LangChain Agent MCP Server

A production-ready MCP server exposing LangChain agent capabilities through the Model Context Protocol, deployed on Google Cloud Run.

🚀 Overview

This is a standalone backend service that wraps a LangChain agent as a single, high-level MCP Tool. The server is built with FastAPI and deployed on Google Cloud Run, providing a scalable, production-ready solution for exposing AI agent capabilities to any MCP-compliant client.

Live Service: https://langchain-agent-mcp-server-554655392699.us-central1.run.app

✨ Features

✅ MCP Compliance - Full Model Context Protocol support
✅ LangChain Agent - Multi-step reasoning with ReAct pattern
✅ Playwright Sandbox - Interactive preview of accessibility snapshots (NEW!)
✅ Google Cloud Run - Scalable, serverless deployment
✅ Tool Support - Extensible framework for custom tools
✅ Production Ready - Error handling, logging, and monitoring
✅ Docker Support - Containerized for easy deployment

🏗️ Architecture

Component	Technology	Purpose
Backend Framework	FastAPI	High-performance, asynchronous web server
Agent Framework	LangChain	Multi-step reasoning and tool execution
Deployment	Google Cloud Run	Serverless, auto-scaling hosting
Containerization	Docker	Consistent deployment environment
Protocol	Model Context Protocol (MCP)	Standardized tool and context sharing

🛠️ Quick Start

Prerequisites

Python 3.11+
OpenAI API key
Google Cloud account (for Cloud Run deployment)
Docker (optional, for local testing)

Local Development

Clone the repository:

git clone https://github.com/mcpmessenger/LangchainMCP.git
cd LangchainMCP

Install dependencies:

# Windows
py -m pip install -r requirements.txt

# Linux/Mac
pip install -r requirements.txt

Set up environment variables: Create a .env file:

OPENAI_API_KEY=your-openai-api-key-here
OPENAI_MODEL=gpt-4o-mini
PORT=8000

Run the server:

# Windows
py run_server.py

# Linux/Mac
python run_server.py

Test the endpoints:
- Health: http://localhost:8000/health
- Manifest: http://localhost:8000/mcp/manifest
- API Docs: http://localhost:8000/docs
- Playwright Sandbox: http://localhost:8080/sandbox (after starting frontend)
Start the frontend (optional):
```
# Install frontend dependencies (first time only)
npm install

# Start frontend dev server
npm run dev
```
Then visit http://localhost:8080/sandbox to use the Playwright Sandbox preview feature.

☁️ Google Cloud Run Deployment

The server is designed for deployment on Google Cloud Run. See our comprehensive deployment guides:

DEPLOY_CLOUD_RUN_WINDOWS.md - Windows deployment guide
DEPLOY_CLOUD_RUN.md - General deployment guide
QUICK_DEPLOY.md - Quick reference

Quick Deploy

# Windows PowerShell
.\deploy-cloud-run.ps1 -ProjectId "your-project-id" -Region "us-central1"

# Linux/Mac
./deploy-cloud-run.sh your-project-id us-central1

Current Deployment

Service URL: https://langchain-agent-mcp-server-554655392699.us-central1.run.app
Project: slashmcp
Region: us-central1
Status: ✅ Live and operational

📡 API Endpoints

MCP Endpoints

Get Manifest

GET /mcp/manifest

Returns the MCP manifest declaring available tools.

Response:

{
  "name": "langchain-agent-mcp-server",
  "version": "1.0.0",
  "tools": [
    {
      "name": "agent_executor",
      "description": "Execute a complex, multi-step reasoning task...",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "The user's query or task"
          },
          "system_instruction": {
            "type": "string",
            "description": "Optional system-level instructions to customize agent behavior"
          }
        },
        "required": ["query"]
      }
    }
  ]
}

Invoke Tool

POST /mcp/invoke
Content-Type: application/json

{
  "tool": "agent_executor",
  "arguments": {
    "query": "What is the capital of France?",
    "task_id": "optional-workflow-id"
  }
}

With System Instruction (Optional):

POST /mcp/invoke
Content-Type: application/json

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Analyze Tesla stock",
    "system_instruction": "You are a financial analyst. Provide detailed analysis with specific numbers."
  }
}

Response:

{
  "content": [
    {
      "type": "text",
      "text": "The capital of France is Paris."
    }
  ],
  "isError": false
}

System Instructions

The agent_executor tool supports an optional system_instruction parameter that allows you to customize the agent's behavior on a per-invocation basis.

