Do I need an API key to use DeepEval?

For metrics using an LLM as a judge (most of them), yes, you need an API key for a model like GPT-4. Some metrics using local NLP models run fully offline.

Can I use my own custom metrics with DeepEval?

Yes. You can build custom metrics using the base class or the DAG framework, which integrate automatically with the test runner and reporting.

How does the G-Eval metric actually work under the hood?

G-Eval uses a novel chain-of-thought (CoT) prompting technique with a probability-weighted scoring rubric, allowing the LLM judge to evaluate on arbitrary criteria without fine-tuning.

What's the best way to handle evaluation cost when using expensive models as judges?

Start with cheaper models (like GPT-3.5) for routine evals. Use the synthetic dataset generation to create representative test sets once, reducing judge calls. Consider caching results.

Directory/Coding/AI Testing/DeepEval

DeepEval

DeepEval is an open-source LLM evaluation framework designed to unit test large-language model applications. It's similar to Pytest but specialized for evaluating LLM systems, including AI agents, RAG pipelines, and chatbots. The framework runs evaluations locally using metrics like G-Eval, task completion, faithfulness, and hallucination, powered by LLM-as-a-judge and other NLP models. It provides a wide variety of metrics for comprehensive testing: agentic metrics (task completion, tool correctness), RAG metrics (answer relevancy, faithfulness, contextual recall), multi-turn conversation metrics, MCP metrics, and multimodal evaluation for image generation and editing. DeepEval integrates seamlessly with popular frameworks like LangChain, OpenAI, CrewAI, and LlamaIndex, and supports CI/CD integration for automated testing.

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Sep 22, 2026

LLM evaluationPytest-basedRAG metricsAgent evaluationSynthetic dataLangChain integration

Product information

Everything worth knowing about DeepEval

The complete picture — from everyday features to the technical detail builders and IT folks dig for.

Core features

What it actually does

Comprehensive LLM Evaluation Metrics

Over 30 ready-to-use metrics covering RAG, agents, chatbots, and safety, powered by any LLM or local NLP models.

End-to-End and Component Testing

Evaluate complete LLM applications as black boxes or break down and test individual agent steps, tool calls, and retrievals.

Seamless Framework Integrations

Directly plugs into major LLM stacks like LangChain, LangGraph, CrewAI, OpenAI, and LlamaIndex via native wrappers.

Synthetic Data Generation

Automatically generate single-turn and multi-turn synthetic datasets for rigorous evaluation without manual creation.

Custom Metric Builder

Create your own deterministic or LLM-as-a-judge metrics that are automatically integrated into the evaluation ecosystem.

Technical capabilities

Pytest-Native Architecture

Framework is designed to run as part of your existing test suites using standard pytest commands and fixtures.

Local Execution Engine

All metrics run on your local machine using specified LLMs or NLP models, ensuring data privacy and control.

Must watch videos

34:22

Tutorial34:22

Evaluate LLMs in Python with DeepEval

Today we learn how to easily and professionally evaluate LLMs in Python using DeepEval. ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾ 📚 Programming Books & Merch 📚 🐍 The Python Bible Book: https://www.neuralnine.com/books/ 💻 The Algorithm Bible Book: https://www.neuralnine.com/books/ 👕 Programming Merch: https://www.neuralnine.com/shop 💼 Services 💼 💻 Freelancing & Tutoring: https://www.neuralnine.com/services 🖥️ Setup & Gear 🖥️: https://neuralnine.com/extras/ 🌐 Social Media & Contact 🌐 📱 Website: https://www.neuralnine.com/ 📷 Instagram: https://www.instagram.com/neuralnine 🐦 Twitter: https://twitter.com/neuralnine 🤵 LinkedIn: https://www.linkedin.com/company/neuralnine/ 📁 GitHub: https://github.com/NeuralNine 🎙 Discord: https://discord.gg/JU4xr8U3dm

