Ragas vs DeepEval vs Giskard: Self-Hosted LLM Evaluation Frameworks 2026

Building an LLM-powered application is one thing; ensuring it produces accurate, safe, and consistent responses is another. LLM evaluation frameworks help you systematically test, measure, and improve the quality of your generative AI applications — from RAG pipelines to chatbots to autonomous agents.

In this guide, we compare three leading open-source evaluation frameworks: Ragas, DeepEval, and Giskard. Each provides a different approach to measuring and improving LLM output quality, and choosing the right one depends on your evaluation needs, your tech stack, and how you integrate testing into your development workflow.

Ragas Overview

Ragas is an evaluation framework specifically designed for Retrieval-Augmented Generation (RAG) pipelines. It provides metrics for evaluating each component of a RAG system — the retriever, the generator, and the end-to-end pipeline — using LLM-as-a-judge scoring.

Key stats:

• ⭐ 13,700+ GitHub stars
• 📅 Last updated: April 2026 (active)
• 🎯 Specialized for RAG evaluation
• Metrics include faithfulness, context precision, context recall, answer relevance

Ragas is the go-to choice if your primary workload is RAG — it provides metrics that directly measure whether your retrieved context is relevant, whether your generated answer is faithful to that context, and whether the answer actually addresses the user’s question.

DeepEval Overview

DeepEval is a general-purpose LLM evaluation framework that positions itself as the “pytest for LLMs.” It provides a wide range of evaluation metrics for any LLM application — RAG, chatbots, agents, summarization, and more — with a test-driven development workflow.

Key stats:

• ⭐ 15,000+ GitHub stars
• 📅 Last updated: April 2026 (very active)
• 🔧 Test-driven approach — write evaluation tests like unit tests
• Supports RAG, agent, chat, and general text generation evaluation

DeepEval’s strength is its test-driven philosophy. You write evaluation tests that look just like pytest tests, making it familiar to any Python developer. It supports a broader range of LLM application types than Ragas.

Giskard Overview

Giskard is an open-source evaluation and testing library for LLM agents and ML models. It focuses on automated testing, vulnerability detection, and bias analysis — going beyond accuracy to identify hallucinations, security risks, and fairness issues.

Key stats:

• ⭐ 5,300+ GitHub stars
• 📅 Last updated: April 2026 (active)
• 🛡️ Focus on security, bias, and robustness testing
• Supports LLM agents, ML models, and RAG systems

Giskard is the most security-focused of the three. It automatically scans your LLM application for vulnerabilities like prompt injection, data leakage, bias, and hallucination — making it essential for production deployments where safety matters.

Feature Comparison

Installation and Usage

Ragas

Ragas installs as a Python package and integrates with your existing RAG pipeline:

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pip install ragas

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from datasets import Dataset
from ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision

# Your RAG evaluation dataset
data = {
    "question": ["What is the capital of France?"],
    "answer": ["Paris is the capital of France."],
    "contexts": [["France is a country in Europe. Its capital is Paris."]],
    "ground_truth": ["Paris"]
}

dataset = Dataset.from_dict(data)
result = evaluate(
    dataset,
    metrics=[faithfulness, answer_relevancy, context_precision]
)
print(result)
# Returns scores for each metric (0-1 scale)

Ragas works with any LLM provider — configure your preferred model via the litellm integration or directly with OpenAI, Anthropic, or local models.

DeepEval

DeepEval uses a pytest-style workflow:

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pip install deeval

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from deeval import assert_test
from deeval.test_case import LLMTestCase
from deeval.metrics import FaithfulnessMetric, AnswerRelevancyMetric, HallucinationMetric

def test_rag_output():
    test_case = LLMTestCase(
        input="What are the key benefits of microservices?",
        actual_output="Microservices enable independent deployment, technology diversity, and fault isolation.",
        retrieval_context=[
            "Microservices architecture allows teams to deploy services independently.",
            "Different services can use different technology stacks.",
            "Failure in one service doesn't cascade to others."
        ],
        expected_output="Microservices enable independent deployment, scalability, and fault isolation."
    )
    
    faithfulness = FaithfulnessMetric(threshold=0.7)
    answer_relevancy = AnswerRelevancyMetric(threshold=0.7)
    
    assert_test(test_case, [faithfulness, answer_relevancy])

Run with deeval test — it produces a report with pass/fail results for each test case, making it easy to integrate into CI/CD pipelines.

