--- title: "FastGPT vs quilt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/labring-fastgpt-vs-quiltdata-quilt" tools: ["labring-fastgpt", "quiltdata-quilt"] --- # FastGPT vs quilt *GraphCanon updated Aug 16, 2026* ## Verdict Pick FastGPT if fastGPT is a knowledge-based platform optimized for developing and deploying complex question-answering systems with built-in capabilities to process data, retrieve relevant information through RAG techniques, and enable; pick quilt if quilt is a platform focused on managing scientific and AI-related datasets through detailed version control and context-rich data packages hosted on AWS. [FastGPT](https://fastgpt.io) reports 29k GitHub stars, 7.3k forks, and 165 open issues, last pushed Aug 16, 2026. [quilt](https://quilt.bio) has 1.4k stars, 90 forks, and 143 open issues, last pushed Jul 31, 2026. Figures are from public GitHub metadata via [FastGPT's repository](https://github.com/labring/FastGPT) and [quilt's repository](https://github.com/quiltdata/quilt). | | [FastGPT](/tools/labring-fastgpt.md) | [quilt](/tools/quiltdata-quilt.md) | | --- | --- | --- | | Tagline | A knowledge-based platform built on LLMs for developing and deploying complex question-answering systems | Scientific Data Management Platform on AWS for managing data packages | | Stars | 29,366 | 1,367 | | Forks | 7,264 | 90 | | Open issues | 165 | 143 | | Language | TypeScript | TypeScript | | Adopt for | FastGPT is a knowledge-based platform optimized for developing and deploying complex question-answering systems with built-in capabilities to process data, retrieve relevant information through RAG techniques, and enable | Quilt is a platform focused on managing scientific and AI-related datasets through detailed version control and context-rich data packages hosted on AWS. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 | | Categories | AI Agents, Data & Retrieval, LLM Frameworks | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [FastGPT](/tools/labring-fastgpt.md) | [quilt](/tools/quiltdata-quilt.md) | | --- | --- | --- | | Days since push | 0d | 2d | | Open issues (now) | 165 | 143 | | Stars delta | +360 (30d) | Unknown | | Open issues delta | +7 (30d) | Unknown | | Full report | [trust report](/tools/labring-fastgpt/trust.md) | [trust report](/tools/quiltdata-quilt/trust.md) | ## Decision facts: FastGPT - **Adopt for:** FastGPT is a knowledge-based platform optimized for developing and deploying complex question-answering systems with built-in capabilities to process data, retrieve relevant information through RAG techniques, and enable ## Decision facts: quilt - **Adopt for:** Quilt is a platform focused on managing scientific and AI-related datasets through detailed version control and context-rich data packages hosted on AWS. ## Choose when ### Choose FastGPT if… - License: FastGPT is Other, quilt is Apache-2.0. - Tags unique to FastGPT: agent, claude, deepseek, llm. - Also covers AI Agents, LLM Frameworks. - You prioritize ease of setup and configuration for advanced AI applications. ### Choose quilt if… - License: quilt is Apache-2.0, FastGPT is Other. - Tags unique to quilt: data-engineering, data-version-control, data-versioning, parquet. - When your team requires in-depth version control for complex scientific datasets, ensuring that every change to the dataset is tracked and documented. ## When NOT to use FastGPT - If your needs extend beyond question-answering to other AI applications that require extensive customization which FastGPT does not natively support. - When the level of customization for specific data processors or RAG methods is more critical than out-of-the-box functionality. - Your use case requires a broader array of model integration, as FastGPT currently specializes in certain frameworks and may lack the depth needed for highly specialized models. ## When NOT to use quilt - Not recommended if you prefer on-premises solutions over AWS-hosted services, as Quilt operates exclusively within the Amazon Web Services ecosystem. - If your project involves handling simple and static datasets where traditional CVS tools suffice. The overhead of using a specialized solution like Quilt might not justify its use. - Teams with limited experience in data engineering or with less sophisticated dataset requirements may find Quilt's robust features overly complex, potentially leading to underutilization. ## Common questions ### What is the difference between FastGPT and quilt? FastGPT: A knowledge-based platform built on LLMs for developing and deploying complex question-answering systems. quilt: Scientific Data Management Platform on AWS for managing data packages. See the comparison table for live GitHub stats and shared categories. ### When should I choose FastGPT over quilt? Choose FastGPT over quilt when License: FastGPT is Other, quilt is Apache-2.0; Tags unique to FastGPT: agent, claude, deepseek, llm; Also covers AI Agents, LLM Frameworks; You prioritize ease of setup and configuration for advanced AI applications. ### When should I choose quilt over FastGPT? Choose quilt over FastGPT when License: quilt is Apache-2.0, FastGPT is Other; Tags unique to quilt: data-engineering, data-version-control, data-versioning, parquet; When your team requires in-depth version control for complex scientific datasets, ensuring that every change to the dataset is tracked and documented. ### When should I avoid FastGPT? If your needs extend beyond question-answering to other AI applications that require extensive customization which FastGPT does not natively support. When the level of customization for specific data processors or RAG methods is more critical than out-of-the-box functionality. Your use case requires a broader array of model integration, as FastGPT currently specializes in certain frameworks and may lack the depth needed for highly specialized models. ### When should I avoid quilt? Not recommended if you prefer on-premises solutions over AWS-hosted services, as Quilt operates exclusively within the Amazon Web Services ecosystem. If your project involves handling simple and static datasets where traditional CVS tools suffice. The overhead of using a specialized solution like Quilt might not justify its use. Teams with limited experience in data engineering or with less sophisticated dataset requirements may find Quilt's robust features overly complex, potentially leading to underutilization. ### Is FastGPT or quilt more popular on GitHub? FastGPT has more GitHub stars (29,366 vs 1,367). Stars measure visibility, not whether either tool fits your constraints. ### Are FastGPT and quilt open source? Yes - both are open-source projects on GitHub (FastGPT: Other, quilt: Apache-2.0). ### Where can I find alternatives to FastGPT or quilt? GraphCanon lists graph-backed alternatives at [FastGPT alternatives](/tools/labring-fastgpt/alternatives) and [quilt alternatives](/tools/quiltdata-quilt/alternatives) ([FastGPT markdown twin](/tools/labring-fastgpt/alternatives.md), [quilt markdown twin](/tools/quiltdata-quilt/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/labring-fastgpt-vs-quiltdata-quilt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, FastGPT or quilt? FastGPT: Very active. quilt: 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 FastGPT and quilt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [FastGPT trust report](/tools/labring-fastgpt/trust); [quilt trust report](/tools/quiltdata-quilt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=labring-fastgpt`](/api/graphcanon/graph?tool=labring-fastgpt) - 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/_