Building smart multilingual voice assistants with Vapi: The API-First strategy

Voice AI is no longer a futuristic dream; it’s a present-day reality transforming customer interactions.

Platforms like Vapi are at forefront, simplifying the creation of sophisticated voice assistants. But as your needs grow, especially towards multilingual support, how you build and manage these assistants becomes crucial.

Today, we’ll explore a robust strategy for creating multilingual voice assistants on Vapi, focusing on why an API-first approach is often the superior choice for developers.

Two Paths to Vapi Assistants: Dashboard vs. API

1. The Vapi dashboard: A user-friendly web interface. It’s great for:

• Quickly spinning up your first assistants.
• Visualizing configurations.
• Testing and iterating in the early stages.

2. The Vapi REST API: A programmatic interface that allows you to manage assistants using HTTP requests. This is the path I advocate for, especially for complex or scalable projects.

Why I Champion the API-First approach:

While the dashboard is convenient, the API offers unparalleled advantages for serious development:

• Version Control (Git): Treat your assistant configurations as code. Track changes, revert to previous versions, and collaborate effectively.
• Reproducibility & Automation: Deploy identical assistant setups across different environments (dev, staging, prod) or for multiple clients with scripts. Integrate into CI/CD pipelines.
• Scalability: Need to manage dozens or hundreds of slightly different assistants? The API makes this manageable, whereas a dashboard would become cumbersome.
• Dynamic Configuration: Build assistants whose configurations are generated from databases, external files, or other systems.
• Custom Tooling: Develop your own internal tools or scripts around assistant management.

For a multilingual system, where you’ll likely have multiple specialized agents, these benefits become even more pronounced.

The Strategy: A “Squad” of specialized multilingual assistants

Instead of trying to build a single, monolithic assistant that attempts to handle multiple languages and complex logic, our strategy involves a team of specialized agents working together within a Vapi “Squad.”

Here’s the breakdown:

1. The language detector assistant:

• Role: A lightweight, focused assistant.
• Task: Its sole purpose is to greet the caller and determine their preferred language (e.g., “For English, say English. Pour le français, dites Français.”).
• It doesn’t handle any other business logic.

2. Language-Specific Assistants (e.g., English agent, French agent):

• Role: Fully functional assistants, each tailored to a specific language..
• Task: Handle all user interactions for their designated language (e.g., answer FAQs, book appointments for a dental clinic). Each has its own prompts, voice, and potentially language-specific tools.

3. The Vapi Squad:

• Role: The orchestrator. A Squad in Vapi groups multiple assistants and defines how calls can be routed or transferred between them.
• Flow: The incoming call is first routed to the Language Detector Assistant.
• Based on the user’s response, Vapi (using the Squad’s configuration) intelligently transfers the call to the appropriate Language-Specific Assistant (e.g., to the English Agent if the user chose English).

Visualizing the Flow:

This modular approach keeps each assistant’s logic simple and focused, making the overall system easier to build, test, and maintain.

A Glimpse: Creating an assistant via API

Let’s see a simplified Python snippet of how you might create a single assistant using Vapi’s API. This demonstrates the core concept before diving into a more complex system.

import requests
import os
import json

# Load your Vapi API Key (best practice: use environment variables)
# For this example, ensure VAPI_API_KEY is set in your environment.
VAPI_API_KEY = os.getenv("VAPI_API_KEY")
VAPI_BASE_URL = "https://api.vapi.ai"

if not VAPI_API_KEY:
    raise ValueError("Please set the VAPI_API_KEY environment variable.")

def create_simple_assistant(name: str, system_prompt: str, first_message: str):
    """
    Creates a very basic assistant using the Vapi API.
    """
    headers = {
        "Authorization": f"Bearer {VAPI_API_KEY}",
        "Content-Type": "application/json"
    }
    payload = {
        "name": name,
        "model": {
            "provider": "openai",  # Or your preferred provider
            "model": "gpt-3.5-turbo", # Or your preferred model
            "messages": [
                {
                    "role": "system",
                    "content": system_prompt
                }
            ]
        },
        "voice": { # Example: Vapi's built-in voice
            "provider": "vapi",
            "voiceId": "maya" # A default Vapi voice
        },
        "firstMessage": first_message,
        # Add other minimal required fields or desired configurations
        # "transcriber": {...},
        # "serverUrl": "...",
    }

    try:
        response = requests.post(f"{VAPI_BASE_URL}/assistant", headers=headers, json=payload)
        response.raise_for_status()  # Raises an HTTPError for bad responses (4XX or 5XX)
        
        assistant_data = response.json()
        print(f"Assistant '{name}' created successfully!")
        print(json.dumps(assistant_data, indent=2))
        return assistant_data
    except requests.exceptions.RequestException as e:
        print(f"Error creating assistant '{name}': {e}")
        if hasattr(e, 'response') and e.response is not None:
            try:
                print("Response content:", e.response.json())
            except json.JSONDecodeError:
                print("Response content (not JSON):", e.response.text)
        return None

if __name__ == "__main__":
    # Example usage:
    my_assistant = create_simple_assistant(
        name="MySimpleAPIAssistant",
        system_prompt="You are a helpful assistant.",
        first_message="Hello, how can I help you today?"
    )
    # You can then find this assistant in your Vapi dashboard!

This snippet uses the requests library in Python to send a POST request to Vapi’s /assistant endpoint. The payload defines the basic configuration for our new assistant.

I’ve put together a complete Python project demonstrating this exact strategy. It includes a more robust Vapi client, service layers for managing assistants and squads, and clear configuration files.

➡️ Explore the full source code on GitHub: https://github.com/bssoufo/vapi-multilingual-voice-assistant

Conclusion

Building sophisticated voice assistants, especially multilingual ones, benefits immensely from an API-first, code-driven approach. Vapi’s API, combined with a clear strategy like the specialized agent squad, empowers developers to create robust, scalable, and maintainable voice AI solutions.

By managing your assistants as code, you unlock new levels of control and efficiency.

Ready to dive deeper or need help building your custom voice AI solution?

Check out the GitHub project!

Happy building!