---
title: Build a WhatsApp AI Agent with Django and Zavu
description: Learn how to build an AI-powered WhatsApp agent using Django and Zavu's managed AI Gateway.
date: 2025-12-15
author: Victor Villalobos
locale: en
source: https://www.zavu.dev/en/blog/whatsapp-ai-agents-django
tags: AI Agents, WhatsApp, Django, Python
---

# Build a WhatsApp AI Agent with Django and Zavu

In this tutorial, you'll build a production-ready WhatsApp AI agent using Django and Zavu. The best part? You don't need to manage any external AI API keys - Zavu's AI Gateway gives you access to all top-tier AI models (GPT-4, Claude, Gemini, Mistral, and more) directly from your Zavu dashboard.

## What We're Building

A Django application that:
- Receives incoming WhatsApp messages via webhooks
- Verifies webhook signatures for security
- Uses Zavu's managed AI agents for intelligent responses
- Automatically maintains conversation context

## Prerequisites

- Python 3.9+
- A Zavu account with API credentials
- Basic Django knowledge

**No external AI API keys required!** Zavu's AI Gateway provides access to all major AI models through a single integration.

## Installation

Create a new Django project and install dependencies:

```bash
mkdir whatsapp-agent && cd whatsapp-agent
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

pip install django zavudev python-dotenv
django-admin startproject config .
python manage.py startapp agent
```

## Project Structure

```
whatsapp-agent/
├── config/
│   ├── settings.py
│   ├── urls.py
│   └── wsgi.py
├── agent/
│   ├── views.py
│   ├── urls.py
│   └── services.py
├── .env
└── manage.py
```

## Environment Configuration

Create a `.env` file in your project root:

```bash
ZAVUDEV_API_KEY=your_zavu_api_key
ZAVU_WEBHOOK_SECRET=your_webhook_secret
```

That's it! No OpenAI, Anthropic, or other AI provider keys needed.

Update `config/settings.py`:

```python
import os
from dotenv import load_dotenv

load_dotenv()

INSTALLED_APPS = [
    # ... default apps
    'agent',
]

ZAVUDEV_API_KEY = os.environ.get('ZAVUDEV_API_KEY')
ZAVU_WEBHOOK_SECRET = os.environ.get('ZAVU_WEBHOOK_SECRET')
```

## How Zavu AI Gateway Works

Zavu provides a unified AI Gateway that gives you access to all top-tier AI models without managing individual API keys:

- **GPT-4o, GPT-4o-mini** - OpenAI's latest models
- **Claude 3.5 Sonnet, Claude 3 Opus** - Anthropic's models
- **Gemini Pro** - Google's AI models
- **Mistral Large** - Mistral AI's models

You can configure which model to use directly in your Zavu dashboard, and optionally add your own API keys if you prefer to use your own credentials for billing purposes.

## Create a Managed AI Agent

The easiest way to add AI capabilities is through Zavu's managed agents. You have two options:

### Option 1: Via Zavu Dashboard

Create an agent for your sender directly in the **Zavu Dashboard**:

1. Go to **Senders** in your Zavu dashboard
2. Select the sender you want to add AI capabilities to
3. Click on **AI Agent** tab
4. Configure your agent:
   - **Name**: Give your agent a descriptive name (e.g., "Customer Support")
   - **Provider**: Select "Zavu" to use Zavu's AI Gateway (no external keys needed)
   - **Model**: Choose from available models (gpt-4o-mini, claude-3-5-sonnet, etc.)
   - **System Prompt**: Define your agent's personality and instructions
   - **Context Window**: Set how many previous messages to include for context
5. Enable the agent and save

With the `zavu` provider, all AI processing is handled by Zavu's managed gateway. Your agent will automatically respond to incoming WhatsApp messages without any additional code!

