ceven

Kaggle

Automates the pipeline from dataset discovery and download to kernel execution and competition submission, turning Kaggle into a programmable backend for your ML experiments.

Try Kaggle in Ceven

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Why use Ceven?

  1. AI native Kaggle integration

    • Describe the outcome and Ceven picks the right Kaggle calls, fills the parameters, and checks the result.
    • Structured, agent friendly tool schemas so each call runs reliably instead of by guesswork.
    • Rich coverage for reading, writing, and querying your Kaggle data, across all 35 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Kaggle access.
    • Per user and per environment credentials instead of shared keys.

  3. Agent optimized design

    • Actions are tuned from real success and error rates so reliability climbs over time.
    • Full execution logs so you always know what ran in Kaggle, when, and on whose behalf.
    • The agent pauses and asks when Kaggle is unclear instead of plowing ahead.

  4. Enterprise grade security

    • Fine grained access so you control which agents and people can reach Kaggle.
    • Least privilege by default, read scopes first and only the writes a workflow needs.
    • A full audit trail of every Kaggle action to support review and sign off.

Supported tools

Every action Ceven's agents can run on Kaggle, and when to use it.

Download competition data
Use this when you need to pull raw data files for a specific competition after you have verified the competition ID.
Create dataset
Push a new dataset to Kaggle with full metadata. Use this after files are uploaded and metadata is finalized.
Get dataset status
Check the processing state of a dataset upload to ensure it is ready for use.
Create dataset version
Publish an updated version of an existing dataset when files or metadata have changed.
Submit competition entry
Send a prediction file to a competition leaderboard using a previously obtained blob token.
Get config directory
Locate the folder containing your kaggle.json credentials on the local system.
Get config file
Retrieve the specific filename of the Kaggle API configuration file.
List config keys
Pull a list of set configuration options without revealing the actual secret values.
Set configuration
Update local CLI settings such as the default download path or proxy settings.
Unset configuration
Remove a specific local CLI configuration parameter to return it to default.
Dataset init
Create a dataset metadata JSON file in a local folder to prepare for upload.
List dataset files
Pull a paginated list of files within a specific dataset by owner and slug.
Kernel init
Initialize a kernel metadata JSON file in a local folder before pushing to Kaggle.
Download kernel output
Pull the latest results and files produced by a Kaggle kernel run.
Get kernel status
Monitor the execution state of a submitted kernel to see if it is running or finished.
List datasets
Search and list available Kaggle datasets using filters and pagination.
Download competition data files
Tool to download competition data files. Use after confirming the competition ID.
Initialize Kaggle Configuration
Tool to initialize Kaggle API client configuration. Attempts CLI first; if unavailable, it falls back to creating ~/.kaggle/kaggle.json (or $KAGGLE_CONFIG_DIR/kaggle.json).
Dataset Create
Tool to create a new Kaggle dataset with full metadata. Use after uploading files and finalizing metadata. Returns creation status and message.
Get Kaggle Config Directory
Tool to retrieve the directory of the Kaggle API configuration file. Use when you need to locate the directory containing your kaggle.json credentials.
Get Kaggle Config File
Tool to retrieve the filename of the Kaggle API configuration file. Use when you need to find out where the local Kaggle config file is stored before reading or updating.
List Kaggle Configuration Keys
Tool to list local Kaggle API configuration keys. Use when you need to see which configuration options are set without revealing values.
Get Kaggle Config Path
Tool to retrieve local Kaggle API configuration file path. Use when you need to know the location of the Kaggle config before operations.
Reset Kaggle Configuration
Tool to reset local Kaggle CLI configuration to defaults. Clears CLI managed keys ('competition', 'path', 'proxy').
Set Kaggle Configuration
Tool to set a Kaggle CLI configuration parameter. Use when updating local CLI settings such as default download path or proxy. Ensure Kaggle CLI is installed.
Unset Kaggle Configuration
Tool to unset a Kaggle CLI configuration parameter. Use when removing local CLI settings such as default download path or proxy. Ensure Kaggle CLI is installed.
View Kaggle Configuration
Tool to view local Kaggle API configuration. Use when you need to confirm credentials before API calls.
Kaggle Dataset Init
Tool to initialize a dataset metadata.json file in a local folder. Use when preparing a dataset folder before uploading to Kaggle.
List Kaggle Dataset Files
Tool to list files in a Kaggle dataset. Use when you need to retrieve paginated file listings by owner and dataset slugs, with optional version and paging controls.
Kaggle Kernel Init
Tool to initialize a kernel metadata.json file in a local folder. Use when preparing a kernel folder before pushing to Kaggle.

