Use case · decision ranking
Document question answering
Answer questions using retrieved context from documents or knowledge sources.
5 reviewed matches, ranked by fit and deterministic project health.
#1
Editorialrun-llama/llama_index: LlamaIndex is the leading document agent and OCR platform.
Editorial88% fitRAG frameworks can support grounded question answering when ingestion and retrieval quality meet the workload needs.Compare this repository → ★ 51.9KPythonMITrag
#2
Editorialtopoteretes/cognee: Cognee is the open-source AI memory platform for agents. Give your AI agents persistent long-term memory across sessions with a self-hosted knowledge graph engine.
Editorial88% fitRAG frameworks can support grounded question answering when ingestion and retrieval quality meet the workload needs.Compare this repository → ★ 30.2KPythonApache-2.0rag
#3
Editorialneuml/txtai: 💡 All-in-one AI framework for semantic search, LLM orchestration and language model workflows.
Editorial88% fitRAG frameworks can support grounded question answering when ingestion and retrieval quality meet the workload needs.Compare this repository → ★ 12.9KPythonApache-2.0rag
#4
Editorialpathwaycom/pathway: Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Editorial88% fitRAG frameworks can support grounded question answering when ingestion and retrieval quality meet the workload needs.Compare this repository → ★ 62.4KPythonNOASSERTIONdata-extractionrag
#5
Editorialllmware-ai/llmware: Unified framework for building enterprise RAG pipelines with small, specialized models.
Editorial88% fitRAG frameworks can support grounded question answering when ingestion and retrieval quality meet the workload needs.Compare this repository → ★ 14.8KPythonApache-2.0rag