--- title: "do-not-answer vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/libr-ai-do-not-answer-vs-lm-sys-fastchat" tools: ["libr-ai-do-not-answer", "lm-sys-fastchat"] --- # do-not-answer vs FastChat *GraphCanon updated Jul 11, 2026* ## Verdict Pick do-not-answer when do-not-answer is primarily Jupyter Notebook; FastChat is Python; pick FastChat when fastChat is primarily Python; do-not-answer is Jupyter Notebook. [do-not-answer](https://github.com/Libr-AI/do-not-answer) reports 334 GitHub stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. [FastChat](https://github.com/lm-sys/FastChat) has 39k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | An open platform for training, serving, and evaluating large language models | | Stars | 334 | 39,490 | | Forks | 29 | 4,788 | | Open issues | 0 | 1,027 | | Language | Jupyter Notebook | 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 | Apache-2.0 | | Categories | Evaluation & Observability, LLM Frameworks | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [do-not-answer](/tools/libr-ai-do-not-answer.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 764d | 71d | | Open issues (now) | 0 | 1.0k | | Full report | [trust report](/tools/libr-ai-do-not-answer/trust.md) | [trust report](/tools/lm-sys-fastchat/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 do-not-answer if… - do-not-answer is primarily Jupyter Notebook; FastChat is Python. - Tags unique to do-not-answer: jupyter notebook. - Leaner open-issue backlog (0). ### Choose FastChat if… - FastChat is primarily Python; do-not-answer is Jupyter Notebook. - Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models. - Also covers Inference & Serving, Model Training. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use do-not-answer - Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ## 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. ## Common questions ### What is the difference between do-not-answer and FastChat? do-not-answer: Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs. FastChat: An open platform for training, serving, and evaluating large language models. See the comparison table for live GitHub stats and shared categories. ### When should I choose do-not-answer over FastChat? Choose do-not-answer over FastChat when do-not-answer is primarily Jupyter Notebook; FastChat is Python; Tags unique to do-not-answer: jupyter notebook; Leaner open-issue backlog (0). ### When should I choose FastChat over do-not-answer? Choose FastChat over do-not-answer when FastChat is primarily Python; do-not-answer is Jupyter Notebook; Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models; Also covers Inference & Serving, Model Training; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid do-not-answer? Last GitHub push was 764 days ago (dormant maintenance, Jun 7, 2024). Validate activity before betting a new project on do-not-answer. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. ### 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. ### Is do-not-answer or FastChat more popular on GitHub? FastChat has more GitHub stars (39,490 vs 334). Stars measure visibility, not whether either tool fits your constraints. ### Are do-not-answer and FastChat open source? Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to do-not-answer or FastChat? GraphCanon lists graph-backed alternatives at [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([do-not-answer markdown twin](/tools/libr-ai-do-not-answer/alternatives.md), [FastChat markdown twin](/tools/lm-sys-fastchat/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/libr-ai-do-not-answer-vs-lm-sys-fastchat.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, do-not-answer or FastChat? do-not-answer: Dormant. FastChat: Steady. 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 do-not-answer and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=libr-ai-do-not-answer`](/api/graphcanon/graph?tool=libr-ai-do-not-answer) - 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/_