How to Setup DeepEval for Fast, Easy, and Powerful LLM Evaluations

9:05

Tutorial9:05

How to Setup DeepEval for Fast, Easy, and Powerful LLM Evaluations

Quickly get started running evals for your LLMs with Open-Source framework DeepEval. This is a quick how-to tutorial on how-to get started using the DeepEval framework. Evaluating LLMs is critical for improving performance and guaranteeing reliability for production LLM applications. At Eigen, we run evals on all our applications to ensure we’re meeting required thresholds and find areas where we can improve. If you’re looking to build production AI applications contact us at eigen.net. Follow along the Quick Introduction in the DeepEval documentation. https://docs.confident-ai.com/docs/getting-started Make sure to create a free account to view your eval results at http://confident-ai.com and run the command “deepeval login” in your terminal to automatically view your results in the web app. Here are the commands to run if you're following along: **Setup Python Virtual Environment** python3 -m venv venv source venv/bin/activate **Install DeepEval** pip install -U deepeval **Set OpenAI API Key as Env. Variable** export OPENAI_API_KEY=yourAPIkey **Create file to run test** touch test_example.py **Paste code below to test_example.py** ____________________ from deepeval import assert_test from deepeval.test_case import LLMTestCase from deepeval.metrics import AnswerRelevancyMetric def test_answer_relevancy(): answer_relevancy_metric = AnswerRelevancyMetric(threshold=0.5) test_case = LLMTestCase( input="What if these shoes don't fit?", actual_output="We offer a 30-day full refund at no extra cost." ) assert_test(test_case, [answer_relevancy_metric]) **Command For Testing First Eval** deepeval test run test_example.py

DeepEval for RAG: Let’s Test If Your LLM Really Works as expected! 🔥

19:39

Tutorial19:39

DeepEval for RAG: Let’s Test If Your LLM Really Works as expected! 🔥

Who uses it

Real use cases, no hype

RAG Pipeline Validation

Evaluate answer relevancy, faithfulness, and context precision to ensure retrieval accuracy.

AI Agent Trajectory Analysis

Assess complete agent workflows for task completion, tool correctness, and step efficiency.

LLM Model Benchmarking

Compare performance of OpenAI, Claude, Llama, or custom models on standard benchmarks.

Prompt Engineering Optimization

Use metrics like G-Eval to iteratively refine prompts for better output quality.

Conversational AI Quality Assurance

Monitor multi-turn chats for knowledge retention, role adherence, and turn relevancy.

CI/CD Integration for LLM Apps

Automate evaluation in pipelines to catch regressions in LLM performance before deployment.

Multimodal Content Evaluation

Assess text-to-image generation quality and semantic consistency for creative AI apps.

What's great

Pytest-like syntax for LLM evals makes adoption effortless for developers.
Massive library of 30+ metrics covers RAG, agents, multi-turn, and multimodal use cases.
Runs locally, no vendor lock-in—use any LLM as a judge.
Native integrations with LangChain, OpenAI Agents, LlamaIndex, and other major frameworks.
Synthetic data generation for creating evaluation datasets automatically.

Technical strengths

G-Eval metric uses a research-backed, graph-of-thought (G-Eval) prompting strategy for robust LLM-as-a-judge scoring.
DAG metric allows building deterministic, multi-step evaluation graphs with custom logic for complex assertions.

Where it falls short

Requires a self-hosted LLM judge (e.g., GPT-4) for most metrics, adding cost and latency.
Steep learning curve for advanced metrics like DAG and agentic trajectory evaluation.
Some metrics (e.g., hallucination) rely on context quality—garbage in, garbage out.
Debugging metric failures can be opaque without clear logs from the judge model.

Technical limitations

Multimodal metrics (text-to-image) are still experimental and less mature than text-based ones.
Tracing for async Python code (e.g., async @observe decorator) can break with complex dependency graphs.

For technical folks

The deep-dive specs

FAQ

Includes technical Q&A

DeepEval provides specialized metrics (like faithfulness, relevancy) that quantitatively measure LLM output quality, which standard unit tests can't do. It's like having a smart test suite that understands natural language.

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Pricing overview

Unknown Pricing

DeepEval Pricing

DeepEval uses a unknown pricing structure. Real-time rates, custom enterprise quotas, and plan details are managed directly on their official platform.

Check Pricing on DeepEval

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