--- title: "FastChat vs garak" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-nvidia-garak" tools: ["lm-sys-fastchat", "nvidia-garak"] --- # FastChat vs garak *GraphCanon updated Jul 11, 2026* ## Verdict Pick FastChat when tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models; pick garak when tags unique to garak: ai, llm-evaluation, llm-security, python. [FastChat](https://github.com/lm-sys/FastChat) reports 39k GitHub stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. [garak](https://discord.gg/uVch4puUCs) has 8.4k stars, 1.1k forks, and 367 open issues, last pushed Jul 10, 2026. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [garak's repository](https://github.com/NVIDIA/garak). | | [FastChat](/tools/lm-sys-fastchat.md) | [garak](/tools/nvidia-garak.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | the LLM vulnerability scanner | | Stars | 39,490 | 8,400 | | Forks | 4,788 | 1,079 | | Open issues | 1,027 | 367 | | Language | Python | 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, Inference & Serving, LLM Frameworks, Model Training | Evaluation & Observability, LLM Frameworks, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [garak](/tools/nvidia-garak.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 71d | 1d | | Open issues (now) | 1.0k | 367 | | Security scan | No lockfile | 54 low (54 low) | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/nvidia-garak/trust.md) | ## Shared compatibility - **Python**: [FastChat](/tools/lm-sys-fastchat.md) - Python runtime; [garak](/tools/nvidia-garak.md) - Python runtime ## 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… - 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. ### Choose garak if… - Tags unique to garak: ai, llm-evaluation, llm-security, python. - Also covers Vector Databases. - More recently updated (last pushed Jul 10, 2026). ## 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 garak - 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. - Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ## Common questions ### What is the difference between FastChat and garak? FastChat: An open platform for training, serving, and evaluating large language models. garak: the LLM vulnerability scanner. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over garak? Choose FastChat over garak when 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 choose garak over FastChat? Choose garak over FastChat when Tags unique to garak: ai, llm-evaluation, llm-security, python; Also covers Vector Databases; More recently updated (last pushed Jul 10, 2026). ### 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 garak? 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. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. ### Is FastChat or garak more popular on GitHub? FastChat has more GitHub stars (39,490 vs 8,400). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and garak open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, garak: Apache-2.0). ### Where can I find alternatives to FastChat or garak? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [garak alternatives](/tools/nvidia-garak/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [garak markdown twin](/tools/nvidia-garak/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-nvidia-garak.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or garak? FastChat: Steady. garak: 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 garak? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [garak trust report](/tools/nvidia-garak/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/_