RAG Frameworks and Orchestration

DSPy is a framework for programming and optimizing language-model systems whose retrieval modules and optimizers can tune multi-stage RAG pipelines against an objective.

Embedchain is an open-source RAG framework that loads, chunks, embeds, indexes, and synchronizes unstructured data for question answering and personalized AI applications.

FlashRAG is a research toolkit that implements multiple RAG methods, datasets, retrieval backends, generators, and evaluation workflows behind a common Python interface.

Haystack is deepset's component-based framework for production search and RAG pipelines, with typed components for conversion, preprocessing, embedding, retrieval, ranking, routing, and generation.

LangChain is an application framework with retriever abstractions, document loaders, vector-store integrations, retrieval chains, and middleware for building RAG and agent workflows in Python and JavaScript.

LangGraph is a low-level orchestration framework for stateful agents whose graphs can implement iterative retrieval, query routing, corrective RAG, and human review.

Langroid is a multi-agent LLM framework whose document-chat agents provide parsing, chunking, vector storage, relevance extraction, citations, and conversational RAG.

LlamaIndex is a data framework for context-augmented applications with indexes, retrievers, query engines, data connectors, agents, and managed services available through LlamaCloud.

Pathway is a Python data-processing framework for live, incremental pipelines that supports continuously updated RAG indexes, document parsing, vector search, and streaming connectors.

Quivr is an opinionated RAG framework for integrating ingestion, retrieval, generation, and configurable workflows into applications through Python and API interfaces.

R2R is an open-source retrieval system and API for multimodal ingestion, hybrid search, knowledge graphs, agents, and RAG, paired with hosted and enterprise offerings.

RAGFlow is an open-source RAG engine with document understanding, template-based chunking, hybrid retrieval, agent workflows, and a commercial cloud service.

RAGLite is a compact Python toolkit for PostgreSQL-based RAG with document ingestion, hybrid retrieval, reranking, prompt assembly, and evaluation-oriented metadata.

Semantic Kernel is Microsoft's SDK for composing models, plugins, memory, vector stores, and process orchestration in C#, Python, and Java applications.

Spring AI is a Spring-ecosystem application framework with portable model, embedding, vector-store, document-reader, text-splitter, advisor, and retrieval-augmentation APIs.

txtai is an all-in-one embeddings database and semantic-workflow framework that combines vector search, SQL, graph traversal, topic modeling, extractive QA, and RAG pipelines.