--- title: "dify vs AdalFlow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langgenius-dify-vs-sylphai-inc-adalflow" tools: ["langgenius-dify", "sylphai-inc-adalflow"] --- # dify vs AdalFlow *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 AdalFlow if adalFlow is designed to streamline the development and automatic optimization of LLM applications. [dify](https://dify.ai) reports 156k GitHub stars, 25k forks, and 1.1k open issues, last pushed Sep 18, 2026. [AdalFlow](http://adalflow.sylph.ai/) has 4.2k stars, 387 forks, and 68 open issues, last pushed May 29, 2026. Figures are from public GitHub metadata via [dify's repository](https://github.com/langgenius/dify) and [AdalFlow's repository](https://github.com/SylphAI-Inc/AdalFlow). | | [dify](/tools/langgenius-dify.md) | [AdalFlow](/tools/sylphai-inc-adalflow.md) | | --- | --- | --- | | Tagline | Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace. | The library to build & auto-optimize LLM applications. | | Stars | 156,243 | 4,213 | | Forks | 24,675 | 387 | | Open issues | 1,103 | 68 | | Language | TypeScript | Python | | 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. | AdalFlow is designed to streamline the development and automatic optimization of LLM applications. | | Persona | - | - | | Runtime | - | - | | License | Other | MIT | | Categories | AI Agents, Data & Retrieval, Developer Tools | AI Agents, Data & Retrieval, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [dify](/tools/langgenius-dify.md) | [AdalFlow](/tools/sylphai-inc-adalflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 101d | | Open issues (now) | 1.1k | 68 | | Stars delta | +4.5k (30d) | +17 (30d) | | Open issues delta | +172 (30d) | 0 (30d) | | Full report | [trust report](/tools/langgenius-dify/trust.md) | [trust report](/tools/sylphai-inc-adalflow/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: AdalFlow - **Adopt for:** AdalFlow is designed to streamline the development and automatic optimization of LLM applications. ## Choose when ### Choose dify if… - dify is primarily TypeScript; AdalFlow is Python. - License: dify is Other, AdalFlow is MIT. - Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation. - Also covers Developer Tools. - When you need a collaborative workspace that supports rich AI model and tool integration for agentic workflows and RAG pipelines. ### Choose AdalFlow if… - AdalFlow is primarily Python; dify is TypeScript. - License: AdalFlow is MIT, dify is Other. - Tags unique to AdalFlow: auto-prompting, bm25, chatbot, faiss. - Also covers LLM Frameworks, Model Training. - When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively. ## 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 AdalFlow - Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity. - AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes. ## Common questions ### What is the difference between dify and AdalFlow? dify: Build Agentic workflows and RAG pipelines with rich AI model and tool support on one collaborative workspace.. AdalFlow: The library to build & auto-optimize LLM applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose dify over AdalFlow? Choose dify over AdalFlow when dify is primarily TypeScript; AdalFlow is Python; License: dify is Other, AdalFlow is MIT; Tags unique to dify: agentic-ai, agentic-framework, agentic-workflow, automation; Also covers 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 AdalFlow over dify? Choose AdalFlow over dify when AdalFlow is primarily Python; dify is TypeScript; License: AdalFlow is MIT, dify is Other; Tags unique to AdalFlow: auto-prompting, bm25, chatbot, faiss; Also covers LLM Frameworks, Model Training; When you are working on projects that require advanced AI agents or chatbots with auto-prompting features, as AdalFlow can handle these needs comprehensively. ### 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 AdalFlow? Avoid using AdalFlow if your project does not benefit from auto-optimization features or does not involve LLM applications, as its specialized capabilities might introduce unnecessary complexity. AdalFlow may not be the best choice for projects where custom or low-level control over all aspects of the AI model training and optimization is required, given it's designed to streamline processes. ### Is dify or AdalFlow more popular on GitHub? dify has more GitHub stars (156,243 vs 4,213). Stars measure visibility, not whether either tool fits your constraints. ### Are dify and AdalFlow open source? Yes - both are open-source projects on GitHub (dify: Other, AdalFlow: MIT). ### Where can I find alternatives to dify or AdalFlow? GraphCanon lists graph-backed alternatives at [dify alternatives](/tools/langgenius-dify/alternatives) and [AdalFlow alternatives](/tools/sylphai-inc-adalflow/alternatives) ([dify markdown twin](/tools/langgenius-dify/alternatives.md), [AdalFlow markdown twin](/tools/sylphai-inc-adalflow/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-sylphai-inc-adalflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, dify or AdalFlow? dify: Very active. AdalFlow: 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 dify and AdalFlow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [dify trust report](/tools/langgenius-dify/trust); [AdalFlow trust report](/tools/sylphai-inc-adalflow/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/_