DeepEval

Free

The LLM Evaluation Framework

LLM EvalsFreeFree tier

Type

Open Source

Visit Website GitHub (38 stars)

About DeepEval

DeepEval is an open-source LLM evaluation framework designed for unit testing large language model systems, similar to Pytest. It incorporates the latest research to run evaluations using metrics such as G-Eval, task completion, answer relevancy, hallucination, and more, leveraging LLM-as-a-judge and other NLP models that run locally. The framework supports a wide variety of use cases including AI agents, RAG pipelines, and chatbots, and integrates with popular tools like LangChain and OpenAI. Users can evaluate custom criteria, compare models, prevent prompt drift, and transition between LLM providers with confidence. The project also offers Confident AI, a platform for managing eval results, generating reports, and collaborating.

Key Features

G-Eval: a research-backed LLM-as-a-judge metric for custom criteria

DAG: graph-based deterministic LLM-as-a-judge metric builder

Agentic metrics: Task Completion, Tool Correctness, Goal Accuracy, Step Efficiency, Plan Adherence, Plan Quality, Tool Use, Argument Correctness

RAG metrics: Answer Relevancy, Faithfulness, Contextual Recall, Contextual Precision

LLM-as-a-judge evaluation using any LLM of your choice

NLP models that run locally on your machine

Integrates with LangChain, OpenAI, and other LLM frameworks

Similar to Pytest for unit testing LLM apps

Open source and free to use

Pros & Cons

Pros

Open source and completely free
Wide variety of ready-to-use evaluation metrics covering custom, agentic, and RAG use cases
Supports local execution of NLP models, reducing API costs and latency
Research-backed metrics like G-Eval for higher accuracy
Integrates seamlessly with popular LLM frameworks and tools
Allows users to determine optimal models, prompts, and architecture for their AI quality

Cons

Requires programming knowledge (Python) to set up and use
Some metrics rely on LLM API calls, which may incur costs even with the framework being free
Documentation and community support may be limited compared to established testing frameworks

Best For

Evaluating RAG pipelines for faithfulness and answer relevanceTesting AI agents for task completion and tool usage correctnessComparing different LLM models, prompts, and architecturesPreventing prompt drift in production LLM applicationsTransitioning between LLM providers (e.g., OpenAI to Claude) with confidenceRunning automated unit tests for LLM systems in CI/CD pipelines

Alternatives to DeepEval

Galileo AI

AI evaluation platform for hallucination detection and quality

Patronus AI

Automated AI evaluation and red-teaming platform

Weights & Biases Weave

Trace, evaluate, and improve your LLM applications

FAQ

What is DeepEval?

DeepEval is an open-source LLM evaluation framework for unit testing large language model systems, similar to Pytest.

What metrics does DeepEval support?

DeepEval supports a wide range of metrics including G-Eval, task completion, answer relevancy, faithfulness, contextual recall/precision, and many agentic metrics.

Does DeepEval require an internet connection?

Some metrics use local NLP models, while others rely on LLM-as-a-judge which may require an API call to an external LLM.

Can I use DeepEval with my existing LLM framework?

Yes, DeepEval integrates with popular frameworks like LangChain and OpenAI, and can be adapted to any LLM system.

Is there a paid version or platform for DeepEval?

DeepEval itself is free and open source. Confident AI is a complementary platform for managing evaluation data and generating reports, which may have its own pricing.