Pytest-native evaluation
Write LLM tests as Python tests, run them locally or in CI/CD, and invoke them through the `deepeval` CLI.
DeepEval
Freemium
DeepEval is an open-source LLM evaluation framework for testing and benchmarking AI applications. It helps developers run pytest-native evaluations, score outputs and agent traces, and iterate on systems across text, image, audio, and voice workflows.
What is DeepEval?
DeepEval is an open-source evaluation framework for LLM applications. It provides pytest-native tests and Python-based evaluation flows that can run locally, in CI/CD, or through the deepeval CLI. Teams can evaluate black-box outputs, complete agent trajectories, and individual component decisions using configurable metrics and test cases.
The framework includes more than 50 ready-to-use metrics for areas such as hallucination, faithfulness, answer relevancy, summarization, toxicity, bias, agent task completion, tool correctness, retrieval quality, and multi-turn conversation behavior. Metrics generally return a score from 0 to 1 together with reasoning, and a test passes when its score meets the configured threshold. DeepEval also supports custom criteria through techniques including G-Eval, JevEval, DAG metrics, and self-coded metrics.
DeepEval covers text, images, and audio in the same evaluation model. It can trace agent execution so teams can inspect and grade ordered steps, tools, retrievers, and model calls. Integrations are available for agent frameworks, model providers, voice systems, vector databases, and common CI/CD platforms. For shared reports and broader evaluation operations, DeepEval connects with the Confident AI platform.
What can DeepEval do?
Research-backed metrics
Use ready-made metrics for agents, RAG, chatbots, safety, multimodal evaluation, hallucination, JSON correctness, summarization, and related criteria.
Trace and trajectory grading
Capture ordered agent execution and evaluate whole trajectories or individual actions such as tool selection and argument construction.
Custom evaluation criteria
Define natural-language criteria with G-Eval or bounded questions with JevEval, build conditional DAG metrics, or implement self-coded metrics.
Synthetic data and conversation simulation
Generate synthetic golden test cases from sources such as knowledge-base files and simulate conversations across user personas before deployment.
Broad stack integrations
Connect with frameworks including LangChain, LlamaIndex, CrewAI, LangGraph, OpenAI Agents, Pydantic AI, and others; provider, speech, vector database, and CI/CD integrations are also documented.
Use Cases
“Regression testing for LLM applications”
Add evaluation cases to a Python test suite and rerun them in a CI/CD workflow to identify quality regressions before shipping changes.
“AI agent diagnosis”
Inspect a complete agent trace, then score planning, task completion, step efficiency, tool correctness, or tool arguments to locate a failing action.
“RAG quality evaluation”
Assess retriever context relevancy, precision, and recall separately from generator answer relevancy and faithfulness.
“Chatbot and voice evaluation”
Evaluate multi-turn behavior such as knowledge retention, role adherence, conversation completeness, and relevancy; voice workflows can use speech and voice-agent integrations.
“Synthetic test-set creation”
Generate goldens from documentation or simulate user conversations and edge cases when a manually labeled dataset is not yet available.
Frequently Asked Questions
Is DeepEval a testing library or an observability dashboard?
It is primarily a code-first evaluation framework. Tests can run in Python, through pytest, or with the `deepeval` CLI; results can also be sent to Confident AI for shared reports and broader evaluation workflows.
What types of systems can it evaluate?
The documented metrics and workflows cover LLM applications, AI agents, RAG systems, chatbots, multimodal applications, and voice agents. Evaluations can target final outputs, complete traces, or individual components.
How are metric results represented?
Most metrics produce a score between 0 and 1 and include reasoning for the score. A metric is successful when its score reaches the configured threshold, which defaults to 0.5 according to the metrics documentation.
Can teams define their own evaluation criteria?
Yes. DeepEval supports custom criteria through G-Eval, JevEval, DAG metrics, and fully self-coded metrics, including metrics based on measures such as BLEU or ROUGE.
Does DeepEval have a pricing plan?
The supplied pricing URL currently returns a 404 page, so pricing, plan limits, and commercial terms cannot be confirmed from the available source material. The framework itself is described as open source.
Quick Facts
- Product type
- Open-source LLM evaluation framework
- Primary users
- Developers and engineering teams building AI applications
- Execution model
- Python scripts, pytest, `deepeval` CLI, and CI/CD runners
- Evaluation targets
- Outputs, agent trajectories, individual components, conversations, images, and audio
- Documented integrations
- Agent frameworks, model providers, speech systems, vector databases, and CI/CD platforms
- Related platform
- Confident AI for shared evaluation reports and broader evaluation workflows
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