--- title: "FlagAI vs FastChat" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/flagai-open-flagai-vs-lm-sys-fastchat" tools: ["flagai-open-flagai", "lm-sys-fastchat"] --- # FlagAI vs FastChat *GraphCanon updated Aug 15, 2026* ## Verdict Pick FlagAI if flagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license; 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. [FlagAI](https://github.com/FlagAI-Open/FlagAI) reports 3.9k GitHub stars, 416 forks, and 22 open issues, last pushed Jul 13, 2026. [FastChat](https://github.com/lm-sys/FastChat) has 40k stars, 4.8k forks, and 1.0k open issues, last pushed May 1, 2026. Figures are from public GitHub metadata via [FlagAI's repository](https://github.com/FlagAI-Open/FlagAI) and [FastChat's repository](https://github.com/lm-sys/FastChat). | | [FlagAI](/tools/flagai-open-flagai.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Tagline | Fast, easy-to-use framework for large-scale AI models. | An open platform for training, serving, and evaluating large language models | | Stars | 3,870 | 39,517 | | Forks | 416 | 4,788 | | Open issues | 22 | 1,038 | | Language | Python | Python | | Adopt for | FlagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license. | 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 | LLM Frameworks, Model Training | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FlagAI](/tools/flagai-open-flagai.md) | [FastChat](/tools/lm-sys-fastchat.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Slowing (36%) | | Days since push | 33d | 98d | | Open issues (now) | 22 | 1.0k | | Stars delta | +2 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/flagai-open-flagai/trust.md) | [trust report](/tools/lm-sys-fastchat/trust.md) | ## Shared compatibility - **Python**: [FlagAI](/tools/flagai-open-flagai.md) - Python runtime; [FastChat](/tools/lm-sys-fastchat.md) - Python runtime ## Decision facts: FlagAI - **Adopt for:** FlagAI is identified by its fast and scalable toolkit designed for managing large-scale AI models in Python, under an Apache-2.0 license. ## 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 FlagAI if… - Tags unique to FlagAI: extensible, fast, large-scale models. - FlagAI ships Docker support for self-hosted deployment. - When you prioritize speed and extensibility during the development of large-scale AI models with a focus on easy-to-use interfaces. ### Choose FastChat if… - Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models. - Also covers Evaluation & Observability, Inference & Serving. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ## When NOT to use FlagAI - If your project necessitates a deep level of customization that might not be supported by FlagAI's framework. - If you are working with smaller datasets, the overhead provided by FlagAI’s scalability features could be unnecessary and potentially inefficient. ## 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 FlagAI and FastChat? FlagAI: Fast, easy-to-use framework for large-scale AI models.. 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 FlagAI over FastChat? Choose FlagAI over FastChat when Tags unique to FlagAI: extensible, fast, large-scale models; FlagAI ships Docker support for self-hosted deployment; When you prioritize speed and extensibility during the development of large-scale AI models with a focus on easy-to-use interfaces. ### When should I choose FastChat over FlagAI? Choose FastChat over FlagAI when Tags unique to FastChat: chatbots, distributed-serving, evaluation system, large language models; Also covers Evaluation & Observability, Inference & Serving; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I avoid FlagAI? If your project necessitates a deep level of customization that might not be supported by FlagAI's framework. If you are working with smaller datasets, the overhead provided by FlagAI’s scalability features could be unnecessary and potentially inefficient. ### 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 FlagAI or FastChat more popular on GitHub? FastChat has more GitHub stars (39,517 vs 3,870). Stars measure visibility, not whether either tool fits your constraints. ### Are FlagAI and FastChat open source? Yes - both are open-source projects on GitHub (FlagAI: Apache-2.0, FastChat: Apache-2.0). ### Where can I find alternatives to FlagAI or FastChat? GraphCanon lists graph-backed alternatives at [FlagAI alternatives](/tools/flagai-open-flagai/alternatives) and [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) ([FlagAI markdown twin](/tools/flagai-open-flagai/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/flagai-open-flagai-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, FlagAI or FastChat? FlagAI: Steady. FastChat: 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 FlagAI and FastChat? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FlagAI trust report](/tools/flagai-open-flagai/trust); [FastChat trust report](/tools/lm-sys-fastchat/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=flagai-open-flagai`](/api/graphcanon/graph?tool=flagai-open-flagai) - 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/_