--- title: "auto-evaluator vs airllm" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langchain-ai-auto-evaluator-vs-lyogavin-airllm" tools: ["langchain-ai-auto-evaluator", "lyogavin-airllm"] --- # auto-evaluator vs airllm *GraphCanon updated Jul 12, 2026* ## Verdict Pick auto-evaluator when auto-evaluator is primarily TypeScript; airllm is Jupyter Notebook; pick airllm when airllm is primarily Jupyter Notebook; auto-evaluator is TypeScript. [auto-evaluator](https://autoevaluator.langchain.com/) reports 782 GitHub stars, 103 forks, and 21 open issues, last pushed Jun 26, 2025. [airllm](https://github.com/lyogavin/airllm) has 22k stars, 2.6k forks, and 106 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [auto-evaluator's repository](https://github.com/langchain-ai/auto-evaluator) and [airllm's repository](https://github.com/lyogavin/airllm). | | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Tagline | auto-evaluator | AirLLM 70B inference with single 4GB GPU | | Stars | 782 | 22,399 | | Forks | 103 | 2,581 | | Open issues | 21 | 106 | | Language | TypeScript | Jupyter Notebook | | Adopt for | - | AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | [airllm](/tools/lyogavin-airllm.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Very active (96%) | | Days since push | 380d | 0d | | Archived on GitHub | Yes | No | | Open issues (now) | 21 | 106 | | Owner type | Organization | User | | Security scan | No lockfile | 4 low (4 low) | | Full report | [trust report](/tools/langchain-ai-auto-evaluator/trust.md) | [trust report](/tools/lyogavin-airllm/trust.md) | ## Decision facts: airllm - **Pricing:** freemium - Free and open-source under the Apache-2.0 license; however, infrastructure costs apply. - **Requirements:** Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences. - **Adopt for:** AirLLM is a notable framework designed specifically for running large language models on low-resource hardware, such as a single 4GB GPU. - **License detail:** Apache-2.0 ## Choose when ### Choose auto-evaluator if… - auto-evaluator is primarily TypeScript; airllm is Jupyter Notebook. - License: auto-evaluator is Other, airllm is Apache-2.0. - Tags unique to auto-evaluator: typescript. ### Choose airllm if… - airllm is primarily Jupyter Notebook; auto-evaluator is TypeScript. - License: airllm is Apache-2.0, auto-evaluator is Other. - Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.. - Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.. - Tags unique to airllm: llama, chinese llm, llm, instruct-gpt. - If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM. ## When NOT to use auto-evaluator - auto-evaluator is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## When NOT to use airllm - Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. - Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition. ## Common questions ### What is the difference between auto-evaluator and airllm? auto-evaluator: auto-evaluator. airllm: AirLLM 70B inference with single 4GB GPU. See the comparison table for live GitHub stats and shared categories. ### When should I choose auto-evaluator over airllm? Choose auto-evaluator over airllm when auto-evaluator is primarily TypeScript; airllm is Jupyter Notebook; License: auto-evaluator is Other, airllm is Apache-2.0; Tags unique to auto-evaluator: typescript. ### When should I choose airllm over auto-evaluator? Choose airllm over auto-evaluator when airllm is primarily Jupyter Notebook; auto-evaluator is TypeScript; License: airllm is Apache-2.0, auto-evaluator is Other; Pricing: Free and open-source under the Apache-2.0 license; however, infrastructure costs apply.; Requirements: Min 16 GB RAM; A single 4GB GPU is sufficient for using this framework to run large language model inferences.; Tags unique to airllm: llama, chinese llm, llm, instruct-gpt; If you have limited hardware resources but need to perform inferences on large language models (like the 70B parameter model that AirLLM supports), use AirLLM. ### When should I avoid auto-evaluator? auto-evaluator is archived on GitHub. Prefer an active alternative unless you maintain a private fork or need a frozen dependency. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### When should I avoid airllm? Avoid using AirLLM if you require models to run on higher-end GPUs or multiple GPU clusters, as its strength lies in low-resource efficiency. Do not use AirLLM if you are working primarily with non-Chinese language datasets and models, since support for other languages may be less optimized compared to competition. ### Is auto-evaluator or airllm more popular on GitHub? airllm has more GitHub stars (22,399 vs 782). Stars measure visibility, not whether either tool fits your constraints. ### Are auto-evaluator and airllm open source? Yes - both are open-source projects on GitHub (auto-evaluator: Other, airllm: Apache-2.0). ### Where can I find alternatives to auto-evaluator or airllm? GraphCanon lists graph-backed alternatives at [auto-evaluator alternatives](/tools/langchain-ai-auto-evaluator/alternatives) and [airllm alternatives](/tools/lyogavin-airllm/alternatives) ([auto-evaluator markdown twin](/tools/langchain-ai-auto-evaluator/alternatives.md), [airllm markdown twin](/tools/lyogavin-airllm/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/langchain-ai-auto-evaluator-vs-lyogavin-airllm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, auto-evaluator or airllm? auto-evaluator: Archived. airllm: Very active. 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 airllm? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [auto-evaluator trust report](/tools/langchain-ai-auto-evaluator/trust); [airllm trust report](/tools/lyogavin-airllm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langchain-ai-auto-evaluator`](/api/graphcanon/graph?tool=langchain-ai-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/_