--- title: "FastChat vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-wangrongsheng-awesome-llm-resources" tools: ["lm-sys-fastchat", "wangrongsheng-awesome-llm-resources"] --- # FastChat vs awesome-LLM-resources *GraphCanon updated Aug 17, 2026* ## Verdict Pick FastChat if 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; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG. [FastChat](https://github.com/lm-sys/FastChat) reports 40k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [FastChat](/tools/lm-sys-fastchat.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | Summary of the world's best LLM resources. | | Stars | 39,517 | 8,845 | | Forks | 4,788 | 950 | | Open issues | 1,038 | 23 | | 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 | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 98d | 2d | | Open issues (now) | 1.0k | 23 | | Stars delta | Unknown | +142 (30d) | | Open issues delta | Unknown | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/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 ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose FastChat if… - Tags unique to FastChat: chatbots, distributed-serving, evaluation system. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. - More GitHub stars (40k vs 8.8k) - visibility, not fit. ### Choose awesome-LLM-resources if… - Tags unique to awesome-LLM-resources: awesome-list, book, course, llama. - Also covers AI Agents, Developer Tools. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## 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-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between FastChat and awesome-LLM-resources? FastChat: An open platform for training, serving, and evaluating large language models. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over awesome-LLM-resources? Choose FastChat over awesome-LLM-resources when Tags unique to FastChat: chatbots, distributed-serving, evaluation system; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench; More GitHub stars (40k vs 8.8k) - visibility, not fit. ### When should I choose awesome-LLM-resources over FastChat? Choose awesome-LLM-resources over FastChat when Tags unique to awesome-LLM-resources: awesome-list, book, course, llama; Also covers AI Agents, Developer Tools; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### 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-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is FastChat or awesome-LLM-resources more popular on GitHub? FastChat has more GitHub stars (39,517 vs 8,845). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to FastChat or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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-wangrongsheng-awesome-llm-resources.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-resources? FastChat: Slowing. awesome-LLM-resources: 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 FastChat and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/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/_