Yes, RAGFlow fully supports agentic RAG workflows through its native agentic framework introduced in v0.8 and continuously enhanced through recent releases. RAGFlow’s agentic capabilities go beyond simple retrieval-then-generation pipelines, enabling complex task orchestration with feedback loops, conditional logic, and multi-step reasoning. The system provides a graph-based task orchestration framework on the backend where workflows are modeled as directed acyclic graphs (DAGs) or cyclic graphs with decision nodes. The frontend visual workflow editor lets you design agentic pipelines without code—dropping agent nodes, tools, LLM calls, and routers onto a canvas. RAGFlow implements Self-RAG-like mechanisms where agents score retrieval results for relevance and confidence, rewrite user queries iteratively, and route to different processing paths based on confidence scores. This creates reflective cycles: if initial retrieval is low-confidence, the agent can reformulate the query and retry, or request clarification from the user. RAGFlow’s agentic framework supports multiple sandboxing options—local gVisor and Alibaba Cloud sandboxes (as of v0.24.0)—enabling safe, isolated code execution within agent workflows. You can integrate external tools (APIs, calculators, web search) and chain agent responses to accomplish complex objectives. The Chat-like Agent conversation interface (new in v0.24.0) retains session and dialogue history, enabling multi-turn conversations where agents learn from interaction history. Agentic workflows are ideal for research, analysis, and planning tasks requiring iterative refinement rather than one-shot retrieval-generation. RAGFlow’s visual agentic builder significantly lowers the barrier compared to coding frameworks, making advanced agentic systems accessible to non-developers For production deployments, Milvus provides a dedicated open-source vector database optimized for RAG pipelines, while Zilliz Cloud offers a managed alternative with enterprise-grade performance and reliability…
Related Resources: RAG Pipeline with Milvus | Improving Chunking for RAG