--- title: "Flowise vs ragflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/flowiseai-flowise-vs-infiniflow-ragflow" tools: ["flowiseai-flowise", "infiniflow-ragflow"] --- # Flowise vs ragflow *GraphCanon updated Jul 17, 2026* ## Verdict Pick Flowise if flowise enables users to build AI agents visually, focusing on a low-code/no-code approach with support for technologies like langchain and large-language-models; 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. [Flowise](https://flowiseai.com) reports 55k GitHub stars, 25k forks, and 997 open issues, last pushed Jul 14, 2026. [ragflow](https://ragflow.io) has 85k stars, 9.9k forks, and 2.3k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [Flowise's repository](https://github.com/FlowiseAI/Flowise) and [ragflow's repository](https://github.com/infiniflow/ragflow). | | [Flowise](/tools/flowiseai-flowise.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Tagline | Build AI Agents, Visually | Retrieval-Augmented Generation engine with agent capabilities | | Stars | 54,613 | 84,818 | | Forks | 24,713 | 9,905 | | Open issues | 997 | 2,302 | | Language | TypeScript | Go | | Adopt for | Flowise enables users to build AI agents visually, focusing on a low-code/no-code approach with support for technologies like langchain and large-language-models. | 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. | | Persona | - | - | | Runtime | - | - | | License | Other | Apache-2.0 License | | Categories | AI Agents, Developer Tools | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Flowise](/tools/flowiseai-flowise.md) | [ragflow](/tools/infiniflow-ragflow.md) | | --- | --- | --- | | Open issues (now) | 997 | 2.3k | | Full report | [trust report](/tools/flowiseai-flowise/trust.md) | [trust report](/tools/infiniflow-ragflow/trust.md) | **Typed relationship:** Flowise _(alternative)_ ragflow Both Flowise and RAGFlow enable the creation of AI Agents but through different visual or programming approaches, making them alternatives in the context of building agentic applications. ## Decision facts: Flowise - **Requirements:** Requires Docker; Can be installed locally using NodeJS or Docker Compose. Supports various hosting options including cloud platforms and personal servers. - **Adopt for:** Flowise enables users to build AI agents visually, focusing on a low-code/no-code approach with support for technologies like langchain and large-language-models. ## 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 ## Choose when ### Choose Flowise if… - Flowise is primarily TypeScript; ragflow is Go. - License: Flowise is Other, ragflow is Apache-2.0. - Requirements: Requires Docker; Can be installed locally using NodeJS or Docker Compose. Supports various hosting options including cloud platforms and personal servers.. - Both Flowise and RAGFlow enable the creation of AI Agents but through different visual or programming approaches, making them alternatives in the context of building agentic applications. - Tags unique to Flowise: large language models, low-code, multiagent-systems, no-code. - Also covers Developer Tools. - Flowise enables users to build AI agents visually, focusing on a low-code/no-code approach with support for technologies like langchain and large-language-models. ### Choose ragflow if… - ragflow is primarily Go; Flowise is TypeScript. - License: ragflow is Apache-2.0, Flowise is Other. - Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.. - Both Flowise and RAGFlow enable the creation of AI Agents but through different visual or programming approaches, making them alternatives in the context of building agentic applications. - Tags unique to ragflow: context management, rag, retrieval-augmented-generation. - Also covers Data & Retrieval. - - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ## When NOT to use Flowise - AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. - Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ## 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. ## Common questions ### What is the difference between Flowise and ragflow? Flowise: Build AI Agents, Visually. ragflow: Retrieval-Augmented Generation engine with agent capabilities. See the comparison table for live GitHub stats and shared categories. ### When should I choose Flowise over ragflow? Choose Flowise over ragflow when Flowise is primarily TypeScript; ragflow is Go; License: Flowise is Other, ragflow is Apache-2.0; Requirements: Requires Docker; Can be installed locally using NodeJS or Docker Compose. Supports various hosting options including cloud platforms and personal servers.; Both Flowise and RAGFlow enable the creation of AI Agents but through different visual or programming approaches, making them alternatives in the context of building agentic applications; Tags unique to Flowise: large language models, low-code, multiagent-systems, no-code; Also covers Developer Tools; Flowise enables users to build AI agents visually, focusing on a low-code/no-code approach with support for technologies like langchain and large-language-models. ### When should I choose ragflow over Flowise? Choose ragflow over Flowise when ragflow is primarily Go; Flowise is TypeScript; License: ragflow is Apache-2.0, Flowise is Other; Requirements: Requires Docker; Docker image size is approximately 2 GB; build process requires access to external LLM and embedding services.; Both Flowise and RAGFlow enable the creation of AI Agents but through different visual or programming approaches, making them alternatives in the context of building agentic applications; Tags unique to ragflow: context management, rag, retrieval-augmented-generation; Also covers Data & Retrieval; - You need an integrated RAG system with AI agent capabilities for better context management in your applications. ### When should I avoid Flowise? AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. Developer Tools: A gateway is overkill when you're pinned to a single provider and model. ### 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. ### Is Flowise or ragflow more popular on GitHub? ragflow has more GitHub stars (84,818 vs 54,613). Stars measure visibility, not whether either tool fits your constraints. ### Are Flowise and ragflow open source? Yes - both are open-source projects on GitHub (Flowise: Other, ragflow: Apache-2.0). ### Where can I find alternatives to Flowise or ragflow? GraphCanon lists graph-backed alternatives at [Flowise alternatives](/tools/flowiseai-flowise/alternatives) and [ragflow alternatives](/tools/infiniflow-ragflow/alternatives) ([Flowise markdown twin](/tools/flowiseai-flowise/alternatives.md), [ragflow markdown twin](/tools/infiniflow-ragflow/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/flowiseai-flowise-vs-infiniflow-ragflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Flowise or ragflow? Flowise: Very active. ragflow: 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 Flowise and ragflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Flowise trust report](/tools/flowiseai-flowise/trust); [ragflow trust report](/tools/infiniflow-ragflow/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=flowiseai-flowise`](/api/graphcanon/graph?tool=flowiseai-flowise) - 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/_