AI is a managed MCP-platform that allows you to expose your data sources (such as Azure Data Lake Storage) through the Model Context Protocol (MCP). This means your AI agents can work with metadata, catalogs, tables, and SQL-enabled data access from over 350 data sources, without complex ETL or custom integrations.

This article explores how to register the MCP endpoint in LangGraph, configure data source connectivity via CData Connect AI, and build a workflow that queries and visualizes live data (for example, Azure Data Lake Storage objects) on demand. It demonstrates how to use the built-in MCP toolset (getCatalogs, getSchemas, getTables, queryData, etc.) to enable natural-language agents to interact with your enterprise data securely and interactively.

Prerequisites

An account in CData Connect AI.
Python version 3.10 or higher, to install the LangGraph packages.
Generate and save an OpenAI API key.
Install Visual Studio Code in your system.
Obtain and save the LangGraph API key from LangGraph.

Step 1: Configure Azure Data Lake Storage Connectivity for LangGraph

Before LangGraph can access Azure Data Lake Storage, a Azure Data Lake Storage connection must be created in CData Connect AI. This connection is then exposed to LangGraph through the remote MCP server.

Log in to Connect AI click Sources, and then click + Add Connection
From the available data sources, choose Azure Data Lake Storage
Enter the necessary authentication properties to connect to Azure Data Lake Storage.
Authenticating to a Gen 1 DataLakeStore Account

Gen 1 uses OAuth 2.0 in Entra ID (formerly Azure AD) for authentication.

For this, an Active Directory web application is required. You can create one as follows:
1. Sign in to your Azure Account through the .
2. Select "Entra ID" (formerly Azure AD).
3. Select "App registrations".
4. Select "New application registration".
5. Provide a name and URL for the application. Select Web app for the type of application you want to create.
6. Select "Required permissions" and change the required permissions for this app. At a minimum, "Azure Data Lake" and "Windows Azure Service Management API" are required.
7. Select "Key" and generate a new key. Add a description, a duration, and take note of the generated key. You won't be able to see it again.
To authenticate against a Gen 1 DataLakeStore account, the following properties are required:
- Schema: Set this to ADLSGen1.
- Account: Set this to the name of the account.
- OAuthClientId: Set this to the application Id of the app you created.
- OAuthClientSecret: Set this to the key generated for the app you created.
- TenantId: Set this to the tenant Id. See the property for more information on how to acquire this.
- Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
Authenticating to a Gen 2 DataLakeStore Account

To authenticate against a Gen 2 DataLakeStore account, the following properties are required:
- Schema: Set this to ADLSGen2.
- Account: Set this to the name of the account.
- FileSystem: Set this to the file system which will be used for this account.
- AccessKey: Set this to the access key which will be used to authenticate the calls to the API. See the property for more information on how to acquire this.
- Directory: Set this to the path which will be used to store the replicated file. If not specified, the root directory will be used.
Click Save & Test
Once authenticated, open the Permissions tab in the Azure Data Lake Storage connection and configure user-based permissions as required

Generate a Personal Access Token (PAT)

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

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

With the Azure Data Lake Storage connection configured and a PAT generated, LangGraph is prepared to connect to Azure Data Lake Storage data through the CData MCP server.

Step 2: Set up your development environment

Set up your project directory and install the required dependencies to connect LangGraph with CData Connect AI and use OpenAI LLM for reasoning. This setup enables LangGraph to call the Azure Data Lake Storage MCP server tools exposed by Connect AI while OpenAI processes the natural language reasoning.

Create a new folder for your LangGraph project:
```
mkdir LangGraph cd LangGraph
```

Install the required Python packages:

pip install langgraph langchain-openai langchain-mcp-adapters python-dotenv "langgraph-cli[inmem]"

Ensure you have Python 3.10+ and a valid OpenAI API key configured in your environment.

Step 3: Configure the MCP connection environment variables

LangGraph uses environment variables to connect to the CData Connect AI and define the API credentials and configuration settings. Store these credentials in a .env file to keep them secure and reusable. LangGraph automatically reads this file at runtime, so the script can authenticate and communicate with the MCP server without hardcoding sensitive information.

Create a new file named .env in your project directory.

Add the following environment variables to define your LangGraph, CData Connect AI, and OpenAI configuration:

# LangSmith (Optional) 
LANGSMITH_API_KEY=lsv2_pt_xxxx   #LangSmith API Key
LANGCHAIN_TRACING_V2=true 
LANGCHAIN_PROJECT=LangGraph-Demo 

# MCP Configuration 
MCP_BASE_URL=https://mcp.cloud.cdata.com/mcp   #MCP Server URL
MCP_AUTH=base64encoded(EMAIL:PAT)   #Base64 encoded Connect AI Email:PAT
OPENAI_API_KEY=sk-proj-xxxx

Save the file. LangGraph uses these values to authenticate with the CData Connect AI Azure Data Lake Storage MCP server to connect with the Azure Data Lake Storage data and initialize the OpenAI model for reasoning.

