--- title: "FastChat vs PocketFlow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lm-sys-fastchat-vs-tencent-pocketflow" tools: ["lm-sys-fastchat", "tencent-pocketflow"] --- # FastChat vs PocketFlow *GraphCanon updated Jul 11, 2026* ## Verdict Pick FastChat when license: FastChat is Apache-2.0, PocketFlow is Other; pick PocketFlow when license: PocketFlow is Other, 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. [PocketFlow](https://pocketflow.github.io) has 2.9k stars, 490 forks, and 75 open issues, last pushed Mar 31, 2023. Figures are from public GitHub metadata via [FastChat's repository](https://github.com/lm-sys/FastChat) and [PocketFlow's repository](https://github.com/Tencent/PocketFlow). | | [FastChat](/tools/lm-sys-fastchat.md) | [PocketFlow](/tools/tencent-pocketflow.md) | | --- | --- | --- | | Tagline | An open platform for training, serving, and evaluating large language models | An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications. | | Stars | 39,490 | 2,909 | | Forks | 4,788 | 490 | | Open issues | 1,027 | 75 | | 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 | Other | | Categories | Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training, Vector Databases | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastChat](/tools/lm-sys-fastchat.md) | [PocketFlow](/tools/tencent-pocketflow.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 71d | 1198d | | Open issues (now) | 1.0k | 75 | | Full report | [trust report](/tools/lm-sys-fastchat/trust.md) | [trust report](/tools/tencent-pocketflow/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, PocketFlow is Other. - Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models. - Also covers Evaluation & Observability, LLM Frameworks. - - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### Choose PocketFlow if… - License: PocketFlow is Other, FastChat is Apache-2.0. - Tags unique to PocketFlow: automl, computer-vision, deep-learning, mobile-app. - Also covers Vector Databases. ## 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 PocketFlow - Last GitHub push was 1198 days ago (dormant maintenance, Mar 31, 2023). Validate activity before betting a new project on PocketFlow. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - 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 PocketFlow? FastChat: An open platform for training, serving, and evaluating large language models. PocketFlow: An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastChat over PocketFlow? Choose FastChat over PocketFlow when License: FastChat is Apache-2.0, PocketFlow is Other; Tags unique to FastChat: chatbots, distributed serving, evaluation system, large-language-models; Also covers Evaluation & Observability, LLM Frameworks; - You are looking to train and evaluate state-of-the-art models such as Vicuna or MT-Bench. ### When should I choose PocketFlow over FastChat? Choose PocketFlow over FastChat when License: PocketFlow is Other, FastChat is Apache-2.0; Tags unique to PocketFlow: automl, computer-vision, deep-learning, mobile-app; Also covers Vector Databases. ### 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 PocketFlow? Last GitHub push was 1198 days ago (dormant maintenance, Mar 31, 2023). Validate activity before betting a new project on PocketFlow. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. 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 PocketFlow more popular on GitHub? FastChat has more GitHub stars (39,490 vs 2,909). Stars measure visibility, not whether either tool fits your constraints. ### Are FastChat and PocketFlow open source? Yes - both are open-source projects on GitHub (FastChat: Apache-2.0, PocketFlow: Other). ### Where can I find alternatives to FastChat or PocketFlow? GraphCanon lists graph-backed alternatives at [FastChat alternatives](/tools/lm-sys-fastchat/alternatives) and [PocketFlow alternatives](/tools/tencent-pocketflow/alternatives) ([FastChat markdown twin](/tools/lm-sys-fastchat/alternatives.md), [PocketFlow markdown twin](/tools/tencent-pocketflow/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-tencent-pocketflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastChat or PocketFlow? FastChat: Steady. PocketFlow: Dormant. 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 PocketFlow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastChat trust report](/tools/lm-sys-fastchat/trust); [PocketFlow trust report](/tools/tencent-pocketflow/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/_