Get a superior context layer for LLMs with RAGFlow, a retrieval-augmented generation engine, ideal for founders in AI, with 82k+ GitHub stars.
81,981 stars9,446 forksPythonQuality 9/10Updated 6/5/2026100% free ยท open source
What it does
RAGFlow supercharges your LLM context management by fusing Retrieval-Augmented Generation with advanced agent capabilities, creating a superior context layer for LLMs
โขYou need to improve the context understanding of your LLM application
โขYou want to integrate Retrieval-Augmented Generation with agent capabilities
โขYou're looking for an open-source AI infrastructure to power your LLM context management
Quick start
1Clone the RAGFlow repository using the command `git clone https://github.com/infiniflow/ragflow.git`
2Navigate to the RAGFlow directory using `cd ragflow`
3Run the command `python -m ragflow` to start the RAGFlow server
4Configure your RAGFlow instance by editing the `config.py` file
5Test your RAGFlow setup using the example queries provided in the `examples` directory
Ready-to-paste prompt
python -m ragflow --query 'What are the latest developments in AI research?' --index 'my_index' --agent 'my_agent'
Heads up: Make sure you have the required Python version (3.8 or later) and the necessary dependencies installed, as specified in the RAGFlow README, before attempting to install or run RAGFlow
Saves to your device
Topics
agentic-ai
agentic-retrieval
agentic-search
ai
ai-agents
context-engine
context-management
llm-apps
rag
retrieval-augmented-generation
What's inside โ free to inspect
No purchase needed
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