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ragflow
by infiniflow
RAG engine that fuses retrieval with agentic workflows
Python
Updated Feb 12, 2026
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How It Works
Builds a retrieval-augmented generation (RAG) engine that couples document retrieval with agentic workflows. It combines retrievers, prompt templates, and agent orchestration to provide a context layer that agents can query and act on. Key features include document parsing, multi-step agent pipelines, and adapters for OpenAI and Ollama backends OpenAI and Ollama backends.
Key Benefits
As agents rely more on external knowledge, the quality and provenance of retrieved context becomes a central trust signal. RAGFlow gives teams a reproducible way to surface and manage context used by agents, enabling clearer attribution of where answers come from. For multi-agent systems this matters because reliable retrieval reduces failure cascades and makes agent behavior easier to evaluate and audit.
Target Use Cases
Teams building agent-driven applications that need robust retrieval, document understanding, and agent orchestration for production LLM contexts production LLM contexts.
Real-World Examples
- Create document-aware agents that consult and update a knowledge layer before responding
- Build multi-step agent pipelines that combine retrieval, reasoning, and action
- Standardize provenance and context for LLM responses to improve auditability and debugging
Works With
openaiollamalangchainhuggingface
Topics
agentagenticagentic-aiagentic-workflowaiai-searchdeep-learningdeep-researchdeepseekdeepseek-r1+10 more
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Keywords
retrieval-augmented-generationmulti-agent orchestrationagentic-workflow