DeepEval vs promptfoo: pytest-style evals vs config

As of 2026-10-14, DeepEval and promptfoo are both open-source LLM evaluation tools, but they differ in approach. DeepEval documents Pytest-style assertions and a large metric library. Promptfoo documents declarative test cases run from a CLI, library or CI/CD, with red teaming built in. Neither is declared a winner here.

Quick comparison

The order is editorial and follows how closely each tool's documentation matches the query's three comparison axes, not a quality ranking.

Testing approach: pytest-style metrics vs config-driven assertions

DeepEval's docs list "Unit test LLM outputs with Pytest-style assertions" as a founding principle. The quickstart has you install it, create a test case and run it with deepeval test run.

DeepEval organizes evaluation around test cases, datasets of goldens, metrics and traces. It lists 50+ ready-to-use metrics, including LLM-as-a-judge, agent, tool-use, RAG, safety and multimodal metrics.

Promptfoo describes "Simple, declarative test cases: Define evals without writing code or working with heavy notebooks." Its workflow is to define test cases, configure prompts and providers, run the evaluation, then analyze results in a structured format or web UI.

Promptfoo also states it is language agnostic ("Use Python, Javascript, or any other language") and produces matrix views for comparing outputs across many prompts.

Workflow and CI integration

Promptfoo's docs say it can be used "as a CLI, library, or in CI/CD", and the repository description mentions "command line and CI/CD integration". It lists caching, concurrency and live reloading to speed up evaluations.

DeepEval's docs describe a CLI and say it can take part in iteration cycles with coding agents such as Claude Code, Codex and Cursor. A CI-specific setup is not stated in the cited DeepEval excerpts.

DeepEval offers three evaluation scopes: black-box end-to-end, component-level with tracing, and agent-trajectory. Both tools say evaluations run locally. DeepEval connects natively to Confident AI for dashboards and regression tracking, while promptfoo documents built-in share functionality and a web viewer.

Red-teaming

Promptfoo treats red teaming as a core surface. Its docs list "Red teaming - Scan for security vulnerabilities and compliance risks" next to evaluations, and say it produces vulnerability and risk reports.

DeepEval's docs mention safety metrics. For security testing they point to a separate adjacent "safety and security testing framework for red-teaming LLM applications". The excerpts do not name that framework, so treat red teaming as outside DeepEval's core library per the cited docs.

License and model support

DeepEval's repository reports Apache-2.0. Promptfoo's reports MIT. Pricing is not stated in either cited source, and a repository license does not imply hosted services are free.

Promptfoo lists OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama and custom API providers. DeepEval's cited excerpts say metrics, prompts and models can be customized, but do not list providers.

How we compared

We read the GitHub repository metadata and the introduction pages for both tools on 2026-10-14. We did not install or run either tool, and we did not benchmark speed, accuracy or cost.

Limitations

The sources do not state pricing for either tool, DeepEval's CI/CD setup, DeepEval's provider list, or a feature-by-feature metric comparison. Claims such as "used by OpenAI and Anthropic" and "10 million users" are promptfoo's own statements.

For general coverage see Best LLM evaluation framework: DeepEval vs promptfoo. Tool hubs: DeepEval, promptfoo, and the Agent infra category. Latest launch: promptfoo 0.124.1 adds OpenAI Decisions provider support.