ceven

Hugging Face

Automates the lifecycle of your machine learning assets by managing repositories, handling dataset commits, and coordinating model discussions from a single workflow.

Try Hugging Face in Ceven

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Why use Ceven?

  1. AI native Hugging Face integration

    • Describe the outcome and Ceven picks the right Hugging Face 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 Hugging Face data, across all 135 of its actions.

  2. Managed auth

    • Built in OAuth with automatic token refresh and rotation.
    • One place to manage, scope, and revoke Hugging Face 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 Hugging Face, when, and on whose behalf.
    • The agent pauses and asks when Hugging Face is unclear instead of plowing ahead.

  4. Enterprise grade security

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

Supported tools

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

Create repository
Use this to initialize a new model, dataset, or Space repository on the Hub.
Create models commit
Push files or updates to a model repository using JSON or NDJSON formats.
Create datasets commit
Add or update files in a dataset repository, including support for large file storage.
Create discussion
Start a new conversation or report an issue on a model, dataset, or Space.
Filter dataset rows
Pull specific rows from a dataset split using SQL like query conditions.
Create or update Space secret
Upsert sensitive environment variables or API keys for a Hugging Face Space.
Request repository access
Submit a request to access a gated model or dataset that requires approval.
Create models branch
Create an isolated branch in a model repository for experimentation or versioning.
Create discussion comment
Add a reply or new comment to an existing repository discussion.
Create webhook
Set up a webhook to trigger external workflows on repository or discussion events.
Check dataset validity
Pull available features like preview and search for a specific dataset.
Generate chat completion
Get a response from a conversational model or vision model via the inference API.
Change Discussion Status
Tool to change the status of a Hugging Face repository discussion. Use when you need to open or close discussions on models, datasets, or spaces.
Check Models Upload Method
Tool to check if files should be uploaded through the Large File mechanism or directly. Use when preparing to upload files to a Hugging Face model repository to determine the appropriate upload method for each file.
Check Spaces Upload Method
Tool to check if files should be uploaded through the Large File mechanism or directly to Hugging Face Spaces. Use when preparing to upload files to a Hugging Face Space repository to determine the appropriate upload method for each file.
Claim Paper Authorship
Tool to claim authorship of a paper on Hugging Face. Use when you need to associate yourself or another user with an ArXiv paper.
Create Collection
Tool to create a new collection on Hugging Face. Use when you need to organize and curate models, datasets, spaces, papers, or other collections into a named collection.
Create Datasets Branch
Tool to create a new branch in a Hugging Face dataset repository. Use when you need to create a branch for versioning or experimentation with dataset changes.
Check Dataset File Upload Method
Tool to check if files should be uploaded via Large File Storage (LFS) or directly to a Hugging Face dataset repository. Use before uploading files to determine the correct upload method for each file based on size and repository settings.
Create Datasets Tag
Tool to create a tag on a Hugging Face dataset repository. Use when you need to mark a specific revision with a named tag.
Pin discussion
Tool to pin or unpin a discussion on a Hugging Face repository (model, dataset, or Space). Use when you need to highlight important discussions by pinning them to the top of the list, or unpin them when they're no longer priority.
Create Models Tag
Tool to create a tag on a Hugging Face model repository. Use when you need to mark a specific revision with a named tag.
Create Paper Comment
Tool to create a new comment on a Hugging Face paper. Use when you need to add comments or feedback to research papers on Hugging Face.
Create Papers Comment Reply
Tool to create a reply to a comment on a Hugging Face paper. Use when you need to respond to an existing comment on a paper discussion.
Create Papers Index
Tool to index a paper from arXiv by its ID on Hugging Face. Use when you need to make a paper searchable and accessible on the platform. Note: If the paper is already indexed, only its authors can re index it.
Create spaces branch
Tool to create a new branch in a Hugging Face space repository. Use when you need to create a branch for experimenting with space changes, versioning, or creating isolated development environments.
Create Spaces Commit
Tool to create a commit in a Hugging Face Space repository. Use when you need to add, update, or delete files in a Space. Supports both JSON and NDJSON (recommended) payload formats for commits.
Create Spaces Tag
Tool to create a tag on a Hugging Face space repository. Use when you need to mark a specific revision with a named tag.
Create or update Space variable
Tool to create or update a variable in a Hugging Face Space. Use when you need to add or update environment variables or configuration values for a Space. This action upserts the variable, meaning it will create a new variable if it doesn't
Create SQL Console Embed
Tool to create a SQL Console embed for querying datasets on Hugging Face. Use when you need to create a shareable SQL query interface for exploring dataset splits. The embed allows users to execute SQL queries against dataset views (e.g., t

