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Azure-Samples/langchain-agent-python

page_typelanguagesproductsurlFragmentnamedescription
sample
python
azure-openai
azure-container-apps
azure
langchain
langchain-agent-mcp
LangChain Python Agent with Model Context Protocol (MCP)
A production-ready LangChain agent in Python using Azure OpenAI Responses API with MCP server integration, deployed on Azure Container Apps

LangChain MCP Agent

Open in GitHub Codespaces

This sample demonstrates aproduction-ready LangChain agent that uses theOpenAI Responses API withModel Context Protocol (MCP) for tool integration. The agent usesAzure OpenAI GPT-5-mini withEntra ID authentication,PostgreSQL with pgvector for semantic search, and is deployed as microservices onAzure Container Apps.

This is a simplified, Python version inspired by theMicrosoft AI Tour WRK540 workshop, but uses the same product data and instructions.

Features

LangChain with Responses API - Uses OpenAI's latest Responses API for native MCP tool support
Azure OpenAI GPT-5-mini - Latest reasoning model deployed via Azure
PostgreSQL with pgvector - Semantic search over product catalog using vector embeddings
Entra ID Authentication - Keyless authentication using Managed Identity (no API keys)
MCP Server - FastMCP server with database and semantic search tools
Microservices Architecture - Agent and MCP server deployed as independent container apps
Infrastructure as Code - Complete Bicep templates with Azure best practices
One-command Deployment - Deploy everything withazd up

Architecture

┌─────────────────────────────────────────────────────────────┐│                        Azure Cloud                          ││                                                             ││  ┌──────────────────────────────────────────────────────┐  ││  │         Azure Container Apps Environment             │  ││  │                                                        │  ││  │  ┌─────────────────┐      ┌──────────────────┐      │  ││  │  │  Agent Container │──────│  MCP Server      │      │  ││  │  │  - LangChain     │ HTTP │  - PostgreSQL    │      │  ││  │  │  - Responses API │◄─────│  - Semantic      │      │  ││  │  │                  │      │    Search       │      │  ││  │  └─────────┬────────┘      └────────┬─────────┘      │  ││  │            │                         │                 │  ││  └────────────┼─────────────────────────┼─────────────────┘  ││               │ Entra ID                │                    ││               ▼                         ▼                    ││  ┌─────────────────────────┐  ┌──────────────────────┐     ││  │   Azure OpenAI          │  │  PostgreSQL          │     ││  │   - GPT-5-mini          │  │  - pgvector          │     ││  │   - text-embedding-     │  │  - Zava database     │     ││  │     3-small             │  │                      │     ││  └─────────────────────────┘  └──────────────────────┘     │└─────────────────────────────────────────────────────────────┘Local Development:┌──────────────┐      ┌──────────────┐      ┌──────────────┐│ agent.py     │─HTTP─│ MCP Server   │      │ Azure OpenAI ││ (localhost)  │◄─────│ (localhost:  │─────▶│ (cloud)      ││              │      │  8000)       │ Entra│              │└──────────────┘      └──────────────┘  ID  └──────────────┘

Prerequisites

Cloud Deployment

Local Development

Quick Start (Recommended)

  • GitHub Codespaces - Click the badge above to start in a pre-configured environment with all tools installed!

Quick Start

Deploy to Azure (5 minutes)

# 1. Login to Azureaz loginazd auth login# 2. Deploy everythingazd up

That's it! Theazd up command will:

  • Provision Azure OpenAI with GPT-5-mini
  • Create Container Apps environment
  • Build and deploy both the agent and MCP server containers
  • Configure networking and managed identity
  • Set up monitoring with Application Insights

After deployment completes, you'll see output like:

SUCCESS: Your application was provisioned and deployed to Azure!Endpoints:  - MCP Server: https://ca-mcp-abc123.region.azurecontainerapps.io  - Agent: https://ca-agent-abc123.region.azurecontainerapps.io

Local Development

Option 1: Use Azure Database (Recommended)

# 1. Deploy to Azure firstazd up# 2. Get configuration and set MCP server URLazd env get-values> .env.localecho"MCP_SERVER_URL=http://localhost:8000">> .env.local# 3. Start MCP server (Terminal 1)cd mcpsource ../.env.localpython app.py# 4. Start agent server (Terminal 2)cd agentsource ../.env.localPORT=8001 python app.py# 5. Open browser to http://localhost:8001

Option 2: Full Local Stack

# 1. Start PostgreSQL with pgvectordocker-compose up -d# 2. Configure environmentcp .env.example .env.local# Edit .env.local with your Azure OpenAI credentials# 3. Initialize databasecd datasource ../.env.localpython generate_database.py# 4. Regenerate embeddings (required if your Azure OpenAI uses a different embedding model)python regenerate_embeddings.py# 5. Start MCP server (Terminal 1)cd mcpsource ../.env.localpython app.py# 6. Start agent server (Terminal 2)cd agentsource ../.env.localPORT=8001 python app.py# 7. Open browser to http://localhost:8001

VS Code Tasks:

The project includes pre-configured VS Code tasks. PressCmd+Shift+P (Mac) orCtrl+Shift+P (Windows/Linux) and select "Tasks: Run Task" to see available tasks:

  • Start MCP Server
  • Start Agent
  • Start PostgreSQL (Docker)
  • Initialize Database

Ports:

  • MCP Server:8000
  • Agent/Chat UI:8001 (set viaPORT environment variable)