Usage:

Basic Query (uses default prompt):

{
  "tool": "agent_executor",
  "arguments": {
    "query": "What is the weather today?"
  }
}

Query with Custom Instruction:

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Explain quantum computing",
    "system_instruction": "You are a physics professor. Explain concepts clearly and use examples."
  }
}

Personality Customization:

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Tell me about space",
    "system_instruction": "You are a pirate explaining complex topics. Use pirate terminology!"
  }
}

Notes:

If system_instruction is omitted, the agent uses its default prompt
Empty or whitespace-only instructions are ignored (default prompt is used)
Each invocation with a custom instruction creates a new agent instance

Playwright Sandbox Endpoints

Generate Accessibility Snapshot

POST /api/playwright/snapshot
Content-Type: application/json

{
  "url": "wikipedia.org",
  "use_cache": true
}

Response:

{
  "snapshot": "[body]\n  Name: Wikipedia\n  [main]\n    Name: Main content...",
  "url": "https://wikipedia.org",
  "cached": false,
  "token_count": 3307
}

Features:

Generates structured accessibility snapshots of any website
Shows how AI "views" websites through structured data
Caching support for popular sites
Token count estimation
Windows-compatible (uses ProactorEventLoop)

Test Prompt Against Snapshot

POST /api/playwright/test-prompt
Content-Type: application/json

{
  "snapshot": "[body]\n  [button]\n    Name: Login",
  "prompt": "Find the login button"
}

Response:

{
  "matches": [
    {
      "line": 2,
      "content": "[button] Name: Login",
      "context": "..."
    }
  ],
  "prompt": "Find the login button",
  "total_matches": 1
}

Playwright Sandbox UI: Visit http://localhost:8080/sandbox to use the interactive preview feature:

Enter any URL to generate a snapshot
View live website side-by-side with AI accessibility snapshot
Test prompts to find elements in the snapshot
See token savings compared to full HTML/screenshots

Other Endpoints

GET / - Server information
GET /health - Health check
GET /api/tasks - Safe task summaries (optional monitoring)
GET /api/tasks/{task_id} - Safe task summary (optional monitoring)
GET /docs - Interactive API documentation (Swagger UI)

🔧 Configuration

Environment Variables

Variable	Description	Default	Required
`OPENAI_API_KEY`	OpenAI API key	-	✅ Yes
`OPENAI_MODEL`	OpenAI model to use	`gpt-4o-mini`	No
`PORT`	Server port	`8000`	No
`API_KEY`	Optional API key for authentication	-	No
`MAX_ITERATIONS`	Maximum agent iterations	`10`	No
`DEFAULT_SYSTEM_INSTRUCTION`	Default system prompt (Glazyr)	-	No
`VERBOSE`	Enable verbose logging	`false`	No
`POLICY_ENFORCEMENT`	Enforce /mcp/invoke policy gates	`false`	No
`MAX_QUERY_CHARS`	Max allowed query size	`5000000`	No
`ALLOWLISTED_DOMAINS`	Comma-separated domain allowlist (query URLs)	-	No
`REDIS_URL`	Enable Redis state store + task monitoring	-	No
`TASK_TTL_SECONDS`	Task summary TTL	`86400`	No
`RECENT_TASKS_MAX`	Recent task index size	`200`	No

📚 Documentation

📖 Full Documentation Site - Complete documentation with examples (GitHub Pages)

Quick Links:

Getting Started - Set up and run locally
Examples - Code examples including "Build a RAG agent in 10 lines"
Deployment Guide - Deploy to Google Cloud Run
API Reference - Complete API documentation
Troubleshooting - Common issues and solutions

Build Docs Locally:

# Windows
.\build-docs.ps1 serve

# Linux/Mac
./build-docs.sh serve

Additional Guides:

README_BACKEND.md - Complete technical documentation
PLAYWRIGHT_SANDBOX_SETUP.md - Playwright Sandbox setup and usage
BUG_REPORT_PLAYWRIGHT_NOTIMPLEMENTEDERROR.md - Windows compatibility fix documentation
DEPLOY_CLOUD_RUN_WINDOWS.md - Windows deployment guide
INSTALL_PREREQUISITES.md - Prerequisites installation
SLASHMCP_INTEGRATION.md - SlashMCP integration guide
docs/glazyr-integration.md - Glazyr integration notes (screenshots → MCP invoke)

