Botpress

Botpress is an open-source platform for building AI-powered chatbots and conversational agents with a visual workflow builder.

Prerequisites

• A Botpress Cloud account (app.botpress.cloud)

• Your TensorX API key from app.tensorx.ai

Configuration

Botpress uses the Execute Code card to make HTTP requests to external APIs like TensorX. This method gives you full control over the API integration.

Botpress has built-in AI models, but you can use the Execute Code card to integrate custom OpenAI-compatible providers like TensorX.

Method: Execute Code Card

Step 1: Create a Workflow

1. Open your bot in Botpress Studio

2. Create a new workflow or open an existing one

3. Add a node where you want to call the AI

Step 2: Add Execute Code Card

1. Click + to add a new card

2. Select Execute Code

3. This opens the code editor

Step 3: Write the API Call

const response = await axios.post(
  'https://api.tensorx.ai/v1/chat/completions',
  {
    model: 'z-ai/glm-5.2',
    messages: [
      {
        role: 'system',
        content: 'You are a helpful customer service assistant.'
      },
      {
        role: 'user',
        content: event.preview // The user's message
      }
    ],
    temperature: 0.7,
  },
  {
    headers: {
      'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
      'Content-Type': 'application/json'
    }
  }
);

// Store the response in a workflow variable
workflow.aiResponse = response.data.choices[0].message.content;

Step 4: Set Environment Variable

1. Go to Chatbot Settings → Variables

2. Add a new Secret variable named TENSORX_API_KEY

3. Paste your TensorX API key

Step 5: Display the Response

After the Execute Code card:

1. Add a Text card

2. Set the content to: {{workflow.aiResponse}}

Building a Complete Chatbot

Basic AI Chatbot Flow

Start → Capture User Input → Execute Code (TensorX) → Display Response → End

1. Start Node: Entry point of the conversation

2. Capture: Stores user message in event.preview

3. Execute Code: Calls TensorX API

4. Text Card: Shows the AI response

Multi-Turn Conversation

For conversations with memory:

// Initialize conversation history if needed
if (!workflow.conversationHistory) {
  workflow.conversationHistory = [];
}

// Add user message to history
workflow.conversationHistory.push({
  role: 'user',
  content: event.preview
});

// Call TensorX API
const response = await axios.post(
  'https://api.tensorx.ai/v1/chat/completions',
  {
    model: 'z-ai/glm-5.2',
    messages: [
      {
        role: 'system',
        content: 'You are a helpful assistant. Maintain context from previous messages.'
      },
      ...workflow.conversationHistory
    ],
    temperature: 0.7,
  },
  {
    headers: {
      'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
      'Content-Type': 'application/json'
    }
  }
);

const assistantMessage = response.data.choices[0].message.content;

// Add assistant response to history
workflow.conversationHistory.push({
  role: 'assistant',
  content: assistantMessage
});

// Store for display
workflow.aiResponse = assistantMessage;

Example Implementations

Customer Support Bot

const response = await axios.post(
  'https://api.tensorx.ai/v1/chat/completions',
  {
    model: 'z-ai/glm-5.3',
    messages: [
      {
        role: 'system',
        content: `You are a customer support agent for Acme Corp.
        
        Guidelines:
        - Be helpful and professional
        - If you can't answer, suggest contacting [e-mail]
        - Keep responses concise
        
        Products: Widget Pro ($99), Widget Basic ($49), Widget Enterprise (contact sales)`
      },
      {
        role: 'user',
        content: event.preview
      }
    ],
    temperature: 0.5,
  },
  {
    headers: {
      'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
      'Content-Type': 'application/json'
    }
  }
);

workflow.aiResponse = response.data.choices[0].message.content;

