--- title: "dify vs quilt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langgenius-dify-vs-quiltdata-quilt" tools: ["langgenius-dify", "quiltdata-quilt"] --- # dify vs quilt *GraphCanon updated Oct 4, 2026* ## Verdict Pick dify if dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments; 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. [dify](https://dify.ai) reports 156k GitHub stars, 25k forks, and 1.1k open issues, last pushed Sep 18, 2026. [quilt](https://quilt.bio) has 1.4k stars, 89 forks, and 119 open issues, last pushed Sep 27, 2026. Figures are from public GitHub metadata via [dify's repository](https://github.com/langgenius/dify) and [quilt's repository](https://github.com/quiltdata/quilt). | | [dify](/tools/langgenius-dify.md) | [quilt](/tools/quiltdata-quilt.md) | | --- | --- | --- | | Tagline | Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace. | Scientific Data Management Platform on AWS for managing data packages | | Stars | 156,243 | 1,370 | | Forks | 24,675 | 89 | | Open issues | 1,103 | 119 | | Language | TypeScript | TypeScript | | Adopt for | Dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments. | 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, Developer Tools | Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dify](/tools/langgenius-dify.md) | [quilt](/tools/quiltdata-quilt.md) | | --- | --- | --- | | Open issues (now) | 1.1k | 119 | | Stars delta | +4.5k (30d) | +3 (30d) | | Open issues delta | +172 (30d) | -24 (30d) | | Full report | [trust report](/tools/langgenius-dify/trust.md) | [trust report](/tools/quiltdata-quilt/trust.md) | ## Decision facts: dify - **Adopt for:** Dify is a platform for creating agentic workflows and RAG pipelines, supporting various AI models and tools, deployable on cloud, VPC, or self-hosted environments. ## 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 dify if… - License: dify is Other, quilt is Apache-2.0. - Tags unique to dify: agent, agentic-ai, agentic-framework, agentic-workflow. - Also covers AI Agents, Developer Tools. - When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines. ### Choose quilt if… - License: quilt is Apache-2.0, dify 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 dify - If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool. - When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites. - If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions. ## 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 dify and quilt? dify: Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace.. 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 dify over quilt? Choose dify over quilt when License: dify is Other, quilt is Apache-2.0; Tags unique to dify: agent, agentic-ai, agentic-framework, agentic-workflow; Also covers AI Agents, Developer Tools; When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines. ### When should I choose quilt over dify? Choose quilt over dify when License: quilt is Apache-2.0, dify 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 dify? If your project does not require agentic workflows or RAG pipelines, and you are looking for a more general-purpose AI development tool. When you need a platform that does not require Docker and Docker Compose for setup, as Dify's quick start guide assumes these prerequisites. If your team prefers a platform with a different licensing model, as Dify uses a modified Apache 2.0 license with additional conditions. ### 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 dify or quilt more popular on GitHub? dify has more GitHub stars (156,243 vs 1,370). Stars measure visibility, not whether either tool fits your constraints. ### Are dify and quilt open source? Yes - both are open-source projects on GitHub (dify: Other, quilt: Apache-2.0). ### Where can I find alternatives to dify or quilt? GraphCanon lists graph-backed alternatives at [dify alternatives](/tools/langgenius-dify/alternatives) and [quilt alternatives](/tools/quiltdata-quilt/alternatives) ([dify markdown twin](/tools/langgenius-dify/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/langgenius-dify-vs-quiltdata-quilt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dify or quilt? dify: 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 dify and quilt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dify trust report](/tools/langgenius-dify/trust); [quilt trust report](/tools/quiltdata-quilt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langgenius-dify`](/api/graphcanon/graph?tool=langgenius-dify) - 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/_