> 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/flowise.md).

# Flowise

Flowise is a low-code visual tool for building LLM applications and AI agents using a drag-and-drop interface.

## Prerequisites

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

## Configuration

Flowise connects to TensorX through the **ChatOpenAI Custom** node, which allows you to specify a custom OpenAI-compatible endpoint.

### Step 1: Add ChatOpenAI Custom Node

1. Open your Flowise canvas
2. In the left sidebar, navigate to **Chat Models**
3. Drag the **ChatOpenAI Custom** node onto your canvas

### Step 2: Configure the Node

Configure the ChatOpenAI Custom node with these settings:

| Parameter      | Value                              |
| -------------- | ---------------------------------- |
| **Base Path**  | `https://api.tensorx.ai/v1`        |
| **Model Name** | An ID returned by `GET /v1/models` |

{% hint style="info" %}
Use the full model ID including the provider prefix.
{% endhint %}

### Step 3: Create API Credential

1. Click the **Credential** field and select **Create New**
2. Select **OpenAI API** as the credential type
3. Enter your TensorX API key
4. Save the credential

### Step 4: Adjust Optional Parameters

You can customize these optional settings:

| Parameter       | Description                | Default       |
| --------------- | -------------------------- | ------------- |
| **Temperature** | Controls randomness (0-1)  | 0.7           |
| **Max Tokens**  | Maximum response length    | Model default |
| **Streaming**   | Enable streaming responses | true          |

## Example Configuration

```
Base Path: https://api.tensorx.ai/v1
Model Name: <MODEL_ID>
Temperature: 0.7
```

## Building a Chatflow

Here's how to create a basic chatflow with TensorX:

1. Add a **ChatOpenAI Custom** node and configure as shown above
2. Add a **Chat Input** node
3. Connect Chat Input → ChatOpenAI Custom
4. Add a **Chat Output** node
5. Connect ChatOpenAI Custom → Chat Output
6. Click **Save Chatflow**

## Building an AI Agent

To create an agent with tools:

1. Add a **ChatOpenAI Custom** node configured for TensorX
2. Add an **Agent** node (e.g., OpenAI Functions Agent)
3. Connect your model to the Agent's LLM input
4. Add tool nodes (Calculator, Web Search, etc.)
5. Connect tools to the Agent
6. Add Chat Input and Chat Output nodes

For agent workflows that require tool calling, query `GET /v1/model/info` and select a model whose `supports_function_calling` value is `true`.

## 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.

***

## Troubleshooting

### "Invalid API Key" Error

* Verify your API key at [app.tensorx.ai](https://app.tensorx.ai)
* Ensure the credential type is set to **OpenAI API**
* Check that the Base Path is exactly `https://api.tensorx.ai/v1`

### "Model Not Found" Error

* Use the full model ID including the provider
* Check available models at [tensorx.ai/models](https://tensorx.ai/models)

### Streaming Issues

If responses are slow or not streaming:

* Enable the **Streaming** option in the node settings
* Ensure your Flowise version supports streaming

## Support

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


---

# Agent Instructions
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