30 actions · scroll to see them all

Frequently asked questions

Ceven interacts with the Hugging Face Hub API to determine if a file requires the Large File Storage mechanism or can be uploaded directly. When a workflow triggers a commit, the agent checks the file size and type against the Hub requirements. For files exceeding the standard limit, it utilizes the LFS pointer system to ensure your model weights or large datasets are stored correctly without hitting API payload limits. This prevents commit failures that usually happen when users try to push multi gigabyte files through standard git operations. The agent manages the metadata so your files remain accessible and versioned properly on the platform.
Yes. Ceven can programmatically submit access requests to gated repositories on your behalf. For models that require a manual review of your use case, the agent can fill in the required fields and submit the request. Once you are granted access via the Hugging Face web interface, the agent uses your OAuth token to pull the gated weights or datasets into your workflow. It can also be used to monitor the status of these requests if you are managing access for a large team of researchers who need consistent environment permissions across different projects.
Ceven treats these as two distinct types of configuration. Space variables are for non sensitive configuration values that can be visible to those with repository access, such as a public API endpoint or a theme setting. Space secrets are encrypted environment variables used for sensitive data like private API keys or database passwords. Use the create or update Space secret action when you need to protect credentials. The agent ensures that secrets are handled securely and are never logged in plain text within the workflow history, maintaining the security boundary provided by the Hugging Face infrastructure.
The filter dataset rows action allows you to treat a Hugging Face dataset split as a virtual table. Instead of downloading the entire dataset to your local environment, Ceven sends a SQL like query to the Hub. The platform filters the rows on the server side and returns only the matching records. This is critical for massive datasets where downloading the full file would be impossible or too slow. You can use standard conditions to filter by column values or sort the results, making it easy to pull a specific subset of data for model evaluation or debugging.
Yes. Hugging Face imposes rate limits on their API, particularly for the Inference API and frequent repository commits. If a Ceven workflow triggers a high volume of requests in a short window, you may encounter 429 Too Many Requests errors. The agent implements an exponential backoff strategy to handle these gracefully, but for very large scale operations, you should consider a PRO account or dedicated endpoints. Another quirk is that some gated models have very strict rate limits even for authorized users, which may slow down the speed at which the agent can pull data for your workflows.
Ceven can automate the communication layer of peer review. It can create discussions when a new model branch is pushed, pin the most important feedback to the top of the list, and post automated evaluation results as comments. While the actual intellectual review requires a human, the agent handles the logistics. For example, you can set up a workflow where a model is only merged into the main branch after a specific user has posted a comment containing the word approved. This turns the Hugging Face discussion board into a formal gate for your production models.
Ceven uses OAuth2 to connect to your Hugging Face account. When you authorize the integration, Hugging Face provides a token that allows the agent to perform actions on your behalf based on the scopes you approved. This means you do not have to share your raw user access token with the platform. The token is stored encrypted and is used to sign every API request. If you ever want to stop the automation, you can revoke the application access from your Hugging Face settings, which immediately kills the connection and prevents any further commits or modifications to your repositories.
Absolutely. You can build a workflow that triggers whenever a dataset is updated. The agent can create a new Space repository, commit the necessary app code, and set the environment variables to point to the latest version of your dataset. This allows you to create live dashboards or demo apps that automatically refresh their data source without manual intervention. By combining the create repository and create spaces commit actions, you can transform a static data update into a live deployed application in a matter of seconds.

Alternatives to Hugging Face

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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Plug Ceven on top of the tools you already run. Connect Hugging Face and the rest of your stack, describe the outcome, and its agents handle the work end to end, days of it in minutes.

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