> ## Documentation Index
> Fetch the complete documentation index at: https://docs.thinkplatform.ai/llms.txt
> Use this file to discover all available pages before exploring further.

> ## Agent Instructions
> Choose User guide for browser tasks, Developers for CLI, SDK and external agents, and Administration for organization management.
> Confirm identity and organization/project context before operations. Documentation access does not grant ThinkPlatform permissions.
> Review side effects, verify changes with a read, and do not blindly retry an operation with an uncertain outcome.
> Use documented UI steps for the web app and documented public CLI or SDK interfaces for automation. Follow each page’s prerequisites and authentication requirements.

# Data management

> Understand where project data lives before moving or deleting it.

ThinkPlatform projects can contain application assets and use Data Lake storage. Agent workspace local files are a separate runtime location: treat them as ephemeral unless durable backing has been configured.

## Keep an output

For an agent result offered as a downloadable file, you can preserve a copy in the project's Data Lake:

1. In the agent chat, open the result file card and download it to your device. Check that the downloaded file contains the expected output. If no download action is offered, ask the workspace administrator for an approved export method before replacing the workspace.
2. With the CLI installed, authenticated, and set to the intended project, upload that local file to an authorized project path. For example, if the downloaded file is `./result.txt` and `project/uploads/` is permitted, first run the dry run and inspect its destination:

```bash theme={"system"}
tp datalake upload-file --local-path ./result.txt --remote-path project/uploads/result.txt --dry-run
tp datalake upload-file --local-path ./result.txt --remote-path project/uploads/result.txt
```

3. Follow [Upload and inspect Data Lake files](https://docs.thinkplatform.ai/cli/guides/files.md) to list and download or preview the destination. Read it back and verify the contents before restarting, replacing, or deleting the workspace. An interrupted upload may have written data; inspect the destination before retrying. This path requires both permission to download the result and permission to write the selected Data Lake folder.

## Review data before deletion

Select the intended organization and project and identify the affected resource and its dependencies. Review which people, agents, flows, and applications use it, confirm you have the appropriate permissions, and arrange a recoverable copy where needed. Confirm the target in the deletion dialog and check the result afterward. Deletion can interrupt consumers or remove work they depend on; recovery depends on the storage and retention configuration of your deployment.

## Check Data Lake access

Use the project's permitted paths and verify saved files in the intended location. Workspace access to Data Lake paths is controlled separately from a person's project membership. If a file is unavailable, check the project, path, workspace access configuration, and underlying storage permissions with the administrator responsible for that storage. See [Explore files and data assets](https://docs.thinkplatform.ai/guides/data/files-and-data-assets.md).

For access management, see [Members and permissions](https://docs.thinkplatform.ai/admin/members-and-permissions.md). For programmatic workflows, see the [developer overview](https://docs.thinkplatform.ai/developers/overview.md), [CLI](https://docs.thinkplatform.ai/cli/get-started/overview.md), and [Python SDK](https://docs.thinkplatform.ai/python-sdk/overview.md).


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.