### Option 2: Via Python SDK

You can also create and configure AI agents programmatically using the `zavudev` SDK. Add these helper functions to `agent/services.py`:

```python
def create_ai_agent(sender_id: str, name: str, system_prompt: str, model: str = "gpt-4o-mini") -> dict:
    """Create an AI agent for a sender using Zavu's AI Gateway."""
    response = zavu_client.senders.agent.create(
        sender_id=sender_id,
        name=name,
        provider="zavu",  # Use Zavu's AI Gateway (no external API keys needed)
        model=model,
        system_prompt=system_prompt,
        context_window_messages=10,  # Include last 10 messages for context
        include_contact_metadata=True,  # Include contact info in context
        enabled=True
    )
    return response.agent


def get_agent_stats(sender_id: str) -> dict:
    """Get statistics for an AI agent."""
    response = zavu_client.senders.agent.stats(sender_id)
    return response


def list_agent_executions(sender_id: str, limit: int = 50) -> list:
    """List recent agent executions."""
    response = zavu_client.senders.agent.executions.list(
        sender_id=sender_id,
        limit=limit
    )
    return response.items
```

Now you can create an agent from Django code:

```python
# Example: Create an agent from a Django management command or view
from .services import create_ai_agent

agent = create_ai_agent(
    sender_id="sender_abc123",
    name="Customer Support Bot",
    model="gpt-4o-mini",
    system_prompt="""You are a helpful customer support assistant for Zavu.

Rules:
- Be friendly and concise
- Help with WhatsApp, SMS, and email messaging questions
- If unsure, offer to connect with a human agent
- Respond in the same language as the customer"""
)

print(f"Agent created: {agent['id']}")
```

**Note:** When using `provider="zavu"`, Zavu's AI Gateway handles all AI model access. No external API keys needed!

## Available AI Models

When creating an agent, you can choose from these models:

| Provider | Models |
|----------|--------|
| OpenAI | gpt-4o, gpt-4o-mini, gpt-4-turbo |
| Anthropic | claude-3-5-sonnet, claude-3-opus, claude-3-haiku |
| Google | gemini-pro, gemini-1.5-pro |
| Mistral | mistral-large, mistral-medium |

Simply change the model in your agent configuration through the dashboard.

## Initialize the Zavu Client

Create `agent/services.py`:

```python
import os
import hmac
import hashlib
from zavudev import Zavudev

zavu_client = Zavudev(api_key=os.environ.get('ZAVUDEV_API_KEY'))


def verify_webhook_signature(payload: bytes, signature: str) -> bool:
    """Verify the webhook signature from Zavu."""
    secret = os.environ.get('ZAVU_WEBHOOK_SECRET', '')

    if not signature or not secret:
        return False

    expected = hmac.new(
        secret.encode('utf-8'),
        payload,
        hashlib.sha256
    ).hexdigest()

    return hmac.compare_digest(f"sha256={expected}", signature)


def send_whatsapp_message(to: str, text: str) -> dict:
    """Send a WhatsApp message using Zavu."""
    response = zavu_client.messages.send(
        to=to,
        text=text,
        channel="whatsapp"
    )
    return response.message
```

## Create the Webhook View

Create `agent/views.py`:

```python
import json
import logging
from django.http import JsonResponse, HttpResponse
from django.views.decorators.csrf import csrf_exempt
from django.views.decorators.http import require_POST

from .services import verify_webhook_signature, send_whatsapp_message

logger = logging.getLogger(__name__)


@csrf_exempt
@require_POST
def webhook(request):
    """Handle incoming WhatsApp messages from Zavu."""
    signature = request.headers.get('X-Zavu-Signature', '')

    if not verify_webhook_signature(request.body, signature):
        logger.warning("Invalid webhook signature")
        return HttpResponse(status=401)

    try:
        payload = json.loads(request.body)
    except json.JSONDecodeError:
        return JsonResponse({"error": "Invalid JSON"}, status=400)

    event_type = payload.get('type')
    logger.info(f"Received webhook event: {event_type}")

    return JsonResponse({"status": "ok"})
```

With a managed AI agent configured, Zavu automatically handles the AI response. The webhook is primarily for logging or custom business logic.

## Configure URLs

Create `agent/urls.py`:

```python
from django.urls import path
from . import views

urlpatterns = [
    path('webhook/', views.webhook, name='webhook'),
]
```

Update `config/urls.py`:

```python
from django.contrib import admin
from django.urls import path, include

urlpatterns = [
    path('admin/', admin.site.urls),
    path('api/', include('agent.urls')),
]
```

## Deploy and Configure

1. Deploy your Django app to a server with HTTPS (required for webhooks)

2. Configure the webhook in your Zavu dashboard:
   - Go to **Senders** > Select your sender > **Webhook**
   - Set URL to: `https://yourdomain.com/api/webhook/`
   - Enable events: `message.inbound`
   - Copy the webhook secret to your `.env` file

3. Create an AI agent for your sender in the Zavu dashboard or via API

4. Run the server:

```bash
python manage.py runserver 0.0.0.0:8000
```

## Testing Locally with ngrok

For local development, use ngrok to expose your server:

```bash
ngrok http 8000
```

Copy the HTTPS URL and configure it in your Zavu webhook settings.