🧪 Testing

# Test health endpoint
Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/health"

# Test agent invocation
$body = @{
    tool = "agent_executor"
    arguments = @{
        query = "What is 2+2?"
    }
} | ConvertTo-Json

Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/mcp/invoke" `
    -Method POST `
    -ContentType "application/json" `
    -Body $body

# Test with system instruction
$bodyWithInstruction = @{
    tool = "agent_executor"
    arguments = @{
        query = "What is 2+2?"
        system_instruction = "You are a math teacher. Explain your reasoning step by step."
    }
} | ConvertTo-Json

Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/mcp/invoke" `
    -Method POST `
    -ContentType "application/json" `
    -Body $bodyWithInstruction

🎭 Playwright Sandbox Feature

The Playwright Sandbox is an interactive preview feature that demonstrates how AI agents "view" websites through structured accessibility data. This feature is particularly useful for understanding the value of structured snapshots compared to full HTML or screenshots.

Features

Dual-View Interface: See the live website alongside its structured accessibility snapshot
Token Efficiency: Compare token counts - snapshots are typically 90%+ smaller than full HTML
Interactive Testing: Test prompts to find elements in the snapshot
Caching: Popular sites are cached for faster demo results
Windows Compatible: Fixed NotImplementedError on Windows using ProactorEventLoop

Quick Start

Install Playwright:

py -m pip install playwright
py -m playwright install chromium

Start Backend:
```
py run_server.py
```

Start Frontend:

npm install  # First time only
npm run dev

Visit Sandbox: Open http://localhost:8080/sandbox and try URLs like:
- wikipedia.org
- github.com
- google.com

How It Works

Enter a URL - The system navigates to the website using Playwright
Generate Snapshot - Extracts structured accessibility information (roles, names, descriptions)
View Comparison - See the live site vs. the AI's structured view
Test Prompts - Try asking the AI to find specific elements

Technical Details

Backend: FastAPI endpoint with Playwright integration
Frontend: React + Vite with TanStack Query
Event Loop: Uses ProactorEventLoop on Windows for subprocess support
Stealth Mode: Anti-bot detection measures for better compatibility
Error Handling: Graceful handling of sites that block automated access

See PLAYWRIGHT_SANDBOX_SETUP.md for detailed setup instructions.

🏗️ Project Structure

.
├── src/
│   ├── main.py              # FastAPI application with MCP endpoints
│   ├── agent.py             # LangChain agent definition and tools
│   ├── pages/
│   │   └── Sandbox.tsx      # Playwright Sandbox UI component
│   ├── mcp_manifest.json    # MCP manifest configuration
│   └── start.sh             # Cloud Run startup script
├── tests/
│   └── test_mcp_endpoints.py # Test suite
├── Dockerfile               # Container configuration
├── requirements.txt         # Python dependencies (includes playwright)
├── deploy-cloud-run.ps1     # Windows deployment script
├── deploy-cloud-run.sh      # Linux/Mac deployment script
└── cloudbuild.yaml          # Cloud Build configuration

🚀 Deployment Options

Google Cloud Run (Recommended)

Scalable - Auto-scales based on traffic
Serverless - Pay only for what you use
Managed - No infrastructure to manage
Fast - Low latency with global CDN

See DEPLOY_CLOUD_RUN_WINDOWS.md for detailed instructions.

Docker (Local/Other Platforms)

docker build -t langchain-agent-mcp-server .
docker run -p 8000:8000 -e OPENAI_API_KEY=your-key langchain-agent-mcp-server

📊 Performance

P95 Latency: < 5 seconds for standard 3-step ReAct chains
Scalability: Horizontal scaling on Cloud Run
Uptime: 99.9% target (Cloud Run SLA)
Throughput: Handles concurrent requests efficiently

🔒 Security

API key authentication (optional)
Environment variable management
Secret Manager integration (Cloud Run)
HTTPS by default (Cloud Run)
CORS configuration

🤝 Contributing

We welcome contributions! Please see our contributing guidelines.

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

📜 License

This project is licensed under the MIT License.

🔗 Links

GitHub Repository: https://github.com/mcpmessenger/LangchainMCP
Live Service: https://langchain-agent-mcp-server-554655392699.us-central1.run.app
API Documentation: https://langchain-agent-mcp-server-554655392699.us-central1.run.app/docs
Model Context Protocol: https://modelcontextprotocol.io/

🙏 Acknowledgments

Built with LangChain
Deployed on Google Cloud Run
Uses FastAPI for the web framework

Status: ✅ Production-ready and deployed on Google Cloud Run

LangChain Agent MCP Server

A production-ready MCP server exposing LangChain agent capabilities through the Model Context Protocol, deployed on Google Cloud Run.