Intent Classifier

Use AI to classify user intent:

const response = await axios.post(
  'https://api.tensorx.ai/v1/chat/completions',
  {
    model: 'z-ai/glm-5.3',
    messages: [
      {
        role: 'system',
        content: `Classify the user's intent into one of these categories:
        - pricing: Questions about costs, plans, pricing
        - support: Technical issues, problems, bugs
        - sales: Interest in buying, demos, trials
        - general: Everything else
        
        Respond with only the category name, nothing else.`
      },
      {
        role: 'user',
        content: event.preview
      }
    ],
    temperature: 0.1,
  },
  {
    headers: {
      'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
      'Content-Type': 'application/json'
    }
  }
);

workflow.userIntent = response.data.choices[0].message.content.trim().toLowerCase();

Then use Expression transitions to route based on workflow.userIntent.

FAQ Bot with Knowledge Base

// Assuming you have FAQ content in a knowledge base variable
const faqContent = `
Q: What are your hours?
A: We're open Monday-Friday, 9am-5pm EST.

Q: How do I reset my password?
A: Click "Forgot Password" on the login page.

Q: What's your refund policy?
A: 30-day money-back guarantee on all products.
`;

const response = await axios.post(
  'https://api.tensorx.ai/v1/chat/completions',
  {
    model: 'z-ai/glm-5.2',
    messages: [
      {
        role: 'system',
        content: `You are a helpful FAQ assistant. Answer based on this knowledge base:

${faqContent}

If the question isn't covered, say "I don't have that information, but you can contact [e-mail]"`
      },
      {
        role: 'user',
        content: event.preview
      }
    ],
    temperature: 0.3,
  },
  {
    headers: {
      'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
      'Content-Type': 'application/json'
    }
  }
);

workflow.aiResponse = response.data.choices[0].message.content;

Model selection

Call GET /v1/models with your TensorX API key and use one of the returned IDs. If the integration depends on a capability or token limit, query GET /v1/model/info before selecting the model. See Models for examples.

---

Error Handling

Add try-catch for robust error handling:

try {
  const response = await axios.post(
    'https://api.tensorx.ai/v1/chat/completions',
    {
      model: 'z-ai/glm-5.2',
      messages: [
        { role: 'user', content: event.preview }
      ],
      temperature: 0.7,
    },
    {
      headers: {
        'Authorization': `Bearer ${env.TENSORX_API_KEY}`,
        'Content-Type': 'application/json'
      },
      timeout: 30000 // 30 second timeout
    }
  );
  
  workflow.aiResponse = response.data.choices[0].message.content;
  workflow.aiError = null;
  
} catch (error) {
  console.error('TensorX API error:', error.message);
  workflow.aiResponse = "I'm sorry, I'm having trouble processing your request right now.";
  workflow.aiError = error.message;
}

Best Practices

1. Use Environment Variables

Never hardcode API keys. Use Botpress secrets:

• env.TENSORX_API_KEY

2. Set Appropriate Timeouts

AI responses can take a few seconds. Set reasonable timeouts:

{ timeout: 30000 } // 30 seconds

3. Limit Conversation History

For multi-turn conversations, limit history to prevent token overflow:

// Keep only last 10 messages
if (workflow.conversationHistory.length > 10) {
  workflow.conversationHistory = workflow.conversationHistory.slice(-10);
}

4. Handle Edge Cases

• Empty user messages

• Very long messages

• API failures

Troubleshooting

"axios is not defined"

Axios is available globally in Botpress Execute Code. If you get this error:

• Check for typos in axios

• Ensure you're using await for the API call

API Key Not Working

• Verify the secret variable name matches: env.TENSORX_API_KEY

• Check the key at app.tensorx.ai

• Ensure there are no extra spaces

Timeout Errors

• Increase timeout value

• Use a faster model

• Reduce max_tokens for quicker responses

Response Not Displaying

• Check the variable name: workflow.aiResponse

• Ensure the Text card references {{workflow.aiResponse}}

• Debug by logging: console.log(response.data)

Support

Need help? Contact us at [e-mail]