Create AI Assistant with Python and Flask

Overview

Build a production-ready Flask endpoint that creates AI assistants using the Telnyx AI Assistants API. This tutorial demonstrates the client-based initialization pattern, proper error handling for AI services, and secure credential management via environment variables.

Prerequisites

• Python 3.8 or higher.
• A Telnyx account with an active API key from the Telnyx Portal.
• pip (Python package manager).
• Basic understanding of REST APIs and JSON.

Step 1: Setup

Install the required dependencies:

pip install telnyx flask python-dotenv

Create a project directory and navigate into it:

mkdir telnyx-ai-assistant-creator
cd telnyx-ai-assistant-creator

Step 2: Configuration

Create a .env file in your project root to store credentials securely:

TELNYX_API_KEY=YOUR_API_KEY_HERE

Replace YOUR_API_KEY_HERE with your actual API key from the Telnyx Portal.

Step 3: Implementation

Create app.py and initialize the Telnyx client using the new pattern. Define a helper function to handle assistant creation with proper validation:

import os
import telnyx
from dotenv import load_dotenv

load_dotenv()

# Initialize client with the new SDK pattern
client = telnyx.Telnyx(api_key=os.getenv("TELNYX_API_KEY"))


def create_ai_assistant(name: str, instructions: str, model: str = "meta-llama/Meta-Llama-3.1-70B-Instruct", enabled_features: list = None) -> dict:
    """Create AI assistant via Telnyx and return JSON-serializable response data."""
    if not name or not instructions:
        raise ValueError("Name and instructions are required")

    # Default to messaging if no features specified
    if enabled_features is None:
        enabled_features = ["messaging"]

    # Validate enabled features
    valid_features = ["messaging", "telephony"]
    for feature in enabled_features:
        if feature not in valid_features:
            raise ValueError(f"Invalid feature: {feature}. Must be one of: {valid_features}")

    # Use client.ai_assistants.create() — NOT client.ai_assistants.create()
    response = client.ai_assistants.create(
        name=name,
        instructions=instructions,
        model=model,
        enabled_features=enabled_features,
    )

    # Extract serializable data — SDK objects are NOT JSON-serializable
    return {
        "id": response.data.id,
        "name": response.data.name,
        "model": response.data.model,
        "instructions": response.data.instructions,
        "enabled_features": response.data.enabled_features,
        "created_at": response.data.created_at,
    }

Step 4: Testing

Add the Flask route with comprehensive error handling for production resilience:

from flask import Flask, jsonify, request

app = Flask(__name__)


@app.route("/ai/assistants", methods=["POST"])
def create_assistant_endpoint():
    """HTTP endpoint to create AI assistant."""
    data = request.get_json()

    if not data:
        return jsonify({"error": "Request body required"}), 400

    name = data.get("name")
    instructions = data.get("instructions")
    model = data.get("model", "meta-llama/Meta-Llama-3.1-70B-Instruct")
    enabled_features = data.get("enabled_features", ["messaging"])

    if not name or not instructions:
        return jsonify({"error": "Missing required fields: 'name' and 'instructions'"}), 400

    try:
        result = create_ai_assistant(name, instructions, model, enabled_features)
        return jsonify(result), 201

    except telnyx.AuthenticationError:
        return jsonify({"error": "Invalid API key"}), 401
    except telnyx.RateLimitError:
        return jsonify({"error": "Rate limit exceeded. Please slow down."}), 429
    except telnyx.APIStatusError as e:
        return jsonify({"error": str(e), "status_code": e.status_code}), e.status_code
    except telnyx.APIConnectionError:
        return jsonify({"error": "Network error connecting to Telnyx"}), 503
    except ValueError as e:
        return jsonify({"error": str(e)}), 400


if __name__ == "__main__":
    app.run(debug=True, port=5000)

Start the server:

python app.py

Test the endpoint using curl:

curl -X POST http://localhost:5000/ai/assistants \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Customer Support Bot",
    "instructions": "You are a helpful customer support assistant. Be polite, professional, and provide accurate information about our products and services.",
    "enabled_features": ["messaging", "telephony"]
  }'

Expected response:

{
  "id": "01234567-89ab-cdef-0123-456789abcdef",
  "name": "Customer Support Bot",
  "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
  "instructions": "You are a helpful customer support assistant. Be polite, professional, and provide accurate information about our products and services.",
  "enabled_features": ["messaging", "telephony"],
  "created_at": "2026-04-21T10:30:00.000Z"
}

Complete Code

#!/usr/bin/env python3
"""Production-ready Flask endpoint for creating AI assistants via Telnyx."""

import os
import telnyx
from dotenv import load_dotenv
from flask import Flask, jsonify, request

load_dotenv()

app = Flask(__name__)

# Initialize client with the new SDK pattern
client = telnyx.Telnyx(api_key=os.getenv("TELNYX_API_KEY"))


def create_ai_assistant(name: str, instructions: str, model: str = "meta-llama/Meta-Llama-3.1-70B-Instruct", enabled_features: list = None) -> dict:
    """Create AI assistant via Telnyx and return JSON-serializable response data."""
    if not name or not instructions:
        raise ValueError("Name and instructions are required")

    # Default to messaging if no features specified
    if enabled_features is None:
        enabled_features = ["messaging"]

    # Validate enabled features
    valid_features = ["messaging", "telephony"]
    for feature in enabled_features:
        if feature not in valid_features:
            raise ValueError(f"Invalid feature: {feature}. Must be one of: {valid_features}")

    # Use client.ai_assistants.create() — NOT client.ai_assistants.create()
    response = client.ai_assistants.create(
        name=name,
        instructions=instructions,
        model=model,
        enabled_features=enabled_features,
    )

    # Extract serializable data — SDK objects are NOT JSON-serializable
    return {
        "id": response.data.id,
        "name": response.data.name,
        "model": response.data.model,
        "instructions": response.data.instructions,
        "enabled_features": response.data.enabled_features,
        "created_at": response.data.created_at,
    }


@app.route("/ai/assistants", methods=["POST"])
def create_assistant_endpoint():
    """HTTP endpoint to create AI assistant."""
    data = request.get_json()

    if not data:
        return jsonify({"error": "Request body required"}), 400

    name = data.get("name")
    instructions = data.get("instructions")
    model = data.get("model", "meta-llama/Meta-Llama-3.1-70B-Instruct")
    enabled_features = data.get("enabled_features", ["messaging"])

    if not name or not instructions:
        return jsonify({"error": "Missing required fields: 'name' and 'instructions'"}), 400

    try:
        result = create_ai_assistant(name, instructions, model, enabled_features)
        return jsonify(result), 201

    except telnyx.AuthenticationError:
        return jsonify({"error": "Invalid API key"}), 401
    except telnyx.RateLimitError:
        return jsonify({"error": "Rate limit exceeded. Please slow down."}), 429
    except telnyx.APIStatusError as e:
        return jsonify({"error": str(e), "status_code": e.status_code}), e.status_code
    except telnyx.APIConnectionError:
        return jsonify({"error": "Network error connecting to Telnyx"}), 503
    except ValueError as e:
        return jsonify({"error": str(e)}), 400


if __name__ == "__main__":
    app.run(debug=True, port=5000)

Troubleshooting

Next Steps

• List AI Assistants.
• Update an AI Assistant.
• Chat with an AI Assistant.