Note: You can generate the Base64-encoded authorization string using any online Base64 encoder, such as Base64 encoding tool. Encode your CData Connect AI username and PAT (obtained in the prerequisites).

Step 4: Create the LangGraph agent script

In this step, you need to create a Python script that connects LangGraph to your CData Connect AI MCP server. The script retrieves the available MCP tools, such as getCatalogs, getSchemas, and queryData, builds a LangGraph workflow, and runs a natural language prompt against your connected Azure Data Lake Storage data.

The workflow uses the MCP to securely retrieve live Azure Data Lake Storage data from Connect AI and uses OpenAI GPT-4o to interpret and reason over that data. You also expose the graph so you can visualize it later in LangGraph Studio.

Create the file

Create a new Python file named test.py inside your LangGraph project folder.

Add the following code

Use the following script into test.py:

import asyncio
import os
import operator
from typing_extensions import TypedDict, Annotated
from dotenv import load_dotenv

from langgraph.graph import StateGraph, START, END
from langchain.agents import create_agent
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langchain_core.messages import BaseMessage, HumanMessage

# Load environment variables
load_dotenv()

# Define the agent state
class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], operator.add]

# Define the async agent logic
async def run_agent(user_prompt: str) -> str:
    MCP_BASE_URL = os.getenv("MCP_BASE_URL")
    MCP_AUTH = os.getenv("MCP_AUTH")
    OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

    print("Connecting to the MCP server...")
    mcp_client = MultiServerMCPClient(
        connections={
            "default": {
                "transport": "streamable_http",
                "url": MCP_BASE_URL,
                "headers": {"Authorization": f"Basic {MCP_AUTH}"} if MCP_AUTH else {},
            }
        }
    )

    print("Loading available MCP tools...")
    all_mcp_tools = await mcp_client.get_tools()
    print(f"Loaded tools: {[tool.name for tool in all_mcp_tools]}")

    # Initialize the LLM
    llm = ChatOpenAI(model="gpt-4o", temperature=0.2, api_key=OPENAI_API_KEY)

    print("Creating the LangGraph agent...")
    agent = create_agent(
        model=llm,
        tools=all_mcp_tools,
        system_prompt="You are a helpful assistant. Use tools when needed."
    )

    # Build the workflow graph
    builder = StateGraph(AgentState)
    builder.add_node("agent", agent)
    builder.add_edge(START, "agent")
    builder.add_edge("agent", END)
    graph_instance = builder.compile()

    print(f"Processing user query: {user_prompt}\n")
    initial_state = {"messages": [HumanMessage(content=user_prompt)]}
    result = await graph_instance.ainvoke(initial_state)
    print(f"Agent Response:\n{result['messages'][-1].content}")

# Expose the graph for visualization
builder = StateGraph(AgentState)
builder.add_node(
    "agent",
    create_agent(
        model=ChatOpenAI(model="gpt-4o", temperature=0.2, api_key=os.getenv("OPENAI_API_KEY")),
        tools=[],
        system_prompt="You are a helpful assistant."
    )
)
builder.add_edge(START, "agent")
builder.add_edge("agent", END)
graph = builder.compile()

if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--serve", action="store_true", help="Run visualization server")
    args = parser.parse_args()

    if args.serve:
        print("To visualize the graph, run:")
        print("langgraph dev")
    else:
        asyncio.run(run_agent("List the first 2 catalogs available"))

Step 5: Configure the LangGraph project

Configure the LangGraph project so the CLI recognizes the workflow graph and environment settings. Create a configuration file that registers the graph for use in LangGraph Studio or during local visualization runs.

Create the configuration file

Create a new file named langgraph.json in your project directory.

Add the following configuration

Use the content below in the langgraph.json file:

{
  "dependencies": ["."],
  "graphs": {
    "agent": "./test.py:graph"
  },
  "env": ".env"
}

Step 6: Prompt Azure Data Lake Storage using LangGraph (via Connect AI)

Run the LangGraph development server to view and interact with your workflow in LangGraph Studio. This allows direct visualization of how the agent processes prompts, invokes tools, and retrieves Azure Data Lake Storage data through the MCP server.

Start the LangGraph development server

Open a terminal in your project directory and run:

langgraph dev

Access the Studio interface

After the server starts, LangGraph launches a local API and provides a link to the Studio UI:

https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024

Ideally, the link opens automatically when the command is run. If not, open this link in your browser to load the LangGraph Studio dashboard.

Interact with the agent

In the Studio interface, enter a natural language prompt such as:

Show all Azure Data Lake Storage tables available in my catalog

LangGraph displays a real-time visualization of the agent's reasoning flow, showing how it interprets the prompt, calls the appropriate MCP tools, and retrieves live data from Azure Data Lake Storage.

Query and visualize the Azure Data Lake Storage data

Query and visualize the Azure Data Lake Storage data (contd.)

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