--- title: "GAGE vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hithink-research-gage-vs-rlancemartin-auto-evaluator" tools: ["hithink-research-gage", "rlancemartin-auto-evaluator"] --- # GAGE vs auto-evaluator *GraphCanon updated Sep 20, 2026* ## Verdict Pick GAGE if gAGE is a unified evaluation framework that offers fast local testing through a consistent pipeline for large language models, multimodal models, audio models, diffusion models, agents, and game environments; 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. [GAGE](https://github.com/HiThink-Research/GAGE) reports 52 GitHub stars, 8 forks, and 3 open issues, last pushed Jun 2, 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 [GAGE's repository](https://github.com/HiThink-Research/GAGE) and [auto-evaluator's repository](https://github.com/rlancemartin/auto-evaluator). | | [GAGE](/tools/hithink-research-gage.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Tagline | Unified Evaluation Engine for AI Models | A lightweight evaluation tool for question-answering using Langchain | | Stars | 52 | 1,102 | | Forks | 8 | 92 | | Open issues | 3 | 3 | | Language | Python | Python | | Adopt for | GAGE is a unified evaluation framework that offers fast local testing through a consistent pipeline for large language models, multimodal models, audio models, diffusion models, agents, and game environments. | 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 | (unknown) - License unknown, proceed with caution as license compliance may be unclear. | - | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [GAGE](/tools/hithink-research-gage.md) | [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 99d | 1216d | | Stars delta | +1 (30d) | -3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/hithink-research-gage/trust.md) | [trust report](/tools/rlancemartin-auto-evaluator/trust.md) | ## Shared compatibility - **Python**: [GAGE](/tools/hithink-research-gage.md) - Python runtime; [auto-evaluator](/tools/rlancemartin-auto-evaluator.md) - Python runtime ## Decision facts: GAGE - **Adopt for:** GAGE is a unified evaluation framework that offers fast local testing through a consistent pipeline for large language models, multimodal models, audio models, diffusion models, agents, and game environments. - **License detail:** (unknown) - License unknown, proceed with caution as license compliance may be unclear. - **Runtime:** unknown ## 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 GAGE if… - Tags unique to GAGE: agents, audio_models, diffusion-models, game_environments. - If you need to evaluate various AI model types with one engine, including game engines like Space Invaders or Mahjong. - More recently updated (last pushed Jun 2, 2026). ### Choose auto-evaluator if… - Tags unique to auto-evaluator: gpt-3.5-turbo, langchain, llm, question-answering. - Use when you need a lightweight solution for testing question-answering capabilities of Langchain models. - More GitHub stars (1.1k vs 52) - visibility, not fit. ## When NOT to use GAGE - Avoid if your project requires real-time multiplayer game evaluation capabilities since GAGE focuses more on single-agent sandbox environments and turn-based games. - Not suitable for scenarios where a visual interface is needed for the evaluation process but does not require replayable artifacts or structured arena traces. ## 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 GAGE and auto-evaluator? GAGE: Unified Evaluation Engine for AI Models. 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 GAGE over auto-evaluator? Choose GAGE over auto-evaluator when Tags unique to GAGE: agents, audio_models, diffusion-models, game_environments; If you need to evaluate various AI model types with one engine, including game engines like Space Invaders or Mahjong; More recently updated (last pushed Jun 2, 2026). ### When should I choose auto-evaluator over GAGE? Choose auto-evaluator over GAGE when Tags unique to auto-evaluator: gpt-3.5-turbo, langchain, llm, question-answering; Use when you need a lightweight solution for testing question-answering capabilities of Langchain models; More GitHub stars (1.1k vs 52) - visibility, not fit. ### When should I avoid GAGE? Avoid if your project requires real-time multiplayer game evaluation capabilities since GAGE focuses more on single-agent sandbox environments and turn-based games. Not suitable for scenarios where a visual interface is needed for the evaluation process but does not require replayable artifacts or structured arena traces. ### 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 GAGE or auto-evaluator more popular on GitHub? auto-evaluator has more GitHub stars (1,102 vs 52). Stars measure visibility, not whether either tool fits your constraints. ### Are GAGE and auto-evaluator open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to GAGE or auto-evaluator? GraphCanon lists graph-backed alternatives at [GAGE alternatives](/tools/hithink-research-gage/alternatives) and [auto-evaluator alternatives](/tools/rlancemartin-auto-evaluator/alternatives) ([GAGE markdown twin](/tools/hithink-research-gage/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/hithink-research-gage-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, GAGE or auto-evaluator? GAGE: Slowing. 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 GAGE and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [GAGE trust report](/tools/hithink-research-gage/trust); [auto-evaluator trust report](/tools/rlancemartin-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hithink-research-gage`](/api/graphcanon/graph?tool=hithink-research-gage) - 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/_