A Python toolkit for evaluating, monitoring, and ensuring the safety of LLM deployments in production.

The problem: 95% of enterprise AI pilots fail to deliver value — not because the models are bad, but because organizations lack the production engineering to deploy them reliably.

The solution: Concrete, runnable tools that address the most common failure modes: hallucination, bias, lack of feedback loops, and operational unreadiness.

pip install llm-production-toolkit

For ML-powered modules (hallucination detection):

pip install llm-production-toolkit[hallucination]

For everything:

pip install llm-production-toolkit[all]

Evaluate whether LLM output is grounded in source documents. Uses embedding similarity + NLI entailment for robust detection.

llm-toolkit hallucination check \
  --output "The Eiffel Tower was built in 1820" \
  --source "The Eiffel Tower was constructed from 1887 to 1889 in Paris, France"

from llm_production_toolkit.hallucination import GroundingEvaluator

evaluator = GroundingEvaluator()
result = evaluator.evaluate(
    llm_output="The Eiffel Tower was built in 1820.",
    source_context="The Eiffel Tower was constructed from 1887 to 1889.",
)
print(f"Grounding score: {result.overall_score:.2f}")
print(f"Flagged claims: {len(result.flagged_claims)}")

Test any LLM for demographic bias across gender, race, and age using controlled prompt variations.

from llm_production_toolkit.bias import BiasEvaluator

def my_llm(prompt: str) -> str:
    # Wrap any LLM — OpenAI, Anthropic, local model, etc.
    return call_your_llm(prompt)

evaluator = BiasEvaluator(llm_callable=my_llm, categories=["gender", "race"])
report = evaluator.evaluate(num_runs=3)
print(f"Overall bias score: {report.overall_bias_score:.2f}")

Collect and analyze user feedback on LLM outputs. Python API or REST server.

# Start the feedback server
llm-toolkit feedback start --port 8100

# Check metrics
llm-toolkit feedback metrics --window 24

from llm_production_toolkit.feedback import FeedbackCollector, FeedbackEntry

collector = FeedbackCollector("feedback.db")
collector.record(FeedbackEntry(
    session_id="sess-123",
    prompt_hash="abc",
    feedback_type="thumbs",
    thumbs_value=True,
))
metrics = collector.get_metrics(window_hours=24)
print(f"Satisfaction: {metrics.satisfaction_rate:.1%}")

Interactive CLI that scores your LLM deployment's operational maturity across 9 categories.

llm-toolkit readiness assess

Produces a readiness score (0-100) with category breakdowns and prioritized recommendations.

Map evaluation results to AI best-practice requirements. Works with any subset of module outputs.

llm-toolkit compliance report \
  --readiness readiness.json \
  --hallucination grounding.json

Each module is independently usable with its own CLI and Python API. Optional dependencies keep the core installation small (~5MB). ML-powered modules (hallucination, bias) add heavier dependencies only when needed.

llm-production-toolkit
├── hallucination    # Grounding evaluation (requires torch)
├── bias             # Demographic bias testing (requires textblob)
├── feedback         # Feedback collection (requires fastapi)
├── readiness        # Readiness assessment (core deps only)
└── compliance       # Compliance mapping (core deps only)

git clone https://github.com/frckeepit/llm-production-toolkit.git
cd llm-production-toolkit
pip install -e ".[dev,all]"
pytest tests/ -m "not slow"
ruff check src/ tests/

MIT