Open-source LLM evaluation framework for testing AI agents with 14+ metrics including hallucination detection, tool use correctness, and conversational quality.
A testing framework for AI applications — write tests that check if your AI's responses are accurate and helpful.
DeepEval is an open-source evaluation framework designed for comprehensive testing of LLM applications and AI agents. It provides over 14 research-backed metrics that cover the full spectrum of agent quality assessment, from basic response relevancy to complex multi-turn conversational coherence and tool use correctness. The framework is designed to work like pytest for LLMs — familiar, fast, and easy to integrate into existing development workflows.
The metric suite includes hallucination detection, answer relevancy, faithfulness, contextual precision and recall (for RAG), tool correctness (for agent tool use), conversational relevancy, knowledge retention, and more. Each metric is backed by academic research and validated against human judgment benchmarks, ensuring scores are meaningful and actionable.
DeepEval's approach to agent testing is particularly strong. The tool correctness metric evaluates whether agents call the right tools with correct parameters, essential for validating agent behavior. Conversational metrics assess multi-turn interactions for coherence, topic adherence, and knowledge retention across conversation turns.
The framework supports synthetic test data generation using an LLM to create diverse test cases from your documents, reducing the manual effort of building evaluation datasets. A built-in red-teaming module generates adversarial inputs to test agent robustness against prompt injection, bias, and toxicity.
DeepEval integrates with pytest, enabling LLM tests alongside unit tests in CI/CD pipelines. The Confident AI cloud platform provides a dashboard for tracking evaluation results over time, comparing model versions, and collaborating on evaluation datasets. DeepEval supports all major LLM providers and works with any agent framework, making it a versatile choice for systematic agent quality assurance.
Was this helpful?
Comprehensive metric suite covering hallucination, relevancy, faithfulness, tool correctness, conversational quality, and more — each validated against human judgment.
Use Case:
Tool correctness metric specifically evaluates whether agents call the right tools with correct parameters — essential for agent quality.
Use Case:
Write LLM tests using familiar pytest patterns, running agent evaluations alongside unit tests in existing CI/CD pipelines.
Use Case:
Generate diverse test datasets from documents using LLMs, reducing manual effort in building comprehensive evaluation suites.
Use Case:
Automated adversarial testing for prompt injection, bias, toxicity, and other vulnerabilities in agent systems.
Use Case:
Cloud platform for tracking evaluation results over time, comparing model versions, and collaborative dataset management.
Use Case:
Free
month
Check website for pricing
Ready to get started with DeepEval?
View Pricing Options →Comprehensive agent quality testing with multiple metrics
CI/CD integration for continuous agent evaluation
Agent tool use validation and correctness testing
Red-teaming agents for security vulnerabilities
We believe in transparent reviews. Here's what DeepEval doesn't handle well:
DeepEval is broader — it covers RAG metrics plus agent tool use, conversational quality, and red-teaming. RAGAS focuses specifically on RAG pipeline evaluation with deeper RAG-specific metrics.
Yes. DeepEval includes conversational metrics for coherence, topic adherence, and knowledge retention across multiple conversation turns.
Yes. DeepEval evaluates inputs and outputs regardless of framework. It works with LangChain, CrewAI, LlamaIndex, custom agents, and any LLM application.
DeepEval metrics are validated against human judgment benchmarks. Accuracy varies by metric and evaluator model — using stronger models (GPT-4, Claude) as evaluators produces more accurate scores.
Weekly insights on the latest AI tools, features, and trends delivered to your inbox.
People who use this tool also find these helpful
Comprehensive testing and evaluation framework for AI agent performance and reliability.
Open-source LLM application development platform for prompt engineering, evaluation, and deployment with a collaborative UI.
Comprehensive AI agent testing and evaluation platform with automated test generation and behavior validation.
AI-powered visual testing platform that uses Visual AI to automatically detect visual bugs and regressions across web and mobile applications.
Open-source LLM evaluation and testing platform by Comet for tracing, scoring, and benchmarking AI applications.
AI evaluation and guardrails platform for testing, validating, and securing LLM outputs in production applications.
See how DeepEval compares to RAGAS and other alternatives
View Full Comparison →Testing & Quality
Open-source framework for evaluating RAG pipelines and AI agents with automated metrics for faithfulness, relevancy, and context quality.
Testing & Quality
Open-source LLM testing and evaluation framework for systematically testing prompts, models, and AI agent behaviors with automated red-teaming.
Analytics & Monitoring
LLM evaluation and regression testing platform.
Analytics & Monitoring
Tracing, evaluation, and observability for LLM apps and agents.
No reviews yet. Be the first to share your experience!
Get started with DeepEval and see if it's the right fit for your needs.
Get Started →Take our 60-second quiz to get personalized tool recommendations
Find Your Perfect AI Stack →Explore 20 ready-to-deploy AI agent templates for sales, support, dev, research, and operations.
Browse Agent Templates →