validate-llm 0.1.1

A composable validation framework for LLM inputs and outputs

Composable validation guardrails for LLM pipelines — accuracy, relevancy, toxicity, privacy, and bias checks in one pipeline.

Documentation →

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Install

pip install validate-llm

Requires Python 3.11+. Optional extras:

pip install "validate-llm[demo]"   # FastAPI demo server + web UI
pip install "validate-llm[test]"   # pytest + datasets
pip install "validate-llm[dev]"    # everything

Quick start

from llm_validation_framework import ValidationFramework, LLMProvider, Pipe
from llm_validation_framework import ToxicityAgent, PrivacyAgent, AccuracyAgent
from llm_validation_framework.config_loader import load_api_key

llm = LLMProvider(provider="anthropic", model="claude-haiku-4-5-20251001", key=load_api_key())

vf = ValidationFramework(
    llm=llm,
    input_guardrail=Pipe(steps=[ToxicityAgent()], verbose=False),
    output_guardrail=Pipe(steps=[ToxicityAgent(), PrivacyAgent(), AccuracyAgent()], verbose=False),
)

result = vf.validate("What is the Pacific Ocean?")
print(result["status"], result["score"])  # PASS 0.87

validate() returns a structured dict with status, score, and per-agent results for both the input and output guardrails. See the docs for the full schema.

Agents

Agent	What it does	Needs API key
ToxicityAgent	Three-layer check: profanity filter → toxicity model → semantic similarity	No
PrivacyAgent	Regex scan for SSN, credit cards, API keys; optional system prompt leakage detection	No
AccuracyAgent	LLM-as-a-judge factual accuracy + relevancy, with optional RAG grounding	Yes
RelevancyAgent	LLM-as-a-judge check that the answer addresses the question	Yes
BiasAgent	LLM-as-a-judge scan for stereotypes and discriminatory language	Yes

ToxicityAgent and PrivacyAgent run fully locally with no external calls.

Config

export ANTHROPIC_API_KEY=your-key

Or create a config.ini at the repo root (gitignored):

[ANTHROPIC]
API_KEY=your-key

Supported providers follow litellm's naming.

RAG grounding

Pass a retriever to AccuracyAgent to ground factual checks against your own corpus:

from llm_validation_framework import AccuracyAgent, RAGProvider

accuracy = AccuracyAgent(rag=RAGProvider(your_vectorstore.as_retriever()))

See the RAG Integration guide for a full walkthrough.

Demo

The demo is a FastAPI backend + static web UI.

# Terminal 1
uvicorn demo.api_server:app --host 127.0.0.1 --port 5050

# Terminal 2
python demo/serve_ui.py

Open http://127.0.0.1:8000.

Contributors

• Hitha Shri Nagaruru
• James Wu
• Lewis Lui
• Thomas Yeoh

License

MIT — see LICENSE