--- title: "human-eval vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openai-human-eval-vs-rlancemartin-auto-evaluator" tools: ["openai-human-eval", "rlancemartin-auto-evaluator"] --- # human-eval vs auto-evaluator *GraphCanon updated Aug 8, 2026* ## Verdict Pick human-eval if human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests; pick auto-evaluator if auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. [human-eval](https://github.com/openai/human-eval) reports 3.3k GitHub stars, 452 forks, and 44 open issues, last pushed Jan 17, 2025. [auto-evaluator](https://autoevaluator.langchain.com/) has 1.1k stars, 92 forks, and 3 open issues, last pushed May 10, 2023. Figures are from public GitHub metadata via [human-eval's repository](https://github.com/openai/human-eval) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [human-eval](/tools/openai-human-eval.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Evaluating Large Language Models Trained on Code | A lightweight evaluation tool for question-answering using Langchain | | Stars | 3,331 | 1,105 | | Forks | 452 | 92 | | Open issues | 44 | 3 | | Language | Python | Python | | Adopt for | human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. | Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. | | Persona | - | - | | Runtime | - | - | | License | MIT | - | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [human-eval](/tools/openai-human-eval.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Days since push | 564d | 1186d | | Open issues (now) | 44 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/openai-human-eval/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Shared compatibility - **Python**: [human-eval](/tools/openai-human-eval.md) - Python runtime; [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - Python runtime ## Decision facts: human-eval - **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. - **Adopt for:** human-eval is a tool designed for evaluating large language models trained specifically on code through human-written tests. ## Decision facts: auto-evaluator - **Adopt for:** Auto-evaluator is a Python-based tool designed for evaluating LLM QA chains with the capability to auto-generate question-answer pairs from user-provided documents and evaluate answers using configurations chosen via UI. ## Choose when ### Choose human-eval if… - This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process. - Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.. - Tags unique to human-eval: code evaluation, large language models, python. - 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. ### Choose auto-evaluator if… - Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm. - Use when you need a lightweight solution for testing question-answering capabilities of Langchain models. - Leaner open-issue backlog (3). ## When NOT to use human-eval - 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. ## When NOT to use auto-evaluator - Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. - If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ## Common questions ### What is the difference between human-eval and auto-evaluator? human-eval: Evaluating Large Language Models Trained on Code. auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. See the comparison table for live GitHub stats and shared categories. ### When should I choose human-eval over auto-evaluator? Choose human-eval over auto-evaluator when This evaluation framework must be installed and set up in your own environment, ensuring full control over the testing process; Pricing: The software is available under an MIT license for free use, yet advanced features or services beyond its core functionality might incur costs.; Tags unique to human-eval: code evaluation, large language models, python; 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. ### When should I choose auto-evaluator over human-eval? Choose auto-evaluator over human-eval when Tags unique to auto-evaluator: evaluation, gpt-3.5-turbo, langchain, llm; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; Leaner open-issue backlog (3). ### When should I avoid human-eval? 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. ### When should I avoid auto-evaluator? Avoid using this tool when you do not have access to an OpenAI API key providing access to GPT-4, as it uses that by default for optimal settings. If you are looking for a tool that does not require you to input documents for question generation and prefer a more customized prompt approach rather than the auto-generation feature. ### Is human-eval or auto-evaluator more popular on GitHub? human-eval has more GitHub stars (3,331 vs 1,105). Stars measure visibility, not whether either tool fits your constraints. ### Are human-eval and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to human-eval or auto-evaluator? GraphCanon lists graph-backed alternatives at [human-eval alternatives](/tools/openai-human-eval/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([human-eval markdown twin](/tools/openai-human-eval/alternatives.md), [auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/openai-human-eval-vs-rlancemartin-auto-evaluator.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, human-eval or auto-evaluator? human-eval: Dormant. auto-evaluator: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for human-eval and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [human-eval trust report](/tools/openai-human-eval/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openai-human-eval`](/api/graphcanon/graph?tool=openai-human-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_