🚀 Overview

Live Service: https://langchain-agent-mcp-server-554655392699.us-central1.run.app

✨ Features

✅ MCP Compliance - Full Model Context Protocol support
✅ LangChain Agent - Multi-step reasoning with ReAct pattern
✅ Playwright Sandbox - Interactive preview of accessibility snapshots (NEW!)
✅ Google Cloud Run - Scalable, serverless deployment
✅ Tool Support - Extensible framework for custom tools
✅ Production Ready - Error handling, logging, and monitoring
✅ Docker Support - Containerized for easy deployment

🏗️ Architecture

Component	Technology	Purpose
Backend Framework	FastAPI	High-performance, asynchronous web server
Agent Framework	LangChain	Multi-step reasoning and tool execution
Deployment	Google Cloud Run	Serverless, auto-scaling hosting
Containerization	Docker	Consistent deployment environment
Protocol	Model Context Protocol (MCP)	Standardized tool and context sharing

🛠️ Quick Start

Prerequisites

Python 3.11+
OpenAI API key
Google Cloud account (for Cloud Run deployment)
Docker (optional, for local testing)

Local Development

Clone the repository:

git clone https://github.com/mcpmessenger/LangchainMCP.git
cd LangchainMCP

Install dependencies:

# Windows
py -m pip install -r requirements.txt

# Linux/Mac
pip install -r requirements.txt

Set up environment variables: Create a .env file:

OPENAI_API_KEY=your-openai-api-key-here
OPENAI_MODEL=gpt-4o-mini
PORT=8000

Run the server:

# Windows
py run_server.py

# Linux/Mac
python run_server.py

Test the endpoints:
- Health: http://localhost:8000/health
- Manifest: http://localhost:8000/mcp/manifest
- API Docs: http://localhost:8000/docs
- Playwright Sandbox: http://localhost:8080/sandbox (after starting frontend)
Start the frontend (optional):
```
# Install frontend dependencies (first time only)
npm install

# Start frontend dev server
npm run dev
```
Then visit http://localhost:8080/sandbox to use the Playwright Sandbox preview feature.

☁️ Google Cloud Run Deployment

The server is designed for deployment on Google Cloud Run. See our comprehensive deployment guides:

DEPLOY_CLOUD_RUN_WINDOWS.md - Windows deployment guide
DEPLOY_CLOUD_RUN.md - General deployment guide
QUICK_DEPLOY.md - Quick reference

Quick Deploy

# Windows PowerShell
.\deploy-cloud-run.ps1 -ProjectId "your-project-id" -Region "us-central1"

# Linux/Mac
./deploy-cloud-run.sh your-project-id us-central1

Current Deployment

Service URL: https://langchain-agent-mcp-server-554655392699.us-central1.run.app
Project: slashmcp
Region: us-central1
Status: ✅ Live and operational

📡 API Endpoints

MCP Endpoints

Get Manifest

GET /mcp/manifest

Returns the MCP manifest declaring available tools.

Response:

{
  "name": "langchain-agent-mcp-server",
  "version": "1.0.0",
  "tools": [
    {
      "name": "agent_executor",
      "description": "Execute a complex, multi-step reasoning task...",
      "inputSchema": {
        "type": "object",
        "properties": {
          "query": {
            "type": "string",
            "description": "The user's query or task"
          },
          "system_instruction": {
            "type": "string",
            "description": "Optional system-level instructions to customize agent behavior"
          }
        },
        "required": ["query"]
      }
    }
  ]
}

Invoke Tool

POST /mcp/invoke
Content-Type: application/json

{
  "tool": "agent_executor",
  "arguments": {
    "query": "What is the capital of France?",
    "task_id": "optional-workflow-id"
  }
}

With System Instruction (Optional):

POST /mcp/invoke
Content-Type: application/json

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Analyze Tesla stock",
    "system_instruction": "You are a financial analyst. Provide detailed analysis with specific numbers."
  }
}

Response:

{
  "content": [
    {
      "type": "text",
      "text": "The capital of France is Paris."
    }
  ],
  "isError": false
}

System Instructions

The agent_executor tool supports an optional system_instruction parameter that allows you to customize the agent's behavior on a per-invocation basis.

