Is DeepEval free and open source?

Yes, DeepEval is completely free and open-source under the Apache-2.0 license. You can use it commercially without restrictions.

Do I need pytest to use DeepEval?

While DeepEval includes native pytest integration for CI/CD workflows, you can use it standalone without pytest if preferred.

What are LLM-as-a-judge metrics in DeepEval?

These are evaluation metrics that use an LLM to score outputs against criteria you define (correctness, relevance, etc.), producing pass/fail thresholds for tests.

Can DeepEval evaluate RAG and agent systems?

Yes, DeepEval is specifically designed for RAG and agent evaluation, with built-in metrics for retrieval quality, answer relevance, and component-level tracing.

How does DeepEval compare to other evaluation frameworks?

DeepEval focuses on pytest-style developer experience, 30+ built-in metrics, and strong framework integrations (LlamaIndex, LangChain) compared to alternatives.

Can I use DeepEval without Confident AI?

Yes, DeepEval is fully functional as a standalone open-source framework. Confident AI is the optional commercial platform for team collaboration and cloud features.

DeepEval

DeepEval is an open-source Python framework for LLM evaluation with pytest-style unit testing, 30+ LLM-as-judge metrics, multi-modal support, and integrations for RAG, agents, and fine-tuning workflows.

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About DeepEval

DeepEval is an open-source Python framework for evaluating Large Language Model (LLM) applications. Developed by Confident AI (YC-backed), it provides a developer-friendly, pytest-style approach to LLM testing that integrates seamlessly into existing CI/CD workflows. The framework enables teams to validate LLM outputs consistently and catch regressions before deployment.

DeepEval supports comprehensive evaluation through 30+ LLM-as-a-judge metrics (including G-Eval), customizable scoring criteria, and both end-to-end and component-level evaluation via tracing. It handles multi-modal inputs including text, images, and audio, making it suitable for diverse AI applications.

The framework is particularly strong for RAG pipeline evaluation, agent workflow testing, and chatbot quality assurance. It offers synthetic dataset generation capabilities when labeled test data is scarce, and integrates with popular AI frameworks including LlamaIndex, LangChain, and Hugging Face.

DeepEval is Apache-2.0 licensed, completely free to use, and backed by an active open-source community. It serves as the foundation for Confident AI's commercial platform while remaining fully functional as a standalone tool.

Key Features

Pytest Integration: Write LLM unit tests that fit naturally into existing Python testing workflows.
LLM-as-Judge Metrics: 30+ built-in metrics including G-Eval, with support for custom evaluation criteria.
End-to-End Evaluation: Benchmark complete system behavior with datasets and golden test cases.
Component-Level Tracing: Evaluate and debug individual pipeline components using execution traces.
Multi-Modal Support: Evaluate text, images, and audio inputs with unified test cases.
Synthetic Data Generation: Create evaluation datasets using evolution techniques when test data is limited.

Pricing

DeepEval is completely free and open-source under the Apache-2.0 license. No paid tiers or feature restrictions. Commercial support available through Confident AI platform.

Pricing last updated: February 11, 2026 at 10:03 AM

Use Cases

Add automated LLM unit tests to CI/CD to prevent prompt/model regressions
Evaluate RAG applications for answer relevance, correctness, and faithfulness
Benchmark agent workflows via component-level tracing and scoring
Run evaluations during fine-tuning to monitor quality changes
Generate synthetic test datasets when labeled data is unavailable
Compare multiple LLM providers and prompts with standardized metrics

Pros & Cons

Pros:

Familiar pytest-style workflow reduces learning curve for Python developers
Comprehensive 30+ metric library with customization options
Supports component-level evaluation for complex agent pipelines
Completely free and open-source with Apache-2.0 license
Strong integrations with LlamaIndex, LangChain, and Hugging Face
Active development and community support

Cons:

Requires Python knowledge and development environment setup
Many metrics require LLM provider API keys for judge-based scoring
Self-hosted nature requires infrastructure management
Commercial platform features (team collaboration, cloud UI) require Confident AI