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What is gliner_pii?

guardrails-ai/gliner_pii — explained in plain English

Analysis updated 2026-08-02 · repo last pushed 2026-06-13

PythonAudience · developerComplexity · 3/5MaintainedSetup · moderate

In one sentence

A tool that automatically scans AI-generated text for private details like phone numbers and emails, then flags or blocks the output to prevent accidental data leaks.

Mindmap

mindmap
  root((repo))
    What it does
      Scans AI output for PII
      Flags and blocks leaks
      Customizable entity types
    Tech stack
      Python
      GLiNER model
      Guardrails framework
    Use cases
      Customer support chatbots
      AI email drafters
      Data processing tools
    Audience
      LLM app developers
      Privacy-focused teams
    Key traits
      Runs locally
      No external API calls
      Requires Guardrails

Code map

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filefunction / class

What do people build with it?

USE CASE 1

Add a safety checkpoint to a customer support chatbot to catch leaked phone numbers or addresses.

USE CASE 2

Prevent an AI email-drafting assistant from accidentally including private customer information.

USE CASE 3

Scan any AI-powered tool that processes user data to ensure sensitive details are filtered out before output.

What is it built with?

PythonGLiNERGuardrails AI

How does it compare?

guardrails-ai/gliner_pii0verflowme/learnings0verflowme/r2ai
LanguagePythonPythonPython
Last pushed2026-06-132022-06-182025-11-19
MaintenanceMaintainedDormantQuiet
Setup difficultymoderateeasymoderate
Complexity3/51/53/5
Audiencedeveloperresearcherdeveloper

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Requires installing and using the Guardrails framework, as this tool is a plugin that depends on that ecosystem to function.

The explanation does not specify a license for this repository.

So what is it?

Gliner PII is a tool that automatically scans text produced by an AI model and checks it for personally identifiable information, things like phone numbers, email addresses, or other private details that shouldn't be shared. It acts as a safety checkpoint: if the AI's output contains sensitive data, the validator flags it and can block, filter, or trigger a retry. Under the hood, the project uses a specialized AI model called GLiNER, which is designed to recognize and extract named entities from text. Rather than relying on a large general-purpose chatbot, GLiNER uses a smaller, focused model that the developers say outperforms even ChatGPT at identifying specific types of information. You plug it into the Guardrails framework, which is a broader system for adding safety rules to AI outputs, and it runs automatically whenever text is generated. The people who would use this are developers building applications powered by large language models, especially in settings where privacy matters. For example, if you are building a customer support chatbot, an AI assistant that drafts emails, or any tool that processes user data, you want to make sure the AI doesn't accidentally leak someone's phone number or home address. This tool catches that kind of mistake before the output ever reaches a real person. One notable tradeoff is that the tool depends on the Guardrails ecosystem, you need to be using their framework for it to function. The validation itself runs locally through the GLiNER model, which means it doesn't send your data to an external API for checking, an appealing property for teams handling sensitive information. You can also customize which types of entities the model looks for, tailoring the detection to your specific compliance or privacy needs.

Copy-paste prompts

Prompt 1
Help me write the Guardrails Python code to use the gliner_pii validator to scan an LLM's output for phone numbers and email addresses before returning it to the user.
Prompt 2
I want to customize the gliner_pii validator to detect specific types of personally identifiable information beyond the defaults. Show me how to configure custom entity types.
Prompt 3
Set up a Guardrails configuration that blocks AI output if the gliner_pii validator detects PII, and instead triggers a retry to generate a safe response.

Frequently asked questions

What is gliner_pii?

A tool that automatically scans AI-generated text for private details like phone numbers and emails, then flags or blocks the output to prevent accidental data leaks.

What language is gliner_pii written in?

Mainly Python. The stack also includes Python, GLiNER, Guardrails AI.

Is gliner_pii actively maintained?

Maintained — commit in last 6 months (last push 2026-06-13).

What license does gliner_pii use?

The explanation does not specify a license for this repository.

How hard is gliner_pii to set up?

Setup difficulty is rated moderate, with roughly 30min to a first successful run.

Who is gliner_pii for?

Mainly developer.

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