## Managed Agent Features

Zavu's AI agents come with powerful built-in features, all configurable through the dashboard:

### Knowledge Bases

Create RAG-powered responses by uploading documents to give your agent domain-specific knowledge.

#### Via Dashboard

1. Go to **Senders** > Select your sender > **AI Agent** > **Knowledge Bases**
2. Click **Create Knowledge Base**
3. Upload documents (PDF, TXT, MD) or paste content directly
4. The agent will automatically use this knowledge to answer questions

#### Via Python SDK

You can also manage knowledge bases programmatically. Add these functions to `agent/services.py`:

```python
def create_knowledge_base(sender_id: str, name: str, description: str = None) -> dict:
    """Create a new knowledge base for an agent."""
    response = zavu_client.senders.agent.knowledge_bases.create(
        sender_id=sender_id,
        name=name,
        description=description
    )
    return response.knowledge_base


def add_document_to_kb(sender_id: str, kb_id: str, title: str, content: str) -> dict:
    """Add a document to a knowledge base."""
    response = zavu_client.senders.agent.knowledge_bases.documents.create(
        sender_id=sender_id,
        kb_id=kb_id,
        title=title,
        content=content
    )
    return response.document


def list_kb_documents(sender_id: str, kb_id: str, limit: int = 50) -> list:
    """List documents in a knowledge base."""
    response = zavu_client.senders.agent.knowledge_bases.documents.list(
        sender_id=sender_id,
        kb_id=kb_id,
        limit=limit
    )
    return response.items


def delete_kb_document(sender_id: str, kb_id: str, doc_id: str) -> None:
    """Delete a document from a knowledge base."""
    zavu_client.senders.agent.knowledge_bases.documents.delete(
        sender_id=sender_id,
        kb_id=kb_id,
        doc_id=doc_id
    )
```

Example usage:

```python
from .services import create_knowledge_base, add_document_to_kb

# Create a knowledge base
kb = create_knowledge_base(
    sender_id="sender_abc123",
    name="Product FAQ",
    description="Frequently asked questions about our products"
)

# Add documents to the knowledge base
add_document_to_kb(
    sender_id="sender_abc123",
    kb_id=kb['id'],
    title="Return Policy",
    content="""Our return policy allows returns within 30 days of purchase.

Items must be:
- In original condition
- With receipt or proof of purchase
- Not used or damaged

To process a return, contact support@example.com"""
)

add_document_to_kb(
    sender_id="sender_abc123",
    kb_id=kb['id'],
    title="Shipping Information",
    content="""We offer free shipping on orders over $50.

Standard shipping: 5-7 business days
Express shipping: 2-3 business days
Overnight shipping: Next business day"""
)

print(f"Knowledge base created: {kb['id']}")
```

The agent will automatically use this knowledge to answer customer questions about returns and shipping.

### Custom Tools

Connect external APIs to extend your agent's capabilities by creating tools that the AI can call.

#### Via Dashboard

1. Go to **Senders** > Select your sender > **AI Agent** > **Tools**
2. Click **Create Tool**
3. Configure the tool:
   - **Name**: A descriptive name (e.g., "get_order_status")
   - **Description**: What the tool does (helps the AI decide when to use it)
   - **Webhook URL**: Your HTTPS endpoint that handles the tool call
   - **Parameters**: JSON Schema defining the expected parameters

#### Via Python SDK

You can also create tools programmatically. Add these functions to `agent/services.py`:

```python
def create_tool(
    sender_id: str,
    name: str,
    description: str,
    webhook_url: str,
    parameters: dict,
    webhook_secret: str = None
) -> dict:
    """Create a tool for an agent."""
    response = zavu_client.senders.agent.tools.create(
        sender_id=sender_id,
        name=name,
        description=description,
        webhook_url=webhook_url,
        webhook_secret=webhook_secret,  # Optional signature verification
        parameters=parameters
    )
    return response.tool


def list_tools(sender_id: str, enabled: bool = None) -> list:
    """List tools for an agent."""
    response = zavu_client.senders.agent.tools.list(
        sender_id=sender_id,
        enabled=enabled
    )
    return response.items


def test_tool(sender_id: str, tool_id: str, test_params: dict) -> bool:
    """Test a tool with sample parameters."""
    response = zavu_client.senders.agent.tools.test(
        sender_id=sender_id,
        tool_id=tool_id,
        test_params=test_params
    )
    return response.scheduled
```

