--- title: "FastChat vs Awesome-LLM-Eval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-onejune2018-awesome-llm-eval" tools: ["lm-sys-fastchat", "onejune2018-awesome-llm-eval"] --- # FastChat vs Awesome-LLM-Eval *GraphCanon updated Jul 11, 2026* ## Verdict Pick FastChat when license: FastChat is Apache-2.0, Awesome-LLM-Eval is MIT; pick Awesome-LLM-Eval when license: Awesome-LLM-Eval is MIT, FastChat is Apache-2.0. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [Awesome-LLM-Eval](https://arxiv.org/abs/2508.18646) has 648 stars, 78 forks, and 38 open issues, last pushed Nov 24, 2025. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [Awesome-LLM-Eval's repository](https://github.com/onejune2018/Awesome-LLM-Eval). | | [FastChat](/tools/lm-sys-fastchat.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界. | | Stars | 39,490 | 648 | | Forks | 4,788 | 78 | | Open issues | 1,027 | 38 | | Language | Python | - | | Adopt for | FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB | - | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | LLM Frameworks, Model Training, Inference & Serving, Evaluation & Observability | LLM Frameworks, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [Awesome-LLM-Eval](/tools/onejune2018-awesome-llm-eval.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 71d | 229d | | Open issues (now) | 1.0k | 38 | | Owner type | Organization | User | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/onejune2018-awesome-llm-eval/trust.md) | ## Decision facts: FastChat - **Adopt for:** FastChat is a comprehensive open platform for managing large language models (LLMs) that includes capabilities for training, serving, evaluating, and comparing chatbot models via web UIs and RESTful APIs. It powers ChatB ## Choose when ### Choose FastChat if… - License: FastChat is Apache-2.0, Awesome-LLM-Eval is MIT. - Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving. - Also covers Model Training, Inference & Serving. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### Choose Awesome-LLM-Eval if… - License: Awesome-LLM-Eval is MIT, FastChat is Apache-2.0. - Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark. - Leaner open-issue backlog (38). ## When NOT to use FastChat - - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on - + Mac. ## When NOT to use Awesome-LLM-Eval - Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## Common questions ### What is the difference between FastChat and Awesome-LLM-Eval? FastChat: An open platform for training, serving, and evaluating large language models. Awesome-LLM-Eval: Awesome-LLM-Eval: a curated list of tools, datasets/benchmark, demos, leaderboard, papers, docs and models, mainly for Evaluation on LLMs. 一个由工具、基准/数据、演示、排行榜和大模型等组成的精选列表，主要面向基础大模型评测，旨在探求生成式AI的技术边界.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over Awesome-LLM-Eval? Choose FastChat over Awesome-LLM-Eval when License: FastChat is Apache-2.0, Awesome-LLM-Eval is MIT; Tags unique to FastChat: evaluation system, large-language-models, chatbots, distributed serving; Also covers Model Training, Inference & Serving; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I choose Awesome-LLM-Eval over FastChat? Choose Awesome-LLM-Eval over FastChat when License: Awesome-LLM-Eval is MIT, FastChat is Apache-2.0; Tags unique to Awesome-LLM-Eval: bert, evaluation, dataset, benchmark; Leaner open-issue backlog (38). ### When should I avoid FastChat? - You require a proprietary or closed-source framework; FastChat is open-source under Apache-2.0 license and its use might be unsuitable for environments requiring proprietary solutions. - Your chatbot evaluation needs do not align with the types of data used in FastChat's datasets (e.g., human votes, MT-Bench evaluations). - You prefer a more user-friendly setup without the need to clone a repository and manually install dependencies; FastChat requires installation from source with additional steps for Rust and CMake on + Mac. ### When should I avoid Awesome-LLM-Eval? Last GitHub push was 230 days ago (slowing maintenance, Nov 24, 2025). Validate activity before betting a new project on Awesome-LLM-Eval. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### Is FastChat or Awesome-LLM-Eval more popular on GitHub? FastChat has more GitHub stars (39,490 vs 648). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and Awesome-LLM-Eval open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, Awesome-LLM-Eval: MIT). ### Where can I find alternatives to FastChat or Awesome-LLM-Eval? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [Awesome-LLM-Eval alternatives](/tools/onejune2018-awesome-llm-eval/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [Awesome-LLM-Eval markdown twin](/tools/onejune2018-awesome-llm-eval/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/lm-sys-fastchat-vs-onejune2018-awesome-llm-eval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or Awesome-LLM-Eval? FastChat: Steady. Awesome-LLM-Eval: Slowing. 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 FastChat and Awesome-LLM-Eval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [Awesome-LLM-Eval trust report](/tools/onejune2018-awesome-llm-eval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lm-sys-fastchat`](/api/graphcanon/graph?tool=lm-sys-fastchat) - 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/_