> For the complete documentation index, see [llms.txt](https://docs.virbe.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.virbe.ai/dashboard-management/conversation-flows/nodes/flow-control-nodes/checkpoint.md).

# Checkpoint

It creates a savepoint in the flow, enabling the conversation to resume from that point based on user input or system signals.

Setting up checkpoints:

1. Place checkpoint node at key conversation points
2. Configure **"Wait for user input"** if interaction is needed
3. Connect both user input and signal paths if required
4. Create paths for different continuation scenarios
5. Link to appropriate next steps in the flow

Example: As shown in the screenshot, checkpoints are used after intent recognition to maintain conversation state:

<figure><img src="https://1213579860-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F-MaU7JJyoXT5PfhTD9dJ%2Fuploads%2FxvE618XJkND2g7LkY6lZ%2Fhub-assistant-virbe.virbe.app_dashboard_conversation-flows_ca98acce-9cff-4d65-ad55-7ca28f98965e_node_9a1a61d9-c1bd-4f46-bd6e-a47f2031489d.png?alt=media&amp;token=405ae732-5dc2-4dc3-82fb-e051a81a92a1" alt=""><figcaption><p>Example of using Checkpoints</p></figcaption></figure>

```
Intent matcher identifies topic:
- Pricing questions
- Troubleshooting needs
- Upgrade requests

Each path has Checkpoint:
- Wait for user input enabled
- Handle both user responses and signals
- Continue with appropriate LLM responses
- Maintain context through the interaction
```

Common use cases:

* Maintain conversation state during multi-step processes
* Handle interruptions gracefully
* Create conversation branches
* Manage form-filling and data collection sequences

{% hint style="info" %}
**Important considerations:**

* Consider both user inputs and system signals
* Consider using "Go to flow" for more complex paths
* Test checkpoint returns thoroughly
* Plan checkpoint connections carefully
  {% endhint %}