--- title: "auto-evaluator vs qa_metrics" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rlancemartin-auto-evaluator-vs-zli12321-qa-metrics" tools: ["rlancemartin-auto-evaluator", "zli12321-qa-metrics"] --- # auto-evaluator vs qa_metrics *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick qa_metrics if qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from. [auto-evaluator](https://autoevaluator.langchain.com/) reports 1.1k GitHub stars, 92 forks, and 3 open issues, last pushed May 10, 2023. [qa_metrics](https://github.com/zli12321/qa_metrics) has 64 stars, 6 forks, and 0 open issues, last pushed Jul 18, 2025. Figures are from public GitHub metadata via [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator) and [qa_metrics's repository](https://github.com/zli12321/qa_metrics). | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [qa_metrics](/tools/zli12321-qa-metrics.md) | | --- | --- | --- | | Tagline | A lightweight evaluation tool for question-answering using Langchain | A Python package for basic QA evaluations of large language models. | | Stars | 1,102 | 64 | | Forks | 92 | 6 | | Open issues | 3 | 0 | | Language | Python | Python | | 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. | qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic. | | Persona | - | - | | Runtime | - | - | | License | - | MIT License allows for free use and distribution with attribution required by retaining the copyright notice and license text in any redistribution. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | [qa_metrics](/tools/zli12321-qa-metrics.md) | | --- | --- | --- | | Days since push | 1216d | 417d | | Open issues (now) | 3 | 0 | | Stars delta | -3 (30d) | +2 (30d) | | Full report | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | [trust report](/tools/zli12321-qa-metrics/trust.md) | ## Shared compatibility - **Python**: [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - Python runtime; [qa_metrics](/tools/zli12321-qa-metrics.md) - Python runtime ## 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. ## Decision facts: qa_metrics - **Adopt for:** qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic. - **License detail:** MIT License allows for free use and distribution with attribution required by retaining the copyright notice and license text in any redistribution. ## Choose when ### 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. - More GitHub stars (1.1k vs 64) - visibility, not fit. ### Choose qa_metrics if… - Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test. - When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score. - More recently updated (last pushed Jul 18, 2025). ## 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. ## When NOT to use qa_metrics - Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set. - Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily. ## Common questions ### What is the difference between auto-evaluator and qa_metrics? auto-evaluator: A lightweight evaluation tool for question-answering using Langchain. qa_metrics: A Python package for basic QA evaluations of large language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-evaluator over qa_metrics? Choose auto-evaluator over qa_metrics 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; More GitHub stars (1.1k vs 64) - visibility, not fit. ### When should I choose qa_metrics over auto-evaluator? Choose qa_metrics over auto-evaluator when Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test; When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score; More recently updated (last pushed Jul 18, 2025). ### 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. ### When should I avoid qa_metrics? Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set. Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily. ### Is auto-evaluator or qa_metrics more popular on GitHub? auto-evaluator has more GitHub stars (1,102 vs 64). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-evaluator and qa_metrics open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to auto-evaluator or qa_metrics? GraphCanon lists graph-backed alternatives at [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) and [qa_metrics alternatives](/tools/zli12321-qa-metrics/alternatives) ([auto-evaluator markdown twin](/tools/rlancemartin-auto-evaluator/alternatives.md), [qa_metrics markdown twin](/tools/zli12321-qa-metrics/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/rlancemartin-auto-evaluator-vs-zli12321-qa-metrics.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-evaluator or qa_metrics? auto-evaluator: Dormant. qa_metrics: 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 auto-evaluator and qa_metrics? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust); [qa_metrics trust report](/tools/zli12321-qa-metrics/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rlancemartin-auto-evaluator`](/api/graphcanon/graph?tool=rlancemartin-auto-evaluator) - 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/_