Positioning
DeepEval is an open-source LLM testing framework styled after pytest: tests live alongside code, run on every PR, and read like unit tests. The library covers G-Eval (rubric-driven LLM judge), hallucination, RAG faithfulness and contextual relevance, agentic tool-call evaluation, and multi-turn conversational metrics, plus an OWASP Top 10 for Agentic Apps red-team suite. Confident AI is the hosted layer that adds dashboards, dataset versioning, and team workflows on top of the OSS library 1.
The G-Eval rubric pattern comes from the original G-Eval paper 2; DeepEval's implementation is one of the most-used in production. The OSS project lists 500+ companies as users with Panasonic, Toshiba, Samsung, and Epic Games among named customers.
Strengths
- Pytest ergonomics. Tests run with
deepeval test run, integrate with GitHub Actions, and fail the build on regressions. Developers who already write pytest do not learn a new harness.
- Wide metric library. G-Eval, hallucination, summarization, faithfulness, contextual relevance, answer relevance, bias, toxicity, tool correctness, task completion, and multi-turn conversational quality, all callable from the same API.
- Active red-team suite. The DeepTeam module covers the OWASP Top 10 for Agentic Apps, which is a useful starting point for adversarial testing without rolling your own attack library.
Limitations
- Hosted upsell. The OSS library is genuinely useful, but the most polished workflows (dataset versioning, team dashboards) live in Confident AI. Teams need to decide where to draw the line.
- LLM-judge bias still applies. G-Eval inherits the standard judge biases (position, verbosity, self-preference) flagged in the LLM-judge literature. Calibration against humans remains the user's responsibility.
- Vendor-published benchmarks. Performance and metric-quality claims from Confident AI are vendor-published.
Best fit
Offline regression suites in CI, RAG eval with the built-in faithfulness and contextual-relevance metrics, agentic tool-call evaluation, and red-team programs that want a structured starting point with the OWASP categories.
Getting started
from deepeval import assert_test
from deepeval.metrics import AnswerRelevancyMetric, FaithfulnessMetric
from deepeval.test_case import LLMTestCase
def test_rag_answer():
case = LLMTestCase(
input="Is the lentil bowl vegan?",
actual_output=run_rag("Is the lentil bowl vegan?"),
retrieval_context=["Lentil bowl ingredients: lentils, oil, salt."],
expected_output="Yes.",
)
assert_test(case, [
AnswerRelevancyMetric(threshold=0.7),
FaithfulnessMetric(threshold=0.8),
])
Run with deepeval test run test_app.py. The CLI prints per-test scores and a summary; results push to Confident AI if the project is configured.
Pricing notes
DeepEval is free under Apache 2.0. Confident AI has hosted plans with team and enterprise tiers; pricing is sales-led for higher tiers. Confirm current rates with the vendor. The OSS library is genuinely standalone, so teams that want a CI regression suite without the hosted dashboard pay nothing for the framework itself; LLM-judge calls are billed per token to the underlying provider. Teams that need dataset versioning, team workflows, or compliance-grade reporting are the audience for the hosted SKU.
Alternatives
Consider Promptfoo for a CLI-first OSS regression suite. Consider Ragas for RAG-specific metrics. Consider Inspect AI for sandboxed agentic eval. For hosted dashboards without DeepEval, Braintrust, Langfuse, or LangSmith.
Citations and last verified
Verified 2026-05-29 against the DeepEval GitHub repository.