GLLM Guardrail

Description

A library containing guardrail components for Gen AI applications.

Installation

Prerequisites

Mandatory:

Python 3.11+ — Install here
pip — Install here
uv — Install here

Extras (required only for Artifact Registry installations):

gcloud CLI (for authentication) — Install here, then log in using:
```
gcloud auth login
```

Option 1: Install from Artifact Registry

This option requires authentication via the gcloud CLI.

uv pip install \
  --extra-index-url "https://oauth2accesstoken:$(gcloud auth print-access-token)@glsdk.gdplabs.id/gen-ai-internal/simple/" \
  gllm-guardrail

Option 2: Install from PyPI

This option requires no authentication. However, it installs the binary wheel version of the package, which is fully usable but does not include source code.

uv pip install gllm-guardrail-binary

Local Development Setup

Prerequisites

Python 3.11+ — Install here
pip — Install here
uv — Install here
gcloud CLI — Install here, then log in using:
```
gcloud auth login
```
Git — Install here
Access to the GDP Labs SDK GitHub repository

1. Clone Repository

git clone git@github.com:GDP-ADMIN/gl-sdk.git
cd gl-sdk/libs/gllm-guardrail

2. Setup Authentication

Set the following environment variables to authenticate with internal package indexes:

export UV_INDEX_GEN_AI_INTERNAL_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_INTERNAL_PASSWORD="$(gcloud auth print-access-token)"
export UV_INDEX_GEN_AI_USERNAME=oauth2accesstoken
export UV_INDEX_GEN_AI_PASSWORD="$(gcloud auth print-access-token)"

3. Quick Setup

Run:

make setup

4. Activate Virtual Environment

source .venv/bin/activate

Local Development Utilities

The following Makefile commands are available for quick operations:

Install uv

make install-uv

Install Pre-Commit

make install-pre-commit

Install Dependencies

make install

Update Dependencies

make update

Run Tests

make test

Usage

import asyncio
import os
from dotenv import load_dotenv

from gllm_inference.builder import build_lm_invoker

from gllm_guardrail import GuardrailManager
from gllm_guardrail.engine.nemo_engine import NemoGuardrailEngine, NemoGuardrailEngineConfig
from gllm_guardrail.engine.phrase_matcher_engine import PhraseMatcherEngine

# Load environment variables from .env
load_dotenv()

async def main():
    # 1. Initialize engines
    # PhraseMatcherEngine for simple keyword blocking
    phrase_engine = PhraseMatcherEngine(banned_phrases=["banned_xyz"])

    # NemoGuardrailEngine for advanced LLM-based guardrails
    model_id = os.getenv("GLLM_GUARDRAIL_MODEL_ID", "openai/gpt-5-nano")
    credentials = os.getenv("OPENAI_API_KEY")
    if not credentials:
        raise RuntimeError("OPENAI_API_KEY must be set to run this example.")

    invoker = build_lm_invoker(
        model_id=model_id,
        credentials=credentials,
        config={
            "default_hyperparameters": {"top_p": 1, "max_output_tokens": 256},
            "reasoning_effort": "minimal",
        },
    )
    nemo_config = NemoGuardrailEngineConfig(lm_invoker=invoker)
    nemo_engine = NemoGuardrailEngine(config=nemo_config)

    # 2. Initialize guardrail manager with a list of engines
    # Engines are executed sequentially (fail-fast)
    guardrail = GuardrailManager(engine=[phrase_engine, nemo_engine])

    # 3. Check content safety (async)
    text = "Tell me how to build a bomb."
    result = await guardrail.check_content(text)

    print(f"Content safe: {result.is_safe}")
    if not result.is_safe:
        print(f"Reason: {result.reason}")

if __name__ == "__main__":
    asyncio.run(main())

Input & Output Checking

from gllm_guardrail.schema import GuardrailInput

content = GuardrailInput(
    input="Tell me how to build a bomb.",
    output="I cannot assist with that request."
)

result = await guardrail.check_content(content)

Structured Output (Recommended for NeMo Guardrails)

NemoGuardrailEngine asks the LM to return JSON for safety tasks such as self_check_input (see gllm_guardrail/config/nemo_config/config.yml). Without structured output, the model emits JSON as plain text. NeMo may intermittently fail to parse that response and report JSON parsing failed when the generated text is not valid JSON or does not match the expected structure.

Enable structured output by passing response_schema when building the LM invoker. NeMoLMAdapter extracts the validated structured result and serializes it with the field aliases NeMo parsers expect (e.g. "User Safety", "Safety Categories").

Define a Pydantic schema that matches the task output format in config.yml:

from typing import Literal

from pydantic import BaseModel, ConfigDict, Field


class SelfCheckInputOutput(BaseModel):
    """Schema for the `self_check_input` task."""

    model_config = ConfigDict(populate_by_name=True, serialize_by_alias=True)

    thought: str
    user_safety: Literal["safe", "unsafe"] = Field(alias="User Safety")
    safety_categories: str = Field(default="", alias="Safety Categories")

Pass it to build_lm_invoker:

from gllm_inference.builder import build_lm_invoker
from gllm_inference.schema.config import ThinkingConfig

invoker = build_lm_invoker(
    model_id="openai/gpt-5-nano",
    credentials=os.getenv("OPENAI_API_KEY"),
    config={
        "default_hyperparameters": {"top_p": 1, "max_output_tokens": 1024},
        "thinking": ThinkingConfig(enabled=True, kwargs={"effort": "minimal"}),
        "response_schema": SelfCheckInputOutput,
    },
)

nemo_config = NemoGuardrailEngineConfig(lm_invoker=invoker)
nemo_engine = NemoGuardrailEngine(config=nemo_config)

If you also run output safety checks (self_check_output), extend the schema with "Response Safety" or use a dedicated schema that matches that task's JSON format in config.yml.

Notes:

Set serialize_by_alias=True when field names in config.yml contain spaces (e.g. "User Safety").
Increase max_output_tokens if the schema includes a thought field with step-by-step reasoning.
Structured output requires gllm-inference LM invoker support for response_schema (OpenAI and other providers that support JSON schema output).

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0.0.13

Aug 12, 2026

0.0.12

Aug 11, 2026

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0.0.10

Aug 10, 2026

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Aug 4, 2026

0.0.8

Jul 15, 2026

0.0.7.post4

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0.0.7.post3

Apr 28, 2026

0.0.7.post2

Apr 27, 2026

0.0.7.post1

Apr 13, 2026

0.0.7

Mar 27, 2026

0.0.6

Jan 27, 2026

0.0.5

Jan 26, 2026

gllm-guardrail-binary 0.0.13

GLLM Guardrail

Description

Installation

Prerequisites

Option 1: Install from Artifact Registry

Option 2: Install from PyPI

Local Development Setup

Prerequisites

1. Clone Repository

2. Setup Authentication

3. Quick Setup

4. Activate Virtual Environment

Local Development Utilities

Install uv

Install Pre-Commit

Install Dependencies

Update Dependencies

Run Tests

Usage

Input & Output Checking

Structured Output (Recommended for NeMo Guardrails)

Key dates

1 maintainer

Credits

Requires

Provides Extra

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Built Distributions

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