ragflow
RAGFlow is an open-source Retrieval-Augmented Generation (RAG) engine that combines document understanding, intelligent chunking, and agent capabilities to build production-ready AI systems. It handles complex, unstructured data from multiple formats and provides grounded citations with reduced hallucinations.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | infiniflow/ragflow |
| Owner | infiniflow |
| Primary language | Go |
| License | Apache-2.0 — OSI-approved |
| Stars | 84.6k |
| Forks | 9.9k |
| Open issues | 2.3k |
| Latest release | v0.26.4 (2026-07-07) |
| Last updated | 2026-07-08 |
| Source | https://github.com/infiniflow/ragflow |
What ragflow is
RAGFlow fuses RAG with agentic workflows using a converged context engine, supporting multi-modal document parsing (MinerU, Docling), template-based intelligent chunking, multiple recall strategies with re-ranking, configurable LLM/embedding models, and integration with external data sources (Confluence, S3, Notion, Discord, Google Drive). Primary language is Go; supports Python >= 3.13, Docker >= 24.0.0, and gVisor for sandboxed code execution.
Get the ragflow source
Clone the repository and explore it locally.
git clone https://github.com/infiniflow/ragflow.gitcd ragflow# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Configure vm.max_map_count >= 262144 at OS level (Elasticsearch requirement); persistence requires updating /etc/sysctl.conf for production stability.
- Select and configure LLM/embedding models upfront; RAGFlow supports multiple providers but integration depends on API keys, rate limits, and model availability.
- If using code executor (sandbox) feature, gVisor must be installed separately; adds operational overhead for secure Python/JavaScript execution.
- Document parsing quality improves with template-based chunking configuration; manual tuning and testing on representative samples recommended before production rollout.
- Plan for persistent storage of ingestion state and embeddings; Docker-based deployment requires backing external volume mounts or cloud storage for fault tolerance.
When to avoid it — and what to weigh
- ARM64 or Non-x86 Deployments Without Custom Builds — Pre-built Docker images support x86 only. ARM64 users must build custom images from source, adding deployment complexity and maintenance burden.
- Minimal Resource Environments — Requires CPU >= 4 cores, RAM >= 16 GB, and disk >= 50 GB minimum. Not suitable for lightweight edge devices or serverless functions with tight memory constraints.
- Strict Regulatory/Closed-Source Requirements — Apache-2.0 license requires derivative works to disclose source; unsuitable for organizations mandating closed proprietary systems or those with extreme IP protection needs.
- Real-Time, Sub-Second Latency Demands — RAGFlow is optimized for document understanding and chunking workflows; latency characteristics for real-time inference not specified in available documentation.
License & commercial use
Apache-2.0 (Apache License 2.0). Permissive OSI-approved license allowing commercial use, modification, and distribution under stated conditions (attribution, license notice, and change documentation required).
Apache-2.0 permits commercial use and proprietary derivative works. However, any distributed modifications must include license notices and state changes. Internal use for commercial purposes is unrestricted. Verify compliance with your legal team if integrating into closed-source products or if modifications are extensive. No commercial warranty or indemnification is provided; relies on community support.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Code executor (Python/JavaScript sandbox via gVisor) introduces attack surface; gVisor isolation quality depends on kernel and configuration. Document parsing from untrusted sources (web, uploads) may expose vulnerabilities in MinerU/Docling libraries; keep dependencies patched. Multi-model and multi-source integration increases credential and API exposure surface. Data ingestion from external sources (S3, Notion, Google Drive) requires robust authentication and access control. No formal security audit data provided; evaluate risk posture based on your threat model.
Alternatives to consider
Langchain / LlamaIndex
Lightweight Python libraries for RAG pipelines; lower resource footprint but require more custom orchestration and lack enterprise-grade document parsing.
Vespa (Yahoo Open-Source Search)
Full-stack retrieval engine with ML-powered ranking; mature and highly scalable but steeper learning curve and less focus on agentic workflows.
Milvus (Open-Source Vector DB) + Custom Orchestration
Pure vector database; provides control and cost efficiency but shifts responsibility for chunking, LLM integration, and agent orchestration to you.
Build on ragflow with DEV.co software developers
Evaluate self-hosting vs. cloud options, verify infrastructure readiness (4+ cores, 16 GB RAM), and plan your LLM/embedding model strategy. Contact our engineering team to assess fit for your use case.
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ragflow FAQ
Can I deploy RAGFlow on ARM64?
What LLMs and embedding models does RAGFlow support?
Is RAGFlow suitable for small teams or prototyping?
What happens to data stored in RAGFlow if the project becomes unmaintained?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If ragflow is part of your rag frameworks roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy RAGFlow?
Evaluate self-hosting vs. cloud options, verify infrastructure readiness (4+ cores, 16 GB RAM), and plan your LLM/embedding model strategy. Contact our engineering team to assess fit for your use case.