--- title: "ColossalAI vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-langchain-ai-auto-evaluator" tools: ["hpcaitech-colossalai", "langchain-ai-auto-evaluator"] --- # ColossalAI vs auto-evaluator *GraphCanon updated Jul 12, 2026* ## Verdict Pick ColossalAI when colossalAI is primarily Python; auto-evaluator is TypeScript; pick auto-evaluator when auto-evaluator is primarily TypeScript; ColossalAI is Python. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [auto-evaluator](https://autoevaluator.langchain.com/) has 782 stars, 103 forks, and 21 open issues, last pushed Jun 26, 2025. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [auto-evaluator's repository](https://github.com/langchain-ai/auto-evaluator). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | auto-evaluator | | Stars | 41,408 | 782 | | Forks | 4,504 | 103 | | Open issues | 501 | 21 | | Language | Python | TypeScript | | Adopt for | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Other | | Categories | Model Training, Inference & Serving | Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Archived (8%) | | Days since push | 46d | 380d | | Archived on GitHub | No | Yes | | Open issues (now) | 501 | 21 | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/langchain-ai-auto-evaluator/trust.md) | ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Choose when ### Choose ColossalAI if… - ColossalAI is primarily Python; auto-evaluator is TypeScript. - License: ColossalAI is Apache-2.0, auto-evaluator is Other. - Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. - Also covers Model Training. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose auto-evaluator if… - auto-evaluator is primarily TypeScript; ColossalAI is Python. - License: auto-evaluator is Other, ColossalAI is Apache-2.0. - Tags unique to auto-evaluator: typescript. ## When NOT to use ColossalAI - You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. - Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## 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. ## Common questions ### What is the difference between ColossalAI and auto-evaluator? ColossalAI: Making large AI models cheaper, faster and more accessible. auto-evaluator: auto-evaluator. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over auto-evaluator? Choose ColossalAI over auto-evaluator when ColossalAI is primarily Python; auto-evaluator is TypeScript; License: ColossalAI is Apache-2.0, auto-evaluator is Other; Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training; Also covers Model Training; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose auto-evaluator over ColossalAI? Choose auto-evaluator over ColossalAI when auto-evaluator is primarily TypeScript; ColossalAI is Python; License: auto-evaluator is Other, ColossalAI is Apache-2.0; Tags unique to auto-evaluator: typescript. ### When should I avoid ColossalAI? You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems. Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### 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. ### Is ColossalAI or auto-evaluator more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 782). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and auto-evaluator open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, auto-evaluator: Other). ### Where can I find alternatives to ColossalAI or auto-evaluator? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [auto-evaluator alternatives](/tools/langchain-ai-auto-evaluator/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/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/hpcaitech-colossalai-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, ColossalAI or auto-evaluator? ColossalAI: Steady. 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 ColossalAI and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [auto-evaluator trust report](/tools/langchain-ai-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_