30 actions · scroll to see them all

Frequently asked questions

Ceven manages your Kaggle credentials by interacting with the kaggle.json file required by the Kaggle API. When you first connect, the agent looks for the configuration in the standard Kaggle config directory. If it is not found, the agent can initialize the configuration by creating the necessary JSON file in your home directory or a specified config path. We treat this file as a sensitive secret, ensuring that the raw API key is never exposed in the clear within your workflow logs or passed to the model in a way that allows extraction. You can update or reset these configurations using the dedicated management tools provided in the integration.
Yes. The integration allows you to automate the entire submission loop. Once your model has finished training and you have generated a CSV prediction file, the agent can handle the upload process and call the submission endpoint. It is important to note that you must first accept the competition rules on the Kaggle website manually before the API will accept submissions. Once the rules are accepted, the agent can programmatically submit entries and you can then use the kernel status tool to verify that the submission was processed correctly by the Kaggle backend.
One specific quirk of the Kaggle API is the way it handles dataset versions. You cannot simply overwrite a file in a dataset; you must create a new version of the dataset entirely. This means that every time your workflow updates a data file, the agent must call the create dataset version tool. Additionally, very large datasets may experience processing delays after upload. The agent handles this by using the get dataset status tool to poll the API until the processing state is marked as complete, preventing your downstream workflows from trying to access files that are not yet live.
Kaggle kernels run asynchronously on Kaggle servers. When the agent pushes a kernel, Kaggle returns a kernel ID. The agent then uses the get kernel status tool to periodically check if the run is still pending, running, or has completed. If the kernel fails, the agent can pull the logs to help you debug the error. Once the status returns as complete, the agent can automatically trigger the download kernel output action to bring your model weights or prediction files back into your local environment or a cloud storage bucket.
Yes, the list datasets action allows the agent to query Kaggle for datasets based on specific keywords or filters. You can build a workflow that runs every morning to search for new datasets related to a specific topic, such as financial markets or healthcare. When the agent finds a dataset that matches your criteria and has a high enough usability score, it can notify you via Slack or automatically download the files to a staging area for your data pipeline to ingest and analyze.
As long as the API key used in your kaggle.json file has the necessary permissions, Ceven can interact with both public and private datasets. The agent uses the same authentication flow for both. If you are working within a team, ensure that the account associated with the API key has been granted explicit access to the private dataset. The agent will then be able to list files, download data, and create new versions just as it would with a public dataset.
Kaggle imposes strict time limits on kernel execution depending on whether the kernel is using a CPU or GPU. If a kernel exceeds these limits, the status will move to a failed state. The Ceven agent detects this state change through the get kernel status tool. You can configure your workflow to handle this by either attempting a retry with a smaller data sample or by sending an alert to the developer. Because the agent tracks the status in real time, you do not have to manually refresh the Kaggle dashboard to find out that a long running job has timed out.
The current integration relies on the local Kaggle config path and the kaggle.json file. To manage multiple accounts, you would need to programmatically update the configuration keys using the set configuration and unset configuration tools before switching between account contexts. We recommend using a single service account for automated workflows to avoid the complexity of rotating credentials for multiple users. The agent can help manage these transitions by storing different config paths for different projects and switching the active path before executing Kaggle specific actions.

Alternatives to Kaggle

Other tools that solve a similar problem. Ceven supports these too, so you can switch or run more than one at once.

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