DeepEval
What is DeepEval?
DeepEval is an LLM evaluation framework for AI engineers that turns model behavior into repeatable tests, traces, and scored runs. It supports Unit testing for LLMs, LLM-as-a-Judge, 50+ research-backed metrics, Native conversational evals, and Multi-modal by default, while tracing across LangChain, LlamaIndex, LangGraph, OpenTelemetry, and Vercel AI SDK. DeepEval is used by Google, LEGO, Visa, Adobe, Walmart, Samsung, Microsoft, and Pfizer, and it runs locally in your own environment.
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At a glance
- DeepEval is best for AI engineers who need repeatable tests for agents, RAG, and tool-using workflows.
What does DeepEval do?
DeepEval turns LLM behavior into repeatable tests, traces, and scored runs. It lets teams write Pytest-style assertions for model outputs, run 50+ ready-to-use metrics, and evaluate both end-to-end flows and component-level spans in the same workflow. The framework covers agents, chatbots, RAG pipelines, tool use, and multimodal inputs, while also supporting synthetic goldens and simulated conversations when real examples are scarce. It is local-first, so evaluations run in your own environment, and it can plug into CI/CD or a Python script without forcing a dashboard-first workflow. DeepEval says it is used by 150K+ developers, powers over 100 million daily evals, and is adopted by > 50% of Fortune 500s. Named users include Google, LEGO, Visa, Adobe, Walmart, Samsung, Microsoft, Pfizer, AXA, and Siemens. For teams that want shared observability, it integrates natively with Confident AI.
Why use DeepEval?
- Pytest-style assertions make LLM checks fit existing engineering workflows instead of forcing a separate review process.
- Local-first execution keeps evaluations in your own environment, which helps teams control where test data and traces run.
- Tracing plus component-level evals help teams isolate failures in planners, retrievers, tools, and generators instead of only scoring final outputs.
- Native conversational and multimodal evals cover multi-turn and non-text interactions without stitching together separate tools.
- Synthetic goldens and conversation simulation reduce dependence on manually collected datasets for edge cases.
Who is DeepEval for?
- AI engineers who need to test agents, RAG pipelines, and tool calls with repeatable evals.
- Data scientists who compare prompts, models, datasets, and metric scores across experiments.
- QA teams who want regression tests for AI behavior before changes reach users.
- Tech-savvy product managers who need to inspect failures and track output quality over time.
What are DeepEval's key features?
Unit testing for LLMs
Write pytest-style assertions for LLM outputs and run them in CI with GitHub Actions, GitLab CI, or Jenkins to catch regressions early.
LLM-as-a-Judge
Use model-based grading with OpenAI, Claude, Gemini, or Azure OpenAI to score outputs against custom criteria when exact rules are not enough.
50+ research-backed metrics
Evaluate outputs with 50+ research-backed metrics, including G-Eval, DAG, and QAG, to measure quality across different LLM tasks.
Native conversational evals
Test multi-turn chats with simulated conversations and conversational evals, so teams can measure dialogue quality before shipping agents.
Multi-modal by default
Assess text, image, and other multi-modal outputs in one workflow, which matters for products that combine models and media.
Tracing
Trace runs across LangChain, LlamaIndex, LangGraph, OpenTelemetry, and Vercel AI SDK to inspect failures and compare behavior over time.
Generate goldens
Create synthetic datasets and goldens from real usage, then reuse them for repeatable evals across end-to-end and component-level tests.
Local-first
Run evaluations locally with support for Ollama, LM Studio, and vLLM, keeping development fast while avoiding unnecessary cloud dependency.
What does DeepEval integrate with?
- LangChain
- LlamaIndex
- CrewAI
- OpenAI Agents
- LangGraph
- PydanticAI
- Anthropic
- Google ADK
- AgentCore
- Strands
- Vercel AI SDK
- OpenTelemetry
- OpenAI
- Claude
- Gemini
- Azure OpenAI
- AWS Bedrock
- Vertex AI
- Mistral
- LiteLLM
- Portkey
- GitHub Actions
- GitLab CI
- Jenkins
- CircleCI
- Buildkite
- Azure Pipelines
- Confident AI
- Pydantic AI
- Ollama
What are DeepEval's use cases?
AI engineers test agent behavior
AI engineers use DeepEval to test agents, RAG pipelines, and tool calls before release, using Unit testing for LLMs and Tracing to catch broken reasoning paths and failed actions early. They can then use Pytest-style assertions to turn those checks into repeatable gates in CI.
Data scientists compare experiments
Data scientists use DeepEval to compare prompts, models, datasets, and metric scores across experiments, using 50+ research-backed metrics and LLM-as-a-Judge to rank outputs consistently. They can also use Generate goldens to build stable baselines for side-by-side evaluation.
QA regression tests for AI
QA teams use DeepEval to run regression tests on AI behavior before changes reach users, using Native conversational evals and End-to-end evals to verify full user flows. With Trace, grade, and iterate, they can spot quality drops and block risky releases.
Product managers inspect output quality
Tech-savvy product managers use DeepEval to inspect failures and track output quality over time, using Tracing and Multi-modal by default to review where responses break across text and richer inputs. They can use Generate goldens to keep quality benchmarks aligned as the product evolves.
How does DeepEval work?
- Connect your first app, agent, or pipeline and start capturing runs with Tracing. DeepEval records inputs, outputs, and intermediate steps so you can inspect failures instead of guessing.
- Choose the evaluation style that fits your workflow, such as Unit testing for LLMs, Native conversational evals, or End-to-end evals. Add Pytest-style assertions to define pass-fail checks.
- Pick from 50+ research-backed metrics or use LLM-as-a-Judge for subjective quality checks. Apply them to prompts, models, datasets, or multi-step agent traces.
- Generate goldens or Synthetic datasets to create repeatable baselines, then compare new runs against them. Use Local-first workflows to iterate without depending on a remote review loop.
- Review scores, trace failures, and refine prompts or logic until the output stabilizes. Keep the same checks running over time so regressions are caught before users see them.
Frequently asked questions
What is DeepEval?
DeepEval is an LLM evaluation framework for AI engineers that turns model behavior into repeatable tests, traces, and scored runs. It supports Unit testing for LLMs, LLM-as-a-Judge, 50+ research-backed metrics, and Native conversational evals, while tracing across LangChain and LlamaIndex. DeepEval is used by Google, LEGO, Visa, and Microsoft, and it runs locally in your own environment.
What is DeepEval used for? Who is it for?
DeepEval is used for Unit testing for LLMs, LLM-as-a-Judge, and 50+ research-backed metrics. It's built for AI engineers, Data scientists, and QA teams.
Does DeepEval have an API and what does it integrate with?
DeepEval doesn't publish a public API.
Editor's read
Check whether your workflow needs shared observability in Confident AI, since that integration is the listed path for dashboards and regression tracking. If you need a dashboard-first setup from day one, verify that before adopting the local-first workflow.
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