--- title: "every_eval_ever vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evaleval-every-eval-ever-vs-langchain-ai-auto-evaluator" tools: ["evaleval-every-eval-ever", "langchain-ai-auto-evaluator"] --- # every_eval_ever vs auto-evaluator *GraphCanon updated Sep 20, 2026* ## Verdict Pick every_eval_ever when every_eval_ever is primarily Python; auto-evaluator is TypeScript; pick auto-evaluator when auto-evaluator is primarily TypeScript; every_eval_ever is Python. [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 783 stars, 99 forks, and 21 open issues, last pushed Jun 26, 2025. 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/langchain-ai/auto-evaluator). | | [every_eval_ever](/tools/evaleval-every-eval-ever.md) | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Tagline | Shared schema and crowdsourced eval database | auto-evaluator | | Stars | 111 | 783 | | Forks | 49 | 99 | | Open issues | 27 | 21 | | Language | Python | TypeScript | | Adopt for | Every Eval Ever is dedicated to providing a standardized metadata framework and a crowdsourced evaluation database for AI results. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | 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/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Archived (8%) | | Days since push | 1d | 438d | | Archived on GitHub | No | Yes | | Open issues (now) | 27 | 21 | | Stars delta | +9 (30d) | 0 (30d) | | Open issues delta | +3 (30d) | 0 (30d) | | Full report | [trust report](/tools/evaleval-every-eval-ever/trust.md) | [trust report](/tools/langchain-ai-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. ## Choose when ### Choose every_eval_ever if… - every_eval_ever is primarily Python; auto-evaluator is TypeScript. - License: every_eval_ever is MIT, auto-evaluator is Other. - 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… - auto-evaluator is primarily TypeScript; every_eval_ever is Python. - License: auto-evaluator is Other, every_eval_ever is MIT. - Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel. - Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models ## 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 auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript - Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway ## Common questions ### What is the difference between every_eval_ever and auto-evaluator? every_eval_ever: Shared schema and crowdsourced eval database. auto-evaluator: auto-evaluator. 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 every_eval_ever is primarily Python; auto-evaluator is TypeScript; License: every_eval_ever is MIT, auto-evaluator is Other; 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 auto-evaluator is primarily TypeScript; every_eval_ever is Python; License: auto-evaluator is Other, every_eval_ever is MIT; Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel; Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models. ### 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 auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway ### Is every_eval_ever or auto-evaluator more popular on GitHub? auto-evaluator has more GitHub stars (783 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 (every_eval_ever: MIT, auto-evaluator: Other). ### 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/langchain-ai-auto-evaluator/alternatives) ([every_eval_ever markdown twin](/tools/evaleval-every-eval-ever/alternatives.md), [auto-evaluator markdown twin](/tools/langchain-ai-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-langchain-ai-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: Archived. 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/langchain-ai-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/_