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deepeval

DeepEval evaluation workflow for AI agents and LLM applications

by confident-aiFree

18.9K
#3107in AI Skills
0In stores

Description

DeepEval is an agent skill that adds an end-to-end evaluation loop to AI applications, including AI agents and tool-using workflows. When a…

Highlights

  • Instrument an AI app so its runs are recorded
  • Create a committed pytest evaluation suite
  • Curate or reuse a dataset for the evals
  • Run evals and iterate on failures
  • Needs Python 3.9+ and pip install deepeval

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deepeval FAQ

What does this skill make the agent do?

It adds an evaluation loop to an AI app: instrument the app, build or reuse a dataset, write a pytest eval suite, run it and work through the failures.

How do I install it?

Run npx skills add https://github.com/confident-ai/deepeval --skill deepeval in your project.

What do I need before using it?

Python 3.9 or newer, and deepeval installed with pip in the project you want to test.

Which parts need credentials?

Metrics and synthetic dataset generation need model credentials. Confident AI reporting, hosted traces and online evals need deepeval login.

Does the eval suite stay in the project?

Yes. The skill writes a pytest suite meant to be committed with the code, so the checks run again later.

Which kinds of apps does it fit?

AI agents, tool-using workflows and LLM applications, as the skill's own description puts it.

What do the security audits show?

Gen Agent Trust Hub, Socket and Snyk all pass.

What are the best deepeval alternatives?

Popular alternatives to deepeval include agent-browser, hyperframes and ponytail. See all deepeval alternatives

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