--- title: "every_eval_ever vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evaleval-every-eval-ever-vs-rlancemartin-auto-evaluator" tools: ["evaleval-every-eval-ever", "rlancemartin-auto-evaluator"] --- # every_eval_ever vs auto-evaluator *GraphCanon updated Sep 20, 2026* ## Verdict Pick every_eval_ever if every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results; 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. [every_eval_ever](https://evalevalai.com/projects/every-eval-ever/) reports 111 GitHub stars, 49 forks, and 27 open issues, last pushed Sep 7, 2026. [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 [every_eval_ever's repository](https://github.com/evaleval/every_eval_ever) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Shared schema and crowdsourced eval database | A lightweight evaluation tool for question-answering using Langchain | | Stars | 111 | 1,102 | | Forks | 49 | 92 | | Open issues | 27 | 3 | | Language | Python | Python | | Adopt for | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | 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._ | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 1216d | | Open issues (now) | 27 | 3 | | Stars delta | +9 (30d) | -3 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/evaleval-every-eval-ever/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Decision facts: every_eval_ever - **Pricing:** freemium - Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database. - **Requirements:** Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip. - **Adopt for:** Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. ## 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 every_eval_ever if… - Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.. - Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.. - Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra. - Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema. ### 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 111) - visibility, not fit. ## When NOT to use every_eval_ever - Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. - If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable. ## 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 every_eval_ever and auto-evaluator? every_eval_ever: Shared schema and crowdsourced eval database. 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 every_eval_ever over auto-evaluator? Choose every_eval_ever over auto-evaluator when Pricing: Every Eval Ever is open-source under the MIT license, allowing free use and modification. No direct costs are associated with using the schema or contributing to the database.; Requirements: Min 2 GB RAM; To utilize all features, you need to install specific converter dependencies via pip.; Tags unique to every_eval_ever: agent-evaluation, ai-evaluation, evaluations, infra; Use Every Eval Ever if you need to compare evaluation results from different frameworks in a consistent manner, ensuring results can be easily reproduced or reused as they conform to a defined schema. ### When should I choose auto-evaluator over every_eval_ever? Choose auto-evaluator over every_eval_ever 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 111) - visibility, not fit. ### When should I avoid every_eval_ever? Avoid Every Eval Ever if you require real-time updates on evaluation results, as the database relies on contributions from a community to maintain and update its dataset. If your project needs to integrate evaluation outcomes without an explicit need for extensive metadata validation or standardization, this tool might be less suitable. ### 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 every_eval_ever or auto-evaluator more popular on GitHub? auto-evaluator has more GitHub stars (1,102 vs 111). Stars measure visibility, not whether either tool fits your constraints. ### Are every_eval_ever and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to every_eval_ever or auto-evaluator? GraphCanon lists graph-backed alternatives at [every_eval_ever alternatives](/tools/evaleval-every-eval-ever/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/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/evaleval-every-eval-ever-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, every_eval_ever or auto-evaluator? every_eval_ever: Very active. 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 every_eval_ever and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [every_eval_ever trust report](/tools/evaleval-every-eval-ever/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evaleval-every-eval-ever`](/api/graphcanon/graph?tool=evaleval-every-eval-ever) - 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/_