AI Agent Knowledge Base

DeepEval models each LLM interaction as a test case (LLMTestCase or ConversationalTestCase), similar to a unit test in traditional software development. Each test case contains an input, the actual output from your LLM application, optional expected output, and retrieval context for RAG systems.

Metrics are applied to test cases with configurable thresholds. If the metric score falls below the threshold, the test fails — just like a failing assertion in Pytest. This integrates directly into CI/CD pipelines to catch regressions on every push.

• Pytest integration — Write LLM tests with familiar @pytest.mark.parametrize and assert_test⁵⁾
• Pre-built metrics — Faithfulness, relevancy, hallucination, toxicity, bias, and more
• Custom metrics (G-Eval) — LLM-as-judge with custom criteria and evaluation steps
• Dataset management — Golden datasets with expected inputs/outputs for batch testing
• Conversation testing — Multi-turn evaluation via ConversationalTestCase
• CI/CD ready — Run on every push to detect regressions automatically
• Cloud collaboration — Optional Confident AI platform for team-wide testing

# Install DeepEval
# pip install deepeval
 
from deepeval import assert_test, evaluate
from deepeval.test_case import LLMTestCase
from deepeval.metrics import (
    AnswerRelevancyMetric,
    FaithfulnessMetric,
    HallucinationMetric,
    GEval
)
 
# Define a test case for a RAG application
def test_rag_response():
    test_case = LLMTestCase(
        input="What is the refund policy?",
        actual_output=your_rag_app("What is the refund policy?"),
        expected_output="Full refund within 30 days of purchase",
        retrieval_context=[
            "Our refund policy allows full refunds within 30 days."
        ]
    )
 
    # Apply metrics with thresholds
    relevancy = AnswerRelevancyMetric(threshold=0.7)
    faithfulness = FaithfulnessMetric(threshold=0.8)
 
    assert_test(test_case, [relevancy, faithfulness])
 
# Custom metric with G-Eval (LLM-as-judge)
correctness = GEval(
    name="Correctness",
    criteria="The response should be factually correct and complete",
    evaluation_steps=[
        "Check if the response contains accurate information",
        "Verify all key points from expected output are covered",
        "Ensure no fabricated information is present"
    ],
    threshold=0.8
)
 
# Batch evaluation with datasets
from deepeval.dataset import EvaluationDataset
 
dataset = EvaluationDataset(test_cases=[
    LLMTestCase(input="Q1", actual_output="A1"),
    LLMTestCase(input="Q2", actual_output="A2"),
])
results = evaluate(dataset, [relevancy, correctness])

%%{init: {'theme': 'dark'}}%%
graph TB
    Dev([Developer]) -->|Write Tests| Tests[Pytest Test Suite]
    Tests -->|LLMTestCase| TC[Test Cases]
    TC -->|input + actual_output| App[Your LLM App]
    TC -->|expected_output| Golden[Golden Dataset]
    TC -->|retrieval_context| RAG[RAG Pipeline]
    Tests -->|Metrics| Metrics{Metric Engine}
    Metrics -->|Pre-built| PreBuilt[Relevancy / Faithfulness / Hallucination]
    Metrics -->|Custom| GEval[G-Eval LLM Judge]
    Metrics -->|Score + Reason| Results[Test Results]
    Results -->|Pass / Fail| CI[CI/CD Pipeline]
    Results -->|Dashboard| Cloud[Confident AI Platform]
    CI -->|On Push| GHA[GitHub Actions]
    GHA -->|Regression Alert| Team([Development Team])

• Promptfoo — LLM Evaluation and Red Teaming
• Arize Phoenix — AI Observability
• Guidance — Structured Generation Language