Giskard

Giskard provides automated scanning:

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pip install giskard

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import giskard
from giskard import Model, scan

# Wrap your LLM function
def predict_fn(text):
    # Your LLM application logic here
    return llm.generate(text)

model = Model(
    model=predict_fn,
    model_type="text_generation",
    name="my_llm_app"
)

# Run automated vulnerability scan
results = scan(model, "My LLM Application")
results.to_df()  # Shows detected vulnerabilities

Giskard’s scan automatically tests for prompt injection, hallucination, bias, data leakage, and other vulnerabilities, producing a detailed report with remediation suggestions.

When to Choose Ragas

• Your primary workload is RAG — Ragas provides the most comprehensive RAG-specific metrics
• You need to evaluate retrieval quality — context precision and recall metrics tell you if your retriever is finding the right documents
• You want faithfulness scoring — ensure your generated answers are grounded in the retrieved context
• You’re iterating on RAG pipelines — Ragas makes it easy to compare different retriever configurations, chunking strategies, and prompt templates

When to Choose DeepEval

• You want test-driven development for LLMs — write evaluation tests like pytest tests and run them in CI/CD
• You evaluate multiple LLM application types — not just RAG but chatbots, agents, summarization, and classification
• You need a web dashboard — DeepEval provides a visual report of all your evaluation results
• Your team already uses pytest — the learning curve is minimal

When to Choose Giskard

• Security is your top priority — automated vulnerability scanning for prompt injection, data leakage, and bias
• You need regulatory compliance — Giskard helps document and mitigate AI risks for compliance purposes
• You evaluate LLM agents — Giskard’s agent evaluation covers multi-step reasoning and tool use
• You want automated testing — scan your model without writing individual test cases

Related Reading

For the broader ML tooling stack, see our MLflow vs ClearML vs Aim experiment tracking guide and Label Studio vs Doccano vs CVAT data labeling comparison. If you’re also managing LLM API access, our LiteLLM vs One-API gateway comparison covers the routing layer.

FAQ

What is LLM evaluation?

LLM evaluation is the systematic testing and measurement of large language model outputs to ensure they meet quality standards. Unlike traditional software testing, LLM evaluation deals with non-deterministic outputs, requiring statistical metrics, LLM-as-a-judge scoring, and human-in-the-loop validation.

What is “LLM-as-a-judge”?

LLM-as-a-judge is an evaluation technique where a strong LLM (like GPT-4) acts as an automated evaluator, scoring the outputs of other models against criteria like relevance, faithfulness, and coherence. It’s faster and cheaper than human evaluation, though it has its own biases that frameworks account for.

Can I use these frameworks with local/self-hosted models?

Yes. All three frameworks support any LLM provider. Ragas integrates with LiteLLM for provider abstraction. DeepEval and Giskard support OpenAI-compatible endpoints, meaning you can point them at Ollama, vLLM, or any local model server.

Do I need all three frameworks?

No — they serve different primary purposes. Choose Ragas for RAG evaluation, DeepEval for test-driven LLM development, or Giskard for security and robustness testing. Many teams start with one and add another as their needs grow.

How do I integrate LLM evaluation into CI/CD?

DeepEval has the strongest CI/CD integration — its pytest-style tests run naturally in any CI pipeline. Ragas and Giskard can also be scripted into CI/CD by running evaluation suites as Python scripts and checking that scores meet minimum thresholds.

What metrics should I track for a RAG pipeline?

For RAG, the key metrics are: context precision (is the retrieved context relevant?), context recall (did the retriever find all relevant information?), faithfulness (is the answer grounded in the context?), and answer relevance (does the answer address the question?). Ragas provides all four out of the box.