--- title: "ragflow vs AdalFlow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/infiniflow-ragflow-vs-sylphai-inc-adalflow" tools: ["infiniflow-ragflow", "sylphai-inc-adalflow"] --- # ragflow vs AdalFlow *GraphCanon updated Aug 7, 2026* ## Verdict Pick ragflow if rAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license; pick AdalFlow if adalFlow is designed to streamline the development and automatic optimization of LLM applications. [ragflow](https://ragflow.io) reports 87k GitHub stars, 10k forks, and 2.0k open issues, last pushed Jul 31, 2026. [AdalFlow](http://adalflow.sylph.ai/) has 4.2k stars, 384 forks, and 68 open issues, last pushed May 29, 2026. Figures are from public GitHub metadata via [ragflow's repository](https://github.com/infiniflow/ragflow) and [AdalFlow's repository](https://github.com/SylphAI-Inc/AdalFlow). | | [ragflow](/tools/infiniflow-ragflow.md) | [AdalFlow](/tools/sylphai-inc-adalflow.md) | | --- | --- | --- | | Tagline | Retrieval-Augmented Generation engine with agent capabilities | The library to build & auto-optimize LLM applications. | | Stars | 86,541 | 4,196 | | Forks | 10,167 | 384 | | Open issues | 1,993 | 68 | | Language | Go | Python | | Adopt for | RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. | AdalFlow is designed to streamline the development and automatic optimization of LLM applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | MIT | | Categories | AI Agents, Data & Retrieval | AI Agents, Data & Retrieval, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ragflow](/tools/infiniflow-ragflow.md) | [AdalFlow](/tools/sylphai-inc-adalflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 0d | 70d | | Open issues (now) | 2.0k | 68 | | Full report | [trust report](/tools/infiniflow-ragflow/trust.md) | [trust report](/tools/sylphai-inc-adalflow/trust.md) | ## Decision facts: ragflow - **Requirements:** Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services. - **Adopt for:** RAGFlow is a Retrieval-Augmented Generation (RAG) engine that integrates AI agents for enhanced context management in LLM applications, built using Go language and released under the Apache-2.0 license. - **License detail:** Apache-2.0 License ## Decision facts: AdalFlow - **Adopt for:** AdalFlow is designed to streamline the development and automatic optimization of LLM applications. ## Choose when ### Choose ragflow if… - ragflow is primarily Go; AdalFlow is Python. - License: ragflow is Apache-2.0, AdalFlow is MIT. - Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.. - Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation. - ragflow ships Docker support for self-hosted deployment. - - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### Choose AdalFlow if… - AdalFlow is primarily Python; ragflow is Go. - License: AdalFlow is MIT, ragflow is Apache-2.0. - Tags unique to AdalFlow: agent, ai, auto-prompting, bm25. - 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 ragflow - - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ## 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 ragflow and AdalFlow? ragflow: Retrieval-Augmented Generation engine with agent capabilities. AdalFlow: The library to build & auto-optimize LLM applications.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ragflow over AdalFlow? Choose ragflow over AdalFlow when ragflow is primarily Go; AdalFlow is Python; License: ragflow is Apache-2.0, AdalFlow is MIT; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Tags unique to ragflow: agentic-ai, context management, rag, retrieval-augmented-generation; ragflow ships Docker support for self-hosted deployment; - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### When should I choose AdalFlow over ragflow? Choose AdalFlow over ragflow when AdalFlow is primarily Python; ragflow is Go; License: AdalFlow is MIT, ragflow is Apache-2.0; Tags unique to AdalFlow: agent, ai, auto-prompting, bm25; 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 ragflow? - If you specifically require a non-Golang developed RAG engine, as RAGFlow is built entirely in Go. - Your setup does not support or need Docker (RAGFlow requires building a Docker image that is approximately 2 GB). - You cannot use external LLM services and embedding services, as RAGFlow relies on them to function. ### 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 ragflow or AdalFlow more popular on GitHub? ragflow has more GitHub stars (86,541 vs 4,196). Stars measure visibility, not whether either tool fits your constraints. ### Are ragflow and AdalFlow open source? Yes - both are open-source projects on GitHub (ragflow: Apache-2.0, AdalFlow: MIT). ### Where can I find alternatives to ragflow or AdalFlow? GraphCanon lists graph-backed alternatives at [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) and [AdalFlow alternatives](/tools/sylphai-inc-adalflow/alternatives) ([ragflow markdown twin](/tools/infiniflow-ragflow/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/infiniflow-ragflow-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, ragflow or AdalFlow? ragflow: Very active. AdalFlow: Steady. 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 ragflow and AdalFlow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ragflow trust report](/tools/infiniflow-ragflow/trust); [AdalFlow trust report](/tools/sylphai-inc-adalflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=infiniflow-ragflow`](/api/graphcanon/graph?tool=infiniflow-ragflow) - 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/_