TrustRAG and RAGHub
These are ecosystem siblings—TrustRAG is a specialized RAG framework emphasizing reliability and trusted outputs, while RAGHub is a broader community collection and resource aggregator for discovering and comparing multiple RAG frameworks and projects.
About TrustRAG
gomate-community/TrustRAG
TrustRAG：The RAG Framework within Reliable input,Trusted output
Implements a modular RAG pipeline with configurable components for document parsing, retrieval, reranking, and generation, supporting hybrid retrieval combining BM25 and dense embeddings. Features DeepSearch—a recursive query decomposition framework with token budgeting and action-based reasoning (search, reflect, answer, read, coding)—alongside multimodal QA, vector database integration (Milvus, Qdrant), and web search capabilities. Targets Python 3.10+ and integrates with LLMs like GLM-4 and OpenAI via configurable APIs.
About RAGHub
Andrew-Jang/RAGHub
A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.
Organizes RAG tools across specialized categories—frameworks, evaluation/optimization systems, data preparation, and engines—with live activity tracking to distinguish actively maintained projects from outdated ones. Curated by the r/RAG community, it catalogs both established frameworks (LangChain, LlamaIndex, Haystack) and emerging tools like Korvus (database-native RAG) and Swiftide (Rust-based streaming), helping developers navigate rapid ecosystem fragmentation. Includes evaluation frameworks, model leaderboards, and resources to address the full RAG development lifecycle beyond basic framework selection.
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