CxBlueprint

Programmatic Amazon Connect contact flow generation using Python. Lets AI models and developers build contact flows from plain English instead of hand-writing JSON.

47 of 56 AWS Connect block types supported (84%) — covers all common IVR, routing, A/B testing, queue transfer, customer profiles, case management, and Voice ID patterns. Includes an MCP server for AI-assisted flow generation with Claude Desktop, Cursor, and VS Code.

Simple Example

from cxblueprint import Flow

flow = Flow.build("Burger Order")

welcome = flow.play_prompt("Welcome to Burger Palace!")
menu = flow.get_input("Press 1 for Classic Burger or 2 for Veggie Burger", timeout=10)
welcome.then(menu)

classic = flow.play_prompt("You selected Classic Burger. Your order is confirmed!")
veggie = flow.play_prompt("You selected Veggie Burger. Your order is confirmed!")
error_msg = flow.play_prompt("Invalid selection. Goodbye.")

disconnect = flow.disconnect()

menu.when("1", classic) \
    .when("2", veggie) \
    .otherwise(error_msg) \
    .on_error("InputTimeLimitExceeded", error_msg) \
    .on_error("NoMatchingCondition", error_msg) \
    .on_error("NoMatchingError", error_msg)

classic.then(disconnect)
veggie.then(disconnect)
error_msg.then(disconnect)

flow.compile_to_file("burger_order.json")

Terraform Template Example

Use placeholders for dynamic resource ARNs:

from cxblueprint import Flow

flow = Flow.build("Counter Flow")

welcome = flow.play_prompt("Thank you for calling!")
invoke_counter = flow.invoke_lambda(
    function_arn="${COUNTER_LAMBDA_ARN}",  # Resolved by Terraform
    timeout_seconds="8"
)
welcome.then(invoke_counter)

say_count = flow.play_prompt("You are caller number $.External.count")
invoke_counter.then(say_count)

disconnect = flow.disconnect()
say_count.then(disconnect)
invoke_counter.on_error("NoMatchingError", disconnect)

flow.compile_to_file("counter_flow.json")

Generated Flow Examples

Here's what the generated flows look like in the Amazon Connect console:

Installation

pip install cxblueprint

# With MCP server for AI integration
pip install cxblueprint[mcp]

MCP Server

CxBlueprint includes an MCP server that lets AI tools (Claude Desktop, Cursor, VS Code) build contact flows conversationally.

Configure Claude Desktop (~/Library/Application Support/Claude/claude_desktop_config.json):

{
  "mcpServers": {
    "cxblueprint": {
      "command": "cxblueprint-mcp"
    }
  }
}

Then ask Claude: "Build me an IVR with a welcome message and 3 menu options for sales, support, and billing"

The AI reads the bundled documentation automatically and uses the compile_flow tool to generate valid Amazon Connect JSON.

Block Coverage

47 / 56 block types implemented (84%)

See each category's README for the full per-block breakdown.

Features

• Fluent Python API for building flows
• MCP server for AI-assisted flow generation
• Canvas layout positioning for AWS Connect visual editor
• Automatic UUID generation for blocks
• Conditional branching and error handling
• AWS Lambda and Lex V2 bot integration
• Template placeholder support for Terraform/IaC
• Decompile existing flows back to Python

Project Structure

src/cxblueprint/
  flow_builder.py       # Main builder API
  flow_analyzer.py      # Flow validation
  mcp_server.py         # MCP server for AI integration
  blocks/               # All Connect block types
examples/               # Sample flows
docs/                   # API reference & AI instructions

Documentation

• API Reference
• Terraform Example

Requirements

• Python 3.11+
• AWS credentials (for deployment)
• Terraform (optional, for infrastructure)