---
name: regression-test-llm-apps-and-agents-with-metrics-tr-2a9f7e4b566e
slug: "regression-test-llm-apps-and-agents-with-metrics-traces-and-eval-suites-using-deepeval"
description: "Run repeatable eval suites against prompts, RAG pipelines, and agents so regressions surface before release."
github_stars: 14815
verification: "security_reviewed"
source: "https://github.com/confident-ai/deepeval"
author: "Confident AI"
publisher_type: "organization"
category: "Code Quality & Review"
framework: "Multi-Framework"
tool_ecosystem:
  github_repo: "confident-ai/deepeval"
  github_stars: 14815
  npm_package: "deepeval"
  npm_weekly_downloads: 1263
---

# Regression test LLM apps and agents with metrics, traces, and eval suites using DeepEval

Run repeatable eval suites against prompts, RAG pipelines, and agents so regressions surface before release.

## Prerequisites

Python or Node.js, API access to an LLM judge or compatible local models, CI optional

## Installation

Use the upstream install or setup path that matches your environment:
- pip install -U deepeval

Requirements and caveats from upstream:
- Deepeval works with **Python>=3.9+**.
- python

Basic usage or getting-started notes:
- <a href="#-quickstart">Getting Started</a> |
- **DeepEval** is a simple-to-use, open-source LLM evaluation framework, for evaluating large-language model systems. It is similar to Pytest but specialized for unit testing LLM apps. DeepEval incorporates the latest r...
- 📐 Large variety of ready-to-use LLM eval metrics (all with explanations) powered by **ANY** LLM of your choice, statistical methods, or NLP models that run **locally on your machine** covering all use cases:

- Source: https://github.com/confident-ai/deepeval
- Extracted from upstream docs: https://raw.githubusercontent.com/confident-ai/deepeval/HEAD/README.md

## Documentation

- https://docs.confident-ai.com/docs/getting-started

## Source

- [Agent Skill Exchange](https://agentskillexchange.com/skills/regression-test-llm-apps-and-agents-with-metrics-traces-and-eval-suites-using-deepeval/)


---
Source attribution: agentskillexchange
Original project: https://github.com/agentskillexchange/skills/blob/7964af467fccaf7d68a4b705bb2e26081fbdd3e8/skills/regression-test-llm-apps-and-agents-with-metrics-traces-and-eval-suites-using-deepeval/SKILL.md
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Agent Skill Exchange

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: agentskillexchange
Original project: https://github.com/agentskillexchange/skills/blob/7964af467fccaf7d68a4b705bb2e26081fbdd3e8/skills/regression-test-llm-apps-and-agents-with-metrics-traces-and-eval-suites-using-deepeval/SKILL.md
Attribution basis: Source repository record

Fields of work: Data & Analytics, Development & Engineering
Roles: Data Scientist, ML / AI Engineer, QA / Test Engineer
Specialties: API, Cross Platform, Testing
Tools: GitHub, Deepeval
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 195c0f47f3bef05dc0f75ce0018561609efeb825dbf5b36ce639575cfebaed0e