deepeval
The LLM Evaluation Framework
DeepEval is an open-source evaluation framework specifically designed for testing and benchmarking large language model applications, similar to Pytest but specialized for LLM systems. It provides a comprehensive suite of ready-to-use metrics including G-Eval, task completion, answer relevancy, hallucination detection, and trajectory-based evaluations that assess agent decisions and tool usage. The framework integrates seamlessly with popular AI frameworks like LangChain, OpenAI, CrewAI, and Pydantic AI, enabling developers to evaluate RAG pipelines, chatbots, and AI agents either end-to-end or at the component level. DeepEval uses LLM-as-a-judge techniques and NLP models that run locally, offering flexibility in choosing evaluation models while supporting both manual instrumentation and automatic tracing.
Key Features
Use Cases
- 01Evaluating RAG pipeline accuracy with faithfulness, contextual precision, and answer relevancy metrics
- 02Testing AI agent task completion and tool usage correctness across multi-step workflows
- 03Detecting hallucinations, bias, and toxicity in chatbot responses before production deployment
- 04Comparing LLM models (e.g., OpenAI vs Claude) to optimize cost and quality trade-offs
- 05Preventing prompt drift by continuously validating outputs against expected behavior
- 06Benchmarking custom LLMs against standard datasets like MMLU, HellaSwag, and HumanEval
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deepeval — FAQ
What is DeepEval?+
DeepEval is an open-source Python framework for evaluating large language model applications, providing metrics for testing RAG pipelines, AI agents, chatbots, and other LLM systems. It works similarly to Pytest but is specialized for unit testing LLM outputs using both LLM-as-a-judge and traditional NLP approaches.
How do I install DeepEval?+
Install DeepEval via pip with 'pip install -U deepeval' on Python 3.9 or higher. Optionally run 'deepeval login' to connect to the Confident AI platform for sharable reports and dataset management.
Which AI frameworks does DeepEval work with?+
DeepEval integrates with LangChain, LangGraph, OpenAI, OpenAI Agents, Anthropic Claude, CrewAI, Pydantic AI, LlamaIndex, AWS AgentCore, Google ADK, AI SDK, and Mastra through callback handlers or wrapper clients.
Do I need API keys to use DeepEval?+
You need an API key for the LLM you choose as the evaluation judge (e.g., OPENAI_API_KEY for GPT-based metrics). Some metrics use local NLP models that don't require API keys. The Confident AI platform is optional but requires a free account.
Is DeepEval free to use?+
Yes, DeepEval is open-source under the Apache 2.0 license and free to use. The Confident AI platform offers a free tier for test result visualization and dataset management, with enterprise features available separately.
Can DeepEval evaluate complete agent trajectories?+
Yes, DeepEval captures full agent execution traces including tool calls, retrieval steps, and model decisions through automatic instrumentation or manual decoration. Trajectory-based metrics like Task Completion and Tool Correctness evaluate the entire execution path, not just final outputs.
How do I install deepeval?+
Open the source repository on GitHub and follow its README. deepeval is a skill — MCP Agents Market links you directly to the official repo.
Is deepeval free?+
deepeval is an open-source project hosted on GitHub. Check the repository for its license and any usage requirements.