> 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**      | `deepseek/deepseek-chat-v3.1` |

{% 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                | Recommended |
| --------------- | -------------------------- | ----------- |
| **Temperature** | Controls randomness (0-2)  | 0.7         |
| **Max Tokens**  | Maximum response length    | 4096        |
| **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

## Recommended Models

{% hint style="info" %}
**For Coding Tasks:**

* `z-ai/glm-5.1` - Best for tool calling and structured outputs
* `minimax/minimax-m2.5` - Best for complex reasoning tasks
  {% endhint %}

| Model ID                      | Best For                   |
| ----------------------------- | -------------------------- |
| `deepseek/deepseek-chat-v3.1` | General chat, balanced     |
| `z-ai/glm-5.1`                | Tool calling, agents, code |
| `minimax/minimax-m2.5`        | Reasoning, functions       |
| `moonshotai/kimi-k2.5`        | Vision, long context       |

## Agent Workflows

When building agent workflows with tools:

### Step 1: Configure Model for Tool Calling

Use `z-ai/glm-5.1` for best tool calling support:

```
OpenAI API Base: https://api.tensorx.ai/v1
Model Name: z-ai/glm-5.1
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

## Example Configurations

### Code Assistant

```
OpenAI API Base: https://api.tensorx.ai/v1
Model Name: z-ai/glm-5.1
Temperature: 0.2
Max Tokens: 8192
```

### Creative Writing

```
OpenAI API Base: https://api.tensorx.ai/v1
Model Name: deepseek/deepseek-chat-v3.1
Temperature: 0.9
Max Tokens: 4096
```

### Analytical Tasks

```
OpenAI API Base: https://api.tensorx.ai/v1
Model Name: minimax/minimax-m2.5
Temperature: 0.4
Max Tokens: 4096
```

## 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
* Example: `deepseek/deepseek-chat-v3.1` not just `deepseek-chat`
* Check available models at [app.tensorx.ai/models](https://app.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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