Master DeepEval with our step-by-step tutorial, detailed feature walkthrough, and expert tips.
Install DeepEval via pip in a Python
9+ environment: Run 'pip install
U deepeval' in your terminal to install the framework and its dependencies Set up API keys for your chosen LLM provider: Configure OPENAI_API_KEY, ANTHROPIC_API_KEY, or other provider credentials as environment variables for metric evaluation Create your first test file using pytest structure: Write a test_example.py file with LLMTestCase objects and chosen metrics like GEval for custom criteria evaluation Run evaluation tests using deepeval CLI command: Execute 'deepeval test run test_example.py' to run your evaluation tests and see detailed metric scores and explanations Optionally set up Confident AI cloud platform integration: Run 'deepeval login' to connect with the cloud platform for team collaboration and historical test tracking
💡 Quick Start: Follow these 3 steps in order to get up and running with DeepEval quickly.
Explore the key features that make DeepEval powerful for ai memory & search workflows.
Comprehensive evaluation library including GEval for custom criteria, RAG metrics (faithfulness, relevancy, precision, recall), agent metrics (task completion, tool correctness), multimodal assessments, and safety checks (bias, toxicity, hallucination detection).
Evaluating a customer support RAG system using faithfulness metrics to ensure responses stick to knowledge base context, plus custom GEval criteria to assess helpfulness and professional tone.
Native pytest integration enables developers to write LLM tests using familiar unit testing syntax. Tests run in CI/CD pipelines with standard pytest commands, catching quality regressions automatically before production deployment.
Writing automated tests for a chatbot that validate response accuracy, tone consistency, and factual correctness using pytest assertions and custom evaluation criteria.
The @observe decorator enables granular evaluation of individual pipeline components (LLM calls, retrievers, tool usage) without code changes. Traces execution flow and applies metrics at each component level for detailed performance analysis.
Debugging an AI agent by tracing and evaluating retrieval quality, reasoning accuracy, and tool usage effectiveness separately to identify specific optimization opportunities.
Automated test case generation using state-of-the-art evolution techniques creates diverse evaluation scenarios including edge cases and adversarial examples without manual effort. Supports both single and multi-turn conversation generation.
Automatically generating hundreds of edge case scenarios for testing medical AI chatbot robustness against unusual patient questions and potential safety concerns.
MCP compatibility enables automated LLM evaluation as part of broader AI agent workflows. Integrates with agent orchestration systems for quality validation across complex multi-agent interactions and decision-making processes.
Embedding quality validation into a multi-agent workflow where content generation agents are automatically evaluated before output is passed to downstream agents for processing.
Yes, DeepEval is completely free and open-source under Apache 2.0 license. All evaluation metrics, pytest integration, tracing, and core features are included at no cost with no usage restrictions. Confident AI offers an optional cloud platform for team collaboration and advanced analytics.
DeepEval offers the most comprehensive metric library (50+) compared to competitors, with unique pytest integration familiar to developers. Unlike LangSmith's subscription model, DeepEval is completely free. It provides both end-to-end and component-level evaluation, while maintaining open-source transparency and avoiding vendor lock-in.
DeepEval requires Python programming knowledge and familiarity with pytest testing framework. It's designed for developers and technical teams who want to integrate LLM evaluation into their development workflow, not for non-technical users seeking no-code solutions.
Yes, DeepEval supports comprehensive evaluation of RAG systems, chatbots, AI agents, multi-turn conversations, multimodal applications, and virtually any LLM-powered application. It provides specialized metrics for each use case and supports both end-to-end and component-level evaluation.
DeepEval integrates with all major LLM providers (OpenAI, Anthropic, Google, Azure, Ollama) and frameworks (LangChain, LangGraph, CrewAI, Pydantic AI, LlamaIndex). You can use different models for evaluation than those being tested, and it supports custom LLM implementations.
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Tutorial updated March 2026