Has anyone ever used the Python framework "Deepeval"?

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DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that runs locally on your machine for evaluation.

Whether your LLM applications are RAG pipelines, chatbots, AI agents, implemented via LangChain or LlamaIndex, DeepEval has you covered. With it, you can easily determine the optimal models, prompts, and architecture to improve your RAG pipeline, agentic workflows, prevent prompt drifting, or even transition from OpenAI to hosting your own Deepseek R1 with confidence.

Want to talk LLM evaluation, need help picking metrics, or just to say hi? Come join our discord.

🥳 You can now share DeepEval's test results on the cloud directly on Confident AI's infrastructure

• Supports both end-to-end and component-level LLM evaluation.
• Large variety of ready-to-use LLM evaluation metrics (all with explanations) powered by ANY LLM of your choice, statistical methods, or NLP models that runs locally on your machine:
  • G-Eval
  • DAG (deep acyclic graph)
  • RAG metrics:
    • Answer Relevancy
    • Faithfulness
    • Contextual Recall
    • Contextual Precision
    • Contextual Relevancy
    • RAGAS
  • Agentic metrics:
    • Task Completion
    • Tool Correctness
  • Others:
    • Hallucination
    • Summarization
    • Bias
    • Toxicity
  • Conversational metrics:
    • Knowledge Retention
    • Conversation Completeness
    • Conversation Relevancy
    • Role Adherence
  • etc.
• Build your own custom metrics that are automatically integrated with DeepEval's ecosystem.
• Generate synthetic datasets for evaluation.
• Integrates seamlessly with ANY CI/CD environment.
• Red team your LLM application for 40+ safety vulnerabilities in a few lines of code, including:
  • Toxicity
  • Bias
  • SQL Injection
  • etc., using advanced 10+ attack enhancement strategies such as prompt injections.
• Easily benchmark ANY LLM on popular LLM benchmarks in under 10 lines of code., which includes:
  • MMLU
  • HellaSwag
  • DROP
  • BIG-Bench Hard
  • TruthfulQA
  • HumanEval
  • GSM8K
• 100% integrated with Confident AI for the full evaluation lifecycle:
  • Curate/annotate evaluation datasets on the cloud
  • Benchmark LLM app using dataset, and compare with previous iterations to experiment which models/prompts works best
  • Fine-tune metrics for custom results
  • Debug evaluation results via LLM traces
  • Monitor & evaluate LLM responses in product to improve datasets with real-world data
  • Repeat until perfection

• 🦄 LlamaIndex, to unit test RAG applications in CI/CD
• 🤗 Hugging Face, to enable real-time evaluations during LLM fine-tuning

Let's pretend your LLM application is a RAG based customer support chatbot; here's how DeepEval can help test what you've built.

Using the deepeval platform will allow you to generate sharable testing reports on the cloud. It is free, takes no additional code to setup, and we highly recommend giving it a try.

To login, run:

Follow the instructions in the CLI to create an account, copy your API key, and paste it into the CLI. All test cases will automatically be logged (find more information on data privacy here).

Create a test file:

Open test_chatbot.py and write your first test case to run an end-to-end evaluation using DeepEval, which treats your LLM app as a black-box:

Set your OPENAI_API_KEY as an environment variable (you can also evaluate using your own custom model, for more details visit this part of our docs):

And finally, run test_chatbot.py in the CLI:

Congratulations! Your test case should have passed ✅ Let's breakdown what happened.

• The variable input mimics a user input, and actual_output is a placeholder for what your application's supposed to output based on this input.
• The variable expected_output represents the ideal answer for a given input, and GEval is a research-backed metric provided by deepeval for you to evaluate your LLM output's on any custom custom with human-like accuracy.
• In this example, the metric criteria is correctness of the actual_output based on the provided expected_output.
• All metric scores range from 0 - 1, which the threshold=0.5 threshold ultimately determines if your test have passed or not.

Read our documentation for more information on more options to run end-to-end evaluation, how to use additional metrics, create your own custom metrics, and tutorials on how to integrate with other tools like LangChain and LlamaIndex.

If you wish to evaluate individual components within your LLM app, you need to run component-level evals - a powerful way to evaluate any component within an LLM system.

Simply trace "components" such as LLM calls, retrievers, tool calls, and agents within your LLM application using the @observe decorator to apply metrics on a component-level. Tracing with deepeval is non-instrusive (learn more here) and helps you avoid rewriting your codebase just for evals:

You can learn everything about component-level evaluations here.

Alternatively, you can evaluate without Pytest, which is more suited for a notebook environment.

DeepEval is extremely modular, making it easy for anyone to use any of our metrics. Continuing from the previous example:

Note that some metrics are for RAG pipelines, while others are for fine-tuning. Make sure to use our docs to pick the right one for your use case.

In DeepEval, a dataset is simply a collection of test cases. Here is how you can evaluate these in bulk:

Alternatively, although we recommend using deepeval test run, you can evaluate a dataset/test cases without using our Pytest integration:

The correct LLM evaluation lifecycle is only achievable with the DeepEval platform. It allows you to:

1. Curate/annotate evaluation datasets on the cloud
2. Benchmark LLM app using dataset, and compare with previous iterations to experiment which models/prompts works best
3. Fine-tune metrics for custom results
4. Debug evaluation results via LLM traces
5. Monitor & evaluate LLM responses in product to improve datasets with real-world data
6. Repeat until perfection

Everything on Confident AI, including how to use Confident is available here.

To begin, login from the CLI:

Follow the instructions to log in, create your account, and paste your API key into the CLI.

Now, run your test file again:

You should see a link displayed in the CLI once the test has finished running. Paste it into your browser to view the results!

Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.

Features:

• Integration with Confident AI
• Implement G-Eval
• Implement RAG metrics
• Implement Conversational metrics
• Evaluation Dataset Creation
• Red-Teaming
• DAG custom metrics
• Guardrails

Built by the founders of Confident AI. Contact [email protected] for all enquiries.

DeepEval is licensed under Apache 2.0 - see the LICENSE.md file for details.