--- title: "contextcheck vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/addepto-contextcheck-vs-rlancemartin-auto-evaluator" tools: ["addepto-contextcheck", "rlancemartin-auto-evaluator"] --- # contextcheck vs auto-evaluator *GraphCanon updated Aug 8, 2026* ## Verdict Pick contextcheck if contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines; 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. [contextcheck](https://addepto.com/) reports 96 GitHub stars, 11 forks, and 1 open issues, last pushed Dec 11, 2024. [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 [contextcheck's repository](https://github.com/Addepto/contextcheck) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [contextcheck](/tools/addepto-contextcheck.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Framework for LLMs and RAGs testing in Python | A lightweight evaluation tool for question-answering using Langchain | | Stars | 96 | 1,105 | | Forks | 11 | 92 | | Open issues | 1 | 3 | | Language | Python | Python | | Adopt for | Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. | 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, Model Training | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [contextcheck](/tools/addepto-contextcheck.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Days since push | 604d | 1186d | | Open issues (now) | 1 | 3 | | Owner type | Organization | User | | Full report | [trust report](/tools/addepto-contextcheck/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Decision facts: contextcheck - **Adopt for:** Contextcheck, an MIT-licensed Python framework for evaluating large language models and RAG systems through configurable YAML settings that integrate with CI pipelines. ## 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 contextcheck if… - Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration. - Also covers Model Training. - When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### 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 96) - visibility, not fit. ## When NOT to use contextcheck - Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. - Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ## 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 contextcheck and auto-evaluator? contextcheck: Framework for LLMs and RAGs testing in Python. 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 contextcheck over auto-evaluator? Choose contextcheck over auto-evaluator when Tags unique to contextcheck: ai-chat, ai-testing, chatbot-framework, ci-integration; Also covers Model Training; When you require a framework specifically designed to test the robustness of both LLMs and Retrieval-Augmented Generation systems within Python projects. ### When should I choose auto-evaluator over contextcheck? Choose auto-evaluator over contextcheck 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 96) - visibility, not fit. ### When should I avoid contextcheck? Avoid if you aim for a framework without configuration flexibility through YAML as contextcheck strictly relies on this format for setting up test environments. Skip contextcheck if your project environment or requirements do not align with the MIT license terms, especially in contexts where licensing compatibility must be strictly observed. ### 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 contextcheck or auto-evaluator more popular on GitHub? auto-evaluator has more GitHub stars (1,105 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are contextcheck and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to contextcheck or auto-evaluator? GraphCanon lists graph-backed alternatives at [contextcheck alternatives](/tools/addepto-contextcheck/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([contextcheck markdown twin](/tools/addepto-contextcheck/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/addepto-contextcheck-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, contextcheck or auto-evaluator? contextcheck: 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 contextcheck and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [contextcheck trust report](/tools/addepto-contextcheck/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=addepto-contextcheck`](/api/graphcanon/graph?tool=addepto-contextcheck) - 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/_