Usage:

Basic Query (uses default prompt):

{
  "tool": "agent_executor",
  "arguments": {
    "query": "What is the weather today?"
  }
}

Query with Custom Instruction:

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Explain quantum computing",
    "system_instruction": "You are a physics professor. Explain concepts clearly and use examples."
  }
}

Personality Customization:

{
  "tool": "agent_executor",
  "arguments": {
    "query": "Tell me about space",
    "system_instruction": "You are a pirate explaining complex topics. Use pirate terminology!"
  }
}

Notes:

If system_instruction is omitted, the agent uses its default prompt
Empty or whitespace-only instructions are ignored (default prompt is used)
Each invocation with a custom instruction creates a new agent instance

Playwright Sandbox Endpoints

Generate Accessibility Snapshot

POST /api/playwright/snapshot
Content-Type: application/json

{
  "url": "wikipedia.org",
  "use_cache": true
}

Response:

{
  "snapshot": "[body]\n  Name: Wikipedia\n  [main]\n    Name: Main content...",
  "url": "https://wikipedia.org",
  "cached": false,
  "token_count": 3307
}

Features:

Generates structured accessibility snapshots of any website
Shows how AI "views" websites through structured data
Caching support for popular sites
Token count estimation
Windows-compatible (uses ProactorEventLoop)

Test Prompt Against Snapshot

POST /api/playwright/test-prompt
Content-Type: application/json

{
  "snapshot": "[body]\n  [button]\n    Name: Login",
  "prompt": "Find the login button"
}

Response:

{
  "matches": [
    {
      "line": 2,
      "content": "[button] Name: Login",
      "context": "..."
    }
  ],
  "prompt": "Find the login button",
  "total_matches": 1
}

Playwright Sandbox UI: Visit http://localhost:8080/sandbox to use the interactive preview feature:

Enter any URL to generate a snapshot
View live website side-by-side with AI accessibility snapshot
Test prompts to find elements in the snapshot
See token savings compared to full HTML/screenshots

Other Endpoints

GET / - Server information
GET /health - Health check
GET /api/tasks - Safe task summaries (optional monitoring)
GET /api/tasks/{task_id} - Safe task summary (optional monitoring)
GET /docs - Interactive API documentation (Swagger UI)

🔧 Configuration

Environment Variables

Variable	Description	Default	Required
`OPENAI_API_KEY`	OpenAI API key	-	✅ Yes
`OPENAI_MODEL`	OpenAI model to use	`gpt-4o-mini`	No
`PORT`	Server port	`8000`	No
`API_KEY`	Optional API key for authentication	-	No
`MAX_ITERATIONS`	Maximum agent iterations	`10`	No
`DEFAULT_SYSTEM_INSTRUCTION`	Default system prompt (Glazyr)	-	No
`VERBOSE`	Enable verbose logging	`false`	No
`POLICY_ENFORCEMENT`	Enforce /mcp/invoke policy gates	`false`	No
`MAX_QUERY_CHARS`	Max allowed query size	`5000000`	No
`ALLOWLISTED_DOMAINS`	Comma-separated domain allowlist (query URLs)	-	No
`REDIS_URL`	Enable Redis state store + task monitoring	-	No
`TASK_TTL_SECONDS`	Task summary TTL	`86400`	No
`RECENT_TASKS_MAX`	Recent task index size	`200`	No

📚 Documentation

📖 Full Documentation Site - Complete documentation with examples (GitHub Pages)

Quick Links:

Getting Started - Set up and run locally
Examples - Code examples including "Build a RAG agent in 10 lines"
Deployment Guide - Deploy to Google Cloud Run
API Reference - Complete API documentation
Troubleshooting - Common issues and solutions

Build Docs Locally:

# Windows
.\build-docs.ps1 serve

# Linux/Mac
./build-docs.sh serve

Additional Guides:

README_BACKEND.md - Complete technical documentation
PLAYWRIGHT_SANDBOX_SETUP.md - Playwright Sandbox setup and usage
BUG_REPORT_PLAYWRIGHT_NOTIMPLEMENTEDERROR.md - Windows compatibility fix documentation
DEPLOY_CLOUD_RUN_WINDOWS.md - Windows deployment guide
INSTALL_PREREQUISITES.md - Prerequisites installation
SLASHMCP_INTEGRATION.md - SlashMCP integration guide
docs/glazyr-integration.md - Glazyr integration notes (screenshots → MCP invoke)

