> For the complete documentation index, see [llms.txt](https://docs.tensorx.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tensorx.ai/automation-platforms/activepieces.md).

# Activepieces

Activepieces is an open-source automation platform similar to Zapier, with a visual workflow builder for connecting apps and services.

## Prerequisites

* An Activepieces instance (self-hosted or cloud)
* Your TensorX API key from [app.tensorx.ai](https://app.tensorx.ai)

## Configuration

Since the built-in OpenAI piece uses a hardcoded endpoint, you'll use the **HTTP Request** piece to connect to TensorX's OpenAI-compatible API.

{% hint style="info" %}
The HTTP Request method gives you full control over the API call and works with any OpenAI-compatible endpoint.
{% endhint %}

## Method: HTTP Request Piece

### Step 1: Add HTTP Request Piece

1. Open your Activepieces flow
2. Click **+** to add a new piece
3. Search for **HTTP** and select **HTTP Request (Advanced)**

### Step 2: Configure the Request

Set up the HTTP request with these parameters:

| Field      | Value                                        |
| ---------- | -------------------------------------------- |
| **Method** | `POST`                                       |
| **URL**    | `https://api.tensorx.ai/v1/chat/completions` |

### Step 3: Add Headers

Click **Add Header** and add the following:

| Header          | Value                         |
| --------------- | ----------------------------- |
| `Authorization` | `Bearer YOUR_TENSORX_API_KEY` |
| `Content-Type`  | `application/json`            |

### Step 4: Configure the Body

Set **Body Type** to `Raw (JSON)` and enter:

```json
{
  "model": "z-ai/glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful assistant."
    },
    {
      "role": "user",
      "content": "{{trigger.message}}"
    }
  ],
  "temperature": 0.7
}
```

{% hint style="info" %}
Replace `{{trigger.message}}` with your actual data source from previous pieces in your flow.
{% endhint %}

### Step 5: Parse the Response

The API returns a JSON response. To extract the assistant's message:

1. Add a **Code** piece after the HTTP Request
2. Use JavaScript to parse the response:

```javascript
const response = inputs.httpResponse.body;
return {
  message: response.choices[0].message.content,
  model: response.model,
  tokens: response.usage.total_tokens
};
```

## Example Flows

### Email Summarizer

1. **Trigger**: New Email (Gmail piece)
2. **HTTP Request**: Send email body to TensorX for summarization
3. **Action**: Save summary to Notion or send to Slack

Request body:

```json
{
  "model": "z-ai/glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "Summarize the following email in 2-3 sentences."
    },
    {
      "role": "user",
      "content": "{{trigger.emailBody}}"
    }
  ],
  "temperature": 0.3
}
```

### Customer Support Classifier

1. **Trigger**: New Support Ticket (webhook or integration)
2. **HTTP Request**: Classify ticket category
3. **Branch**: Route based on category
4. **Action**: Assign to appropriate team

Request body:

```json
{
  "model": "z-ai/glm-5.3",
  "messages": [
    {
      "role": "system",
      "content": "Classify this support ticket into one of: billing, technical, general, urgent. Return only the category name."
    },
    {
      "role": "user",
      "content": "{{trigger.ticketContent}}"
    }
  ],
  "temperature": 0.1
}
```

### Content Generator

1. **Trigger**: New Row in Google Sheets
2. **HTTP Request**: Generate content based on topic
3. **Action**: Update sheet with generated content

Request body:

```json
{
  "model": "z-ai/glm-5.2",
  "messages": [
    {
      "role": "system",
      "content": "Write a short blog post introduction about the given topic."
    },
    {
      "role": "user",
      "content": "Topic: {{trigger.topic}}"
    }
  ],
  "temperature": 0.8
}
```

## 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](/api-reference/models.md) for examples.

***

## Handling Streaming Responses

For long responses, you may want to handle streaming:

1. The TensorX API supports streaming with `"stream": true`
2. However, HTTP Request piece may not handle SSE well
3. For streaming, consider using a webhook-based approach or the Code piece with fetch

## Error Handling

### Add Error Handling

1. Click on your HTTP Request piece
2. Enable **Handle Errors**
3. Add a branch to handle failed requests

### Common Errors

| Error | Cause             | Solution                    |
| ----- | ----------------- | --------------------------- |
| 401   | Invalid API key   | Check your API key          |
| 400   | Malformed request | Verify JSON body format     |
| 404   | Wrong endpoint    | Use `/v1/chat/completions`  |
| 429   | Rate limit        | Add delays between requests |

## Creating a Reusable Template

To reuse your TensorX configuration:

1. Create a flow template with the HTTP Request piece configured
2. Use flow variables for dynamic content
3. Duplicate the template for new automations

## Storing API Key Securely

Instead of hardcoding your API key:

1. Go to **Settings** → **Connections**
2. Create a new **Custom Connection**
3. Store your API key securely
4. Reference it in your flows using `{{connections.tensorx.apiKey}}`

## Troubleshooting

### "Request Failed" Error

* Check the URL is exactly `https://api.tensorx.ai/v1/chat/completions`
* Verify headers are correctly formatted
* Test your API key with a simple curl command

### Empty Response

* Check the response body parsing
* Verify the model ID is correct
* Look at the raw response for error messages

### Timeout Errors

* Increase the timeout setting in HTTP Request piece
* Use a faster model for time-sensitive flows
* Consider breaking long operations into smaller chunks

## Support

Need help? Contact us at <support@tensorx.ai>


---

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