> 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/langflow.md).

# Langflow

Langflow is a visual framework for building RAG applications and multi-agent AI workflows using a drag-and-drop interface.

## Prerequisites

* A Langflow instance (self-hosted or [Langflow Cloud](https://astra.datastax.com/langflow))
* Your TensorX API key from [app.tensorx.ai](https://app.tensorx.ai)

## Configuration

Langflow connects to TensorX through the **OpenAI** model component, which supports custom base URLs for OpenAI-compatible APIs.

### Step 1: Add OpenAI Model Component

1. Open your Langflow workspace
2. In the component sidebar, navigate to **Models**
3. Drag the **OpenAI** component onto your canvas

### Step 2: Configure the Component

Click on the OpenAI component to open its settings and configure:

| Parameter           | Value                              |
| ------------------- | ---------------------------------- |
| **OpenAI API Base** | `https://api.tensorx.ai/v1`        |
| **OpenAI API Key**  | Your TensorX API key               |
| **Model Name**      | An ID returned by `GET /v1/models` |

{% hint style="info" %}
The **OpenAI API Base** field allows you to point to any OpenAI-compatible endpoint. Enter the full URL including `/v1`.
{% endhint %}

### Step 3: Adjust Model Parameters

Configure optional parameters as needed:

| Parameter       | Description                         | Default |
| --------------- | ----------------------------------- | ------- |
| **Temperature** | Controls randomness (0-2)           | 0.7     |
| **Max Tokens**  | Optional application response limit | Unset   |
| **Stream**      | Enable streaming responses          | true    |

## Building a Basic Flow

### Simple Chat Flow

1. Add an **OpenAI** model component (configured for TensorX)
2. Add a **Chat Input** component
3. Add a **Chat Output** component
4. Connect: Chat Input → OpenAI → Chat Output
5. Click **Run** to test

### RAG Flow with Vector Store

1. Add an **OpenAI** model component for TensorX
2. Add a **Vector Store** component (Chroma, Pinecone, etc.)
3. Add a **Retriever** component
4. Add a **Prompt** component with RAG template
5. Connect components to build the retrieval pipeline
6. Add Chat Input/Output for interaction

## 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.

***

## Agent Workflows

When building agent workflows with tools:

### Step 1: Configure Model for Tool Calling

Query `GET /v1/model/info` and select a model whose `supports_function_calling` value is `true`:

```
OpenAI API Base: https://api.tensorx.ai/v1
Model Name: <MODEL_ID>
Temperature: 0.3
```

### Step 2: Add Tool Components

1. Add tool components from the **Tools** section
2. Available tools include: Calculator, Web Search, Python Code, etc.
3. Connect tools to your agent flow

### Step 3: Build Agent Logic

1. Add an **Agent** component
2. Connect your TensorX model to the agent's LLM input
3. Connect tool components to the agent
4. Add memory component if needed for conversation history

## Using Multiple Models

You can use different TensorX models in the same flow:

1. Add multiple **OpenAI** components
2. Configure each with a different model
3. Use a **Router** component to direct queries to the appropriate model
4. Or chain models for multi-step processing

## Memory and Context

### Adding Conversation Memory

1. Add a **Memory** component (e.g., Buffer Memory)
2. Connect it to your OpenAI model component
3. This enables multi-turn conversations with context

### Managing Context Length

For long conversations:

* Use models with larger context windows
* Implement summarization for older messages
* Use the Memory component's built-in truncation

## Troubleshooting

### "Invalid API Key" Error

* Verify your API key at [app.tensorx.ai](https://app.tensorx.ai)
* Check for extra spaces in the API key field
* Ensure the base URL is correct: `https://api.tensorx.ai/v1`

### "Model Not Found" Error

* Use the full model ID with provider prefix
* Include the provider prefix shown by `GET /v1/models`
* Check available models at [tensorx.ai/models](https://tensorx.ai/models)

### Connection Timeout

* Check your Langflow instance's network connectivity
* Verify there are no firewall rules blocking the API
* Try increasing timeout settings if available

### Flow Not Responding

* Check the Langflow logs for error messages
* Verify all required connections are made
* Test individual components before full flow

## Support

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


---

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