Example usage:

```python
from .services import create_tool

# Create a tool to check order status
tool = create_tool(
    sender_id="sender_abc123",
    name="get_order_status",
    description="Get the current status of a customer order",
    webhook_url="https://yourdomain.com/api/tools/order-status",
    webhook_secret="your_tool_webhook_secret",
    parameters={
        "type": "object",
        "properties": {
            "order_id": {
                "type": "string",
                "description": "The order ID to look up"
            }
        },
        "required": ["order_id"]
    }
)

print(f"Tool created: {tool['id']}")
```

Then create an endpoint to handle tool invocations:

```python
# agent/views.py
@csrf_exempt
@require_POST
def tool_webhook(request, tool_id):
    """Handle tool calls from the AI agent."""
    import json
    import hmac
    import hashlib

    # Verify tool webhook signature
    signature = request.headers.get('X-Zavu-Tool-Signature', '')
    secret = os.environ.get('ZAVU_TOOL_WEBHOOK_SECRET', '')

    if signature and secret:
        expected = hmac.new(
            secret.encode('utf-8'),
            request.body,
            hashlib.sha256
        ).hexdigest()
        if not hmac.compare_digest(f"sha256={expected}", signature):
            return HttpResponse(status=401)

    try:
        payload = json.loads(request.body)
        order_id = payload.get('order_id')

        # Call your internal API/service
        order_status = get_order_from_database(order_id)

        return JsonResponse({
            "success": True,
            "data": {
                "order_id": order_id,
                "status": order_status.status,
                "estimated_delivery": order_status.delivery_date,
                "tracking_url": order_status.tracking_url
            }
        })

    except Exception as e:
        logger.error(f"Tool error: {e}")
        return JsonResponse({
            "success": False,
            "error": str(e)
        }, status=500)
```

Now when a customer asks "Where's my order ORD-12345?", the AI will automatically call your tool with the order ID and provide the response.

### Conversation Flows

Build guided conversation paths for specific workflows through the dashboard flow builder.

### Analytics

Track token usage, costs, and performance metrics directly in your Zavu dashboard under the **Analytics** section.

## Using Your Own AI Credentials (Optional)

If you prefer to use your own AI provider credentials for billing purposes, you can configure them in your Zavu dashboard:

1. Go to **Settings** > **AI Providers**
2. Add your API keys for OpenAI, Anthropic, Google, or Mistral
3. When creating an agent, use `provider: "openai"` (or your preferred provider) instead of `provider: "zavu"`

This gives you full control over billing while still benefiting from Zavu's unified API and conversation management.

## Custom Webhook Logic (Advanced)

If you need custom processing alongside the AI agent, you can add logic in your webhook:

```python
@csrf_exempt
@require_POST
def webhook(request):
    """Handle incoming WhatsApp messages from Zavu."""
    signature = request.headers.get('X-Zavu-Signature', '')

    if not verify_webhook_signature(request.body, signature):
        return HttpResponse(status=401)

    try:
        payload = json.loads(request.body)
    except json.JSONDecodeError:
        return JsonResponse({"error": "Invalid JSON"}, status=400)

    if payload.get('type') == 'message.inbound':
        message = payload.get('data', {})
        sender = message.get('from')
        text = message.get('text', '')

        logger.info(f"Message from {sender}: {text}")

        if text.lower() == 'human':
            send_whatsapp_message(
                to=sender,
                text="Connecting you with a human agent..."
            )

    return JsonResponse({"status": "ok"})
```

## Next Steps

- Explore the AI Agents dashboard to monitor conversations
- Add knowledge bases for domain-specific responses
- Create custom tools to integrate with your backend systems
- Build conversation flows for specific use cases
- Add support for media messages (images, documents)

Check out the [Zavu documentation](https://docs.zavu.dev) for more examples and advanced features.