🧪 Testing

# Test health endpoint
Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/health"

# Test agent invocation
$body = @{
    tool = "agent_executor"
    arguments = @{
        query = "What is 2+2?"
    }
} | ConvertTo-Json

Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/mcp/invoke" `
    -Method POST `
    -ContentType "application/json" `
    -Body $body

# Test with system instruction
$bodyWithInstruction = @{
    tool = "agent_executor"
    arguments = @{
        query = "What is 2+2?"
        system_instruction = "You are a math teacher. Explain your reasoning step by step."
    }
} | ConvertTo-Json

Invoke-WebRequest -Uri "https://langchain-agent-mcp-server-554655392699.us-central1.run.app/mcp/invoke" `
    -Method POST `
    -ContentType "application/json" `
    -Body $bodyWithInstruction

🎭 Playwright Sandbox Feature

Features

Dual-View Interface: See the live website alongside its structured accessibility snapshot
Token Efficiency: Compare token counts - snapshots are typically 90%+ smaller than full HTML
Interactive Testing: Test prompts to find elements in the snapshot
Caching: Popular sites are cached for faster demo results
Windows Compatible: Fixed NotImplementedError on Windows using ProactorEventLoop

Quick Start

Install Playwright:

py -m pip install playwright
py -m playwright install chromium

Start Backend:
```
py run_server.py
```

Start Frontend:

npm install  # First time only
npm run dev

Visit Sandbox: Open http://localhost:8080/sandbox and try URLs like:
- wikipedia.org
- github.com
- google.com

How It Works

Enter a URL - The system navigates to the website using Playwright
Generate Snapshot - Extracts structured accessibility information (roles, names, descriptions)
View Comparison - See the live site vs. the AI's structured view
Test Prompts - Try asking the AI to find specific elements

Technical Details

Backend: FastAPI endpoint with Playwright integration
Frontend: React + Vite with TanStack Query
Event Loop: Uses ProactorEventLoop on Windows for subprocess support
Stealth Mode: Anti-bot detection measures for better compatibility
Error Handling: Graceful handling of sites that block automated access

See PLAYWRIGHT_SANDBOX_SETUP.md for detailed setup instructions.

🏗️ Project Structure

.
├── src/
│   ├── main.py              # FastAPI application with MCP endpoints
│   ├── agent.py             # LangChain agent definition and tools
│   ├── pages/
│   │   └── Sandbox.tsx      # Playwright Sandbox UI component
│   ├── mcp_manifest.json    # MCP manifest configuration
│   └── start.sh             # Cloud Run startup script
├── tests/
│   └── test_mcp_endpoints.py # Test suite
├── Dockerfile               # Container configuration
├── requirements.txt         # Python dependencies (includes playwright)
├── deploy-cloud-run.ps1     # Windows deployment script
├── deploy-cloud-run.sh      # Linux/Mac deployment script
└── cloudbuild.yaml          # Cloud Build configuration

🚀 Deployment Options

Google Cloud Run (Recommended)

Scalable - Auto-scales based on traffic
Serverless - Pay only for what you use
Managed - No infrastructure to manage
Fast - Low latency with global CDN

See DEPLOY_CLOUD_RUN_WINDOWS.md for detailed instructions.

Docker (Local/Other Platforms)

docker build -t langchain-agent-mcp-server .
docker run -p 8000:8000 -e OPENAI_API_KEY=your-key langchain-agent-mcp-server

📊 Performance

P95 Latency: < 5 seconds for standard 3-step ReAct chains
Scalability: Horizontal scaling on Cloud Run
Uptime: 99.9% target (Cloud Run SLA)
Throughput: Handles concurrent requests efficiently

🔒 Security

API key authentication (optional)
Environment variable management
Secret Manager integration (Cloud Run)
HTTPS by default (Cloud Run)
CORS configuration

🤝 Contributing

We welcome contributions! Please see our contributing guidelines.

Fork the repository
Create a feature branch
Make your changes
Submit a pull request

📜 License

This project is licensed under the MIT License.

🔗 Links

GitHub Repository: https://github.com/mcpmessenger/LangchainMCP
Live Service: https://langchain-agent-mcp-server-554655392699.us-central1.run.app
API Documentation: https://langchain-agent-mcp-server-554655392699.us-central1.run.app/docs
Model Context Protocol: https://modelcontextprotocol.io/

🙏 Acknowledgments

Built with LangChain
Deployed on Google Cloud Run
Uses FastAPI for the web framework

Status: ✅ Production-ready and deployed on Google Cloud Run