Project Structure

langchain-agent-python/├── azure.yaml                      # Azure Developer CLI configuration├── README.md                       # This file├── LICENSE                         # MIT License├── SECURITY.md                     # Security policy├── SUPPORT.md                      # Support information├── CODE_OF_CONDUCT.md             # Code of conduct├── .env.example                    # Template for environment variables├── .gitignore                      # Git ignore rules│├── agent/                          # LangChain Agent Service│   ├── agent.py                    # Main agent with Responses API│   ├── config.py                   # Pydantic configuration│   ├── instructions.txt            # System prompt for agent│   ├── requirements.txt            # Python dependencies│   ├── Dockerfile                  # Container definition│   └── .dockerignore              # Docker ignore rules│├── mcp/                            # MCP Server Service│   ├── mcp_server.py              # FastMCP server with tools│   ├── requirements.txt            # Python dependencies│   ├── Dockerfile                  # Container definition│   └── .dockerignore              # Docker ignore rules│└── infra/                          # Infrastructure as Code    ├── main.bicep                  # Main deployment template    ├── main.parameters.json        # Default parameters    ├── abbreviations.json          # Resource name abbreviations    ├── core/                       # Reusable Bicep modules    │   ├── ai/    │   │   └── cognitiveservices.bicep    │   ├── host/    │   │   ├── container-apps-environment.bicep    │   │   └── container-registry.bicep    │   ├── monitor/    │   │   └── monitoring.bicep    │   └── security/    │       ├── managed-identity.bicep    │       └── role.bicep    └── app/                        # Application-specific modules        ├── agent.bicep        └── mcp-server.bicep

How It Works

1.Agent (LangChain + Responses API)

The agent uses LangChain'sChatOpenAI with the newResponses API for native MCP tool support:

fromlangchain_openaiimportChatOpenAIllm=ChatOpenAI(model="gpt-5-mini",base_url=f"{endpoint}/openai/v1/",api_key=token_provider,model_kwargs={"use_responses_api":True})# Bind MCP tools from servermcp_tools=get_mcp_tools(mcp_server_url)llm=llm.bind_tools(mcp_tools)

2.MCP Server (FastMCP)

The MCP server exposes tools via FastMCP:

fromfastmcpimportFastMCPmcp=FastMCP("Data Analysis Tools")@mcp.tool()defexecute_query(query:str)->dict:"""Execute SQL query on database."""# ... implementation

3.Environment-based Configuration

Both services use environment-aware configuration:

  • Local: Uses.env.local file, connects tolocalhost:8000 for MCP
  • Production: Uses environment variables from Container Apps, connects via HTTPS to cloud MCP server

4.Secure Authentication

  • Azure OpenAI: Uses Managed Identity (Entra ID) - no API keys
  • MCP Server: Internal Container Apps networking
  • Monitoring: Application Insights for observability

Available MCP Tools

The MCP server provides these tools to the agent:

  1. get_current_utc_date() - Returns current UTC timestamp for time-sensitive queries
  2. get_table_schemas() - Returns PostgreSQL database schema information
  3. execute_sales_query(query: str) - Executes SQL queries on PostgreSQL database
  4. semantic_search_products(query_description: str) - Semantic product search using pgvector

Database

The sample usesAzure PostgreSQL Flexible Server withpgvector for semantic product search.

Key Features:

  • 10-table retail schema (products, orders, customers, inventory, etc.)
  • Vector embeddings for semantic search using Azure OpenAI
  • Pre-populated Zava DIY product catalog with ~424 products
  • Natural language queries like "waterproof outdoor electrical boxes"

Data Files Included:

This repository includes pre-generated data files in thedata/ folder, so you don't need to download anything:

  • products_pregenerated.json - 424 products with pre-computed embeddings
  • customers_pregenerated.json - 500 sample customers
  • orders_pregenerated.json - 2000 sample orders

Setup:

  • Production: Automatically provisioned duringazd up
  • Local: Rundocker-compose up -d thenpython data/generate_database.py

Customization

Add New MCP Tools

Editmcp/mcp_server.py:

@mcp.tool()defmy_custom_tool(param:str)->dict:"""Description of what this tool does."""# Your implementationreturn {"result":"data"}

The tool is automatically exposed via the/tools endpoint in OpenAI function format.

Change the Model

Editinfra/main.parameters.json:

{"openAiModelName": {"value":"gpt-5-mini"// Change to gpt-4o, etc.  }}

Modify System Instructions

Editagent/instructions.txt to change the agent's behavior and personality.

Monitoring

View logs and metrics in Azure Portal:

# Open Application Insightsazd monitor# View container logsaz containerapp logs show --name<agent-name> --resource-group<rg-name> --follow

Troubleshooting

"Deployment quota exceeded"
→ Try a different region:azd env set AZURE_LOCATION eastus2

"Authentication failed"
→ Ensure you're logged in:az login && azd auth login

"GPT-5-mini not available"
→ Model may not be available in your region - try eastus or westus

"Container apps failing to start"
→ Check logs:azd monitor

"MCP tools not loading"
→ Ensure MCP_SERVER_URL is accessible from the agent

Clean Up

Remove all Azure resources:

azd down

Resources

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA). For details, visithttps://cla.opensource.microsoft.com.

License

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

Questions or feedback? Open an issue onGitHub or seeSUPPORT.md.

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