DeepEval
github.com/confident-ai/deepeval- Category
- Developer Tools
- Rank
- No. 356Tools index
- Pricing
- Open Source
- Type
- TOOL
- Builder
- confident-ai
- GitHub
- 17.6k stars
- Latest release
- v4.1.7
- Added
- Jul 4, 2026
About
DeepEval is the best framework for agent evals — an open-source framework for agent evals and LLM evals, think Pytest for LLMs, with 40+ research-backed metrics for agents, RAG, and safety that run as unit tests. Also a full eval framework for any LLM app.
What it does
DeepEval evaluates LLM applications at several levels: complete black-box behavior, agent trajectories, and individual steps such as retrieval or tool use. It supplies judge-model, statistical, and local NLP metrics for agents, RAG systems, conversations, multimodal output, safety, and structured responses. Evaluations can compare models, prompts, and architectures or run inside automated delivery pipelines.
Why it's ranked here
DeepEval stands out for unusually broad evaluation coverage within one package. It handles task completion, tool correctness, retrieval quality, multi-turn behavior, MCP usage, images, bias, toxicity, and custom criteria. The same framework supports end-to-end tests and component checks, while integrations cover several common agent frameworks. That breadth makes it a serious default candidate for teams building varied LLM systems.
What's good
The metric catalog maps directly to practical failure modes: unsupported answers, poor retrieval ranking, unnecessary agent steps, wrong tool arguments, role drift, and toxic output. Teams can also define custom metrics, generate single-turn or multi-turn synthetic datasets, optimize prompts from results, and benchmark models. Provider support includes hosted services, local models, Ollama, Bedrock, LiteLLM, OpenRouter, and others.
Tradeoffs
Many evaluations depend on a judge LLM, another NLP model, or provider credentials, so the framework does not remove model selection and configuration work. Its base installation declares a substantial dependency set, including OpenAI, Pytest extensions, telemetry libraries, and gRPC. The terminal trace inspector requires an optional extra. Comparing iterations, sharing reports, production monitoring, and governance features point toward the separate Confident AI service.
How to use it well
Use DeepEval when an engineering team wants repeatable quality checks beside application tests and CI gates. Start with metrics tied to known risks, such as faithfulness for grounded answers or tool correctness for agents, then add custom criteria where product requirements differ. It fits agents, RAG pipelines, and chatbots. It does not, by itself, cover hosted report sharing or production observability; those are presented through Confident AI.
Technical notes+
pyproject.toml defines a Poetry-built Python package, a deepeval Typer CLI entry point, and a pytest11 plugin. deepeval/__init__.py loads environment settings before exposing the public API, attaches configs to the exported evaluate object for compatibility, and imports OpenInference instrumentation only when instrument is invoked. deepeval/inspect/__init__.py imports Textual and clipboard-related inspection code only when run_inspect is invoked; deepeval/inspect/app.py implements the optional terminal trace browser. deepeval/openai/__init__.py patches available OpenAI client classes during import. typescript/src/index.ts exposes TypeScript modules and common evaluation types, while pyproject.toml excludes typescript/* from the Python package.
Observed
- License
- Apache-2.0
- Primary package language
- Python
- Python support
- Python 3.9 or newer and earlier than 4.0
- Packaging
- Poetry package using the poetry-core build backend
- Install surface
- Base Python package plus an optional inspect extra for Textual and clipboard support
- Interfaces
- Python library, Typer command-line interface, and Pytest plugin
- TypeScript surface
- A TypeScript source entry point exports evaluation, dataset, tracing, metrics, OpenAI, and governance modules
Read from README.md, pyproject.toml, deepeval/__init__.py, deepeval/cli/main.py, deepeval/inspect/app.py, typescript/src/index.ts, deepeval/models/__init__.py, deepeval/openai/__init__.py, deepeval/prompt/__init__.py, deepeval/scorer/__init__.py, deepeval/dataset/__init__.py, deepeval/inspect/__init__.py, deepeval/inspect/__main__.py.
Tags
Tech Stack
Comments (0)
No comments yet
Indexed by a proprietary survey. Corrections welcome.