# DeepEval: LLM evaluation framework, README overview

- id: src_01M6VB4638YP71TX1N2H6SDMV7
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T20 · tags: none
- updated: 2026-11-07T15:48:07.367Z
- url: https://veyrul.com/sources/src_01M6VB4638YP71TX1N2H6SDMV7

The README presents DeepEval as an evaluation framework for LLM applications, run as test cases.

Source: https://github.com/confident-ai/deepeval

What it supplies: The README presents DeepEval as an evaluation framework for LLM applications, run as test cases.

Useful for: - Write test cases that evaluate an LLM app end to end.
- Share testing reports through the optional cloud platform.

First action: - Run pip install -U deepeval on Python 3.9 or later.
- Optionally run deepeval login to use the cloud platform.
- Create a test file and write a first test case.

Prerequisites and limits: The README states Python 3.9 or later.

- The platform account is optional but encouraged by the README.
- Metric details are in sections not summarised here.

Source-documented: Apache 2.0. The cloud platform is a separate service with its own terms, and some metrics may call an LLM provider you configure.

Written from the README text only. Statements are as the README gives them; nothing was installed or run.
Based on title, install, first-test and licence README text only; other sections were not read in full. Nothing was installed or run.

Source inspected: https://raw.githubusercontent.com/confident-ai/deepeval/696248fd7f8b4db1aa0d4f05cee22c3d2cc5857b/README.md ; 30739 bytes ; sha256 15fcceb3e0a857bd406870a5546bfa684659cc18ea095d3dfcf152c4c6b7b87a ; 6 exact pinned excerpts in the draft pin map

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Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=permitted training=unknown · Only the team-authored original attributed summary and links are covered. Upstream papers, tutorials, code, models, weights and media retain their separate terms; no source-text/media redistribution right is implied.

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