When it comes to building intelligent workflows and retrieval-augmented applications, having the right tools can make all the difference. With new platforms emerging rapidly in the AI ecosystem, it’s essential to know what each brings to the table. In this benchmark, we put two leading open-source solutions—Langflow and RAGFlow—side by side, highlighting how they approach visual workflow design, multi-agent orchestration, integration options, document handling, and more. Whether you’re a developer seeking a flexible AI pipeline builder or an enterprise looking for robust knowledge management, this comparison aims to clarify their strengths and help you find the best fit for your next project.
| Feature | Langflow | RAGFlow |
|---|---|---|
| Category | AI workflow builder | Retrieval-Augmented Generation Engine |
| Open Source License | Open Source | Open source (see GitHub repo for details) |
| Visual Workflow/Agent Builder | Drag-and-drop visual editor for workflows and agents | Visual/no-code workflow and agent builder |
| Main Features | Visual workflow editor, multi-agent orchestration, supports all major LLMs and vector databases, custom components, MCP protocol support, API server, rapid prototyping | Deep document understanding, multi-format support, knowledge base management, citation-backed Q&A, configurable LLM integration, agent and workflow orchestration, chunking and indexing, API access, multi-agent support, cross-language queries, visual chunk editing, multi-modal and web search, explainability, advanced retrieval and re-ranking |
| Supported LLMs | All major LLMs (generic support) | OpenAI (GPT-3.5, GPT-4, GPT-5), Anthropic Claude, Cohere, Ollama, Xinference, LocalAI, Kimi K2, Grok 4, Qwen, and more |
| Supported Vector Databases | Astra DB, MongoDB, Pinecone, Oracle, and others | Elasticsearch (default), Infinity (AI-native DB), text and vector storage |
| Supported Data Formats | Not specified (focuses on workflow and agent composition) | PDF, DOC, DOCX, TXT, MD, MDX, PPT, PPTX, CSV, XLSX, XLS, JPEG, JPG, PNG, TIF, GIF, web pages, scanned images |
| Deployment Options | Desktop app (Windows/macOS), Docker, Python package (pip/uv), cloud, MCP server | Docker-based, cloud and local deployment, x86 (official), ARM (manual build), integration with Elestio and other platforms |
| Integration & Extensibility | Connects to any data source, vector database, external APIs, supports custom and pre-built components | Integrates with Langfuse, Elestio, HuggingFace models, Ollama, Open WebUI, Python SDK |
| API Access | API server | HTTP API and Python SDK (ragflow-sdk) |
| Custom Component/Extension Support | Full Python code access, custom and reusable components, flow export/import as JSON, extendable with user components | Configurable chunking, embedding models, knowledge base settings, prompt engineering, advanced document parsing |
| Collaboration & Sharing | Flows can be shared and collaboratively developed, prebuilt templates and flows available | Not specified |
| Observability & Monitoring | Integrates with LangSmith, LangFuse for monitoring | Integrated with Langfuse for monitoring, debugging, and trace inspection |
| Security & Sandboxing | Critical vulnerabilities patched; no sandbox for code execution | gVisor for sandboxed code execution |
| Cloud Support | Enterprise-grade, secure cloud deployment, free tier available | Cloud deployment supported (self-hosted free, optional providers via Elestio) |
| Target Audience | Developers, data scientists, AI engineers, enterprises building AI-powered apps | Enterprise search, research, knowledge assistants, customer support, workflow automation |
| Citation/Reference Support | Not specified | Traceable citations and references for grounded answers |
| Platform/OS Support | Desktop (Windows/macOS), Docker, cloud | Linux, Windows (WSL2), Mac (Apple Silicon via emulated x86_64 containers) |
| Documentation & Community | https://docs.langflow.org/, welcomes developer contributions via GitHub, active enterprise and OSS user base | https://github.com/infiniflow/ragflow/tree/main/docs, active open-source community (GitHub, Discord), contribution guidelines |
| Pricing | Open source, free tier available for cloud | Free (self-hosted); optional cloud providers/support via Elestio |
| Release Date | 2023 | Not specified |
Which One Should You Choose?
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Langflow is for you if:
- You want a highly visual, modular way to design AI workflows and agents from scratch or with templates.
- You’re building complex, multi-agent systems and need broad support for LLMs and vector databases.
- You value extensibility—custom Python components, API access, and easy integration with your data sources or existing tools.
- Collaboration and sharing of flows within a team is important to your project.
- You prefer desktop apps or need secure, enterprise-ready cloud options with a free tier.
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RAGFlow is for you if:
- Your main goal is advanced document understanding, enterprise search, or building knowledge assistants with reliable sources.
- You need to work with a wide variety of document formats, including scanned images and multi-modal data.
- Traceable citations, explainability, and retrieval-augmented generation are essential for your use case.
- You want sandboxed code execution for security, or need integration with tools like HuggingFace, Ollama, or Elestio.
- You prefer a Docker-based setup, robust monitoring, and free self-hosting, with optional cloud support.
Both Langflow and RAGFlow are open source, have active communities, and support modern LLM workflows. Your choice comes down to whether you prioritize flexible workflow building and team collaboration (Langflow), or deep document processing and grounded, explainable answers (RAGFlow).
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