Starter kit: How to create an AI therapist

Build a compliant, empathetic AI therapist with Tavus by following this step-by-step technical guide for configuration, integration, and real-time video support.

Introduction

This guide walks you through the technical steps to build an AI therapist using Tavus conversational video AI and AI human technology. You’ll discover how to define your use case, configure Tavus’s Conversational Video Interface (CVI), integrate essential APIs, and ensure privacy and compliance. We’ll focus specifically on the AI therapist scenario, providing actionable steps, code snippets, and configuration details. Throughout, you’ll find references to Tavus documentation to support your implementation.

Phase 1: Defining the AI therapist use case and business value

Identifying core user scenarios

Start by outlining the main user journeys for your AI therapist. Common scenarios include providing 24/7 emotional support through conversational video, guiding self-reflection sessions, tracking mood, and conducting wellness check-ins. Real-time advice for stress, anxiety, or relationship concerns is also essential. Each scenario should be mapped into a structured conversational flow, which you’ll configure later in Tavus’s Conversation Designer. For inspiration, you can review the Conversational Use Cases.

AI therapy is gaining recognition for its ability to deliver scalable mental health support. For example, AI-driven virtual therapists and chatbots can interact with multiple users at once, offering personalized support and guidance. A study by the National Institutes of Health highlights how AI-powered chatbots provide psychological support during crisis situations, showing their potential to complement traditional therapy methods.

Mapping business value to features

To connect your business objectives to technical features, focus on user engagement and retention by offering instant, empathetic video chat with Tavus’s real-time CVI. Personalized guidance becomes possible when you leverage Tavus’s Persona and Context APIs for tailored responses. If you want to reach a broader audience, integrate Tavus’s Language Support for multilingual accessibility.

Each of these features is directly supported by a Tavus API or configuration layer. For instance, instant video chat uses the full CVI pipeline, while personalized guidance relies on Persona and context APIs. According to Forbes, AI’s ability to provide companionship and therapeutic support is among the top uses of AI, highlighting its growing importance in mental health care.

Compliance and ethical considerations

When building an AI therapist, you need to set clear boundaries. The AI therapist should never diagnose or replace licensed professionals, so it’s crucial to establish escalation paths for crisis scenarios. Always follow Tavus’s Privacy Documentation for data handling, anonymization, and user consent. Reference Tavus’s guidelines on ethical conversational AI in the Introduction.

Configure system prompts and escalation logic to reflect these boundaries. For example, use fallback paths in your dialogue trees to detect and respond to crisis situations. As noted in a Psychology Today article, ethical concerns such as data privacy and lack of regulation are critical when deploying AI in therapy.

Phase 2: Technical requirements and prerequisites

Platform and infrastructure setup

Before you begin, make sure you have a web or mobile frontend capable of embedding Tavus widgets or SDKs. You’ll also need a backend server to manage user sessions, store context, and handle API calls. Secure, compliant cloud storage for user data—using providers like AWS, GCP, or Azure—is essential.

Tavus CVI is cloud-native and works with most modern web and mobile stacks. For more details, see the Getting Started guide. The Matellio blog emphasizes choosing the right technology stack and ensuring security compliance during AI therapy app development.

Tavus API access and authentication

To get started, register at the Tavus Developer Portal and retrieve your API key from the dashboard. Tavus uses token-based authentication, so you’ll need to include your API key in the HTTP header:

http Authorization: Bearer <YOUR_TAVUS_API_KEY>

For OAuth or advanced scopes, refer to the API Authentication Guide. Make sure your API key has permissions for conversational AI endpoints, video generation endpoints, and user context APIs.

Remember to store your API keys securely and never expose them in client-side code.

Integrating third-party services

To enhance your AI therapist, connect to health APIs like Apple Health through your backend for mood tracking. You can use Tavus’s built-in Language Support or connect external translation APIs if you need language translation. For crisis helplines, set up webhook integrations to escalate users to human support when necessary.

All third-party integrations should work seamlessly with Tavus’s conversational flow and data model. For more details, see the Integrations section. The integration of AI in psychotherapy, as discussed in a ScienceDirect article, highlights the potential for AI to enhance mental health care through innovative solutions.

Phase 3: Building the conversational AI therapist

Designing conversational flows

Begin by creating dialogue trees for therapy topics such as stress, anxiety, or self-esteem in the Tavus Conversation Designer. Define fallback and escalation paths to detect crises or handle unrecognized queries, ensuring your AI therapist responds appropriately in sensitive situations.

