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human-eval

openai/human-eval

Evaluating Large Language Models Trained on Code

GraphCanon updated 2w · GitHub synced 2w · 25 views this month

3.3k stars452 forksLast push 1y Python MIT

Decision brief

human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests.

Good fit when

  • When you need to evaluate the performance of AI systems that have been trained exclusively on code datasets, as it allows testing via human-created benchmarks relevant only to code-based models.
  • For researchers and developers aiming to assess how well a language model understands programming syntax and semantics.

Avoid when

  • If you are interested in evaluating general natural language processing tasks without coding context, as human-eval is tailored specifically for assessing code-focused AI systems.
  • When the required Python version is below 3.7; this tool mandates at least Python 3.7 to ensure compatibility with its dependencies.
Hosting:
self hosted - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process.
Pricing:
freemium - The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.

Observed Jul 17, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Dormant (564d since push)
As of 2w
Provenance
Not a fork · Organization account
As of 2w
Security (OSV)
No criticals
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Backing

Company context for OpenAI. Display-only - separate from trust and ranking.

Company
OpenAI·GitHub org profile·1mo
Employees
4,500·Wikidata (P1128 employees)·1mo
Funding
$13,000,000,000 (2023-01)·GraphCanon curated seed (public press)·1mo
Commercial model
OSS + managed cloud·GraphCanon curated seed·1mo

Install

pip install human-eval
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

Repository for evaluating large language models trained on code using human-written tests.

Capability facts

Languages
python

Source: github.language · Aug 5, 2026

Categories

Graph entities

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Aug 5, 2026)

Make sure to use python 3.7 or later:
Source link

Tags

README

Installation

Make sure to use python 3.7 or later:

$ conda create -n codex python=3.7
$ conda activate codex

Check out and install this repository:

$ git clone https://github.com/openai/human-eval
$ pip install -e human-eval

For agents

This page has a .md twin and JSON over the API.

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