DeepEval
An open-source Python framework for unit-testing and evaluating the outputs of large language model applications.
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About DeepEval
DeepEval is an evaluation framework for LLM applications, maintained by Confident AI. It lets developers write tests that check model outputs, much like unit tests for ordinary code, so that changes to prompts, models or retrieval steps can be checked before they ship.
The repository is very active, with more than 10,000 commits and a large community. It also includes a TypeScript directory and example projects. It suits teams that want LLM quality checks inside their normal test workflow.
Strengths
- Treats LLM output checks like unit tests
- Very active development with a large user base
- Includes examples and documentation in the repository
Limitations
- Aimed at developers comfortable with Python code
- Evaluations that rely on a judge model may need API access to one
Details
- Pricing
- FreeThe framework is free and open source.
- License
- Open source, license not stated
- Developer
- Confident AI
- Platforms
- Command line
- How it runs
- Downloadable app
- Best suited for
- Developers adding automated quality tests to LLM applications
- Categories
- Developer tools, AI developer tools
- Last verified
- Added
Alternatives to DeepEval
Compare allSoftware that can replace DeepEval for an important use case, and what changes if you switch.
Promptfoo
A command-line tool for testing prompts, evaluating model output and red-teaming LLM applications and agents.
Promptfoo runs tests from the command line with a single npx command, compares prompts and models side by side, and adds red-teaming, with some reporting in a paid edition.
Evidently
An open-source framework for evaluating, testing and monitoring LLM applications, RAG systems and ML models.
Evidently is an Apache-2.0 Python framework that also covers classic ML models and checks for drift, hallucinations and data leaks alongside LLM evaluations.
Opik
An open-source platform from Comet for debugging, evaluating and monitoring LLM applications and agents.
Opik moves evaluation into a self-hostable platform with tracing and dashboards, which means running several services instead of a Python test library.
MLflow
An open-source platform for tracing, evaluating and managing LLM applications, agents and machine learning models.
MLflow brings 50+ built-in evaluation metrics and LLM judges inside a wider platform for tracing and model management, which needs a hosted server for team use.
Langfuse
An open-source platform for tracing, evaluating and managing prompts for LLM applications and AI agents.
Langfuse pairs evaluations with tracing, prompt management and annotation queues in a hosted or self-hosted platform, requiring you to instrument your application with its SDK.
Arize Phoenix
A self-hostable tool for tracing, evaluating and experimenting with LLM applications and agents.
Arize Phoenix combines evals with tracing and experiments in a self-hostable tool, but uses a source-available license rather than an OSI-approved one.
DeepEval as an alternative
Listings that name DeepEval as an alternative.
lm-evaluation-harness
EleutherAI's command-line framework for few-shot evaluation of language models on many benchmark tasks.
DeepEval treats LLM output checks as Python unit tests for applications instead of academic benchmark suites, and judge-based evaluations may need API access to a model.
Similar software
Related functionality, not necessarily a direct replacement.
LangSmith
A hosted platform for tracing, monitoring and evaluating LLM applications and AI agents.
Label Studio
An open-source data labeling tool for building training datasets and reviewing AI output.
Kiln
A free desktop workbench for building, evaluating and fine-tuning AI systems, with an open-source library.