Here’s an example persona configuration:

{
  "persona_name": "AI Therapist",
  "pipeline_mode": "full",
  "system_prompt": "You are a calm, empathetic AI therapist. Provide supportive, non-judgmental guidance. Escalate if the user expresses crisis or self-harm.",
  "context": "User seeks emotional support. Listen actively, offer coping strategies, and adapt to their emotional state.",
  "default_replica_id": "r95fd27b5a37",
  "layers": {
    "tts": {
      "tts_engine": "cartesia",
      "tts_emotion_control": true
    },
    "llm": {
      "model": "tavus-llama-4",
      "speculative_inference": true
    },
    "perception": {
      "perception_model": "raven-0",
      "ambient_awareness_queries": [
        "Does the user appear distressed or anxious?",
        "Is the user calm or agitated?"
      ],
      "perception_tool_prompt": "Use the `user_emotional_state` tool when body language or tone indicates distress.",
      "perception_tools": [
        {
          "type": "function",
          "function": {
            "name": "user_emotional_state",
            "description": "Report the user's emotional state as inferred from body language and voice.",
            "parameters": {
              "type": "object",
              "required": ["emotional_state", "indicator"],
              "properties": {
                "emotional_state": {
                  "type": "string",
                  "description": "Inferred emotion (e.g., anxious, calm, distressed)"
                },
                "indicator": {
                  "type": "string",
                  "description": "Behavioral cue (e.g., fidgeting, crying)"
                }
              }
            }
          }
        }
      ]
    },
    "stt": {
      "stt_engine": "tavus-advanced",
      "participant_pause_sensitivity": "medium",
      "participant_interrupt_sensitivity": "high",
      "smart_turn_detection": true
    }
  }
}

For a similar persona setup, check out the Customer Service Agent Use Case.

Implementing video AI human avatars

To configure your AI therapist’s visual persona, use Tavus’s Replica API to select or train a digital human avatar. Customize the avatar’s appearance, voice, and tone to emphasize empathy and approachability.

Here’s an API example:

curl -X POST https://api.tavus.io/v1/replica \
  -H "Authorization: Bearer <YOUR_TAVUS_API_KEY>" \
  -d '{"name":"AI Therapist","appearance":"calm, friendly","voice":"soothing"}'

Once you’ve created the avatar, link the Replica to your Persona within the Conversation Designer. For advanced customization, refer to Replica Training.

Phase 4: Real-time interaction and multimodal support

Enabling 24/7 chat and video sessions

To provide real-time video chat, use Tavus’s WebRTC or video streaming APIs. Deploy your backend in regions close to your users to reduce latency and improve responsiveness.

Implement session management and reconnection logic to keep the experience reliable, even if connections drop. The Mahalo Health article highlights the importance of multimodal support in AI therapy, including text-based chat, voice interactions, and multimedia content.

Multilingual and multimodal capabilities

Enable Language Support in your Persona configuration to reach users in multiple languages:

"language_support": {
  "enabled": true,
  "languages": ["en", "es", "fr", "de"]
}

Support both text and voice/video input and output by configuring the STT (speech-to-text) and TTS (text-to-speech) layers in your Persona. For supported languages and configuration details, see Language Support.

Mood and progress tracking

Integrate device sensors, such as Apple Watch, via your backend to collect wellness data and display wellness metrics in your user dashboard. Use Tavus’s perception layer to infer mood from video and audio, and supplement this with third-party sensor data when available.

The integration of AI in mental health care, as discussed in a ScienceDirect review, underscores the potential of AI to enhance therapeutic outcomes through data-driven insights.

Phase 5: Integration, testing, and privacy

Seamless integration with Tavus features

Embed Tavus widgets or SDKs into your app or website to bring conversational AI, video avatars, and analytics to your users. For step-by-step instructions, follow the Quickstart guide.

User privacy, security, and anonymity

Implement end-to-end encryption for all video and chat data to protect user privacy. Use anonymized user IDs and always obtain explicit user consent before storing any data. Follow Tavus’s privacy best practices as outlined in the Privacy Documentation.

Store sensitive data only on secure, compliant infrastructure, and review your privacy policy regularly. A ScienceDirect article highlights the ethical considerations of integrating AI into mental healthcare, emphasizing the need for robust privacy measures.

Testing and quality assurance

Develop both automated and manual test cases to cover conversation flows—including crisis escalation—video rendering and avatar performance, and multilingual support. Use Tavus’s sandbox environment for pre-production validation.

For troubleshooting, refer to Errors and Status Details.

Phase 6: Deployment, monitoring, and best practices

Production deployment and scaling

Deploy your application using container orchestration, such as Kubernetes, to ensure scalability and reliability. Follow Tavus’s cloud deployment guidelines for optimal performance, and enable auto-scaling to handle peak usage while maintaining high availability.

According to Solutions Review, establishing a structured monitoring protocol is crucial for successful AI deployment.

Monitoring, analytics, and continuous improvement

Use Tavus analytics APIs to monitor user engagement, conversation quality, and emotional outcomes. Set up alerting for crisis keywords and feedback loops to update your models and dialogue trees as needed.

For real-time monitoring, see Webhooks and Callbacks.

Common implementation patterns and best practices

Always provide a fallback to human support for crisis or complex cases. Update your models and dialogue trees regularly based on user feedback, and collect user feedback after sessions to support continuous improvement.

For reusable implementation patterns, review Tavus’s Implementation Playbooks.

Next Steps

Start building your AI therapist by registering for API access at the Tavus Developer Portal. Configure your conversational flows and integrate the key features outlined above. Explore advanced customization and continuous improvement using Tavus’s documentation and analytics tools to deliver real-time, human-like support that prioritizes privacy, ethics, and user experience.