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Atlas by Comet Lab
Capability layer

RAG frameworks and engines, compared

RAG tools ground a model's answers in your own documents and data: they ingest, index and retrieve the right passages before the model answers. The first choice is between a framework you build with and a finished app you run.

Learn the idea RAG vs. fine-tuning vs. prompting on Horizon

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At a glance

Every tool in this layer, with where it fits and the facts that usually decide it.

Tool Where it fits Licence Self-hostable Hosted API Maturity
LlamaIndex RAG data framework The right framework when retrieval quality is the hard part of the project and you want deep control over ingestion and indexing. Open source Yes Yes Established
Cognita RAG framework A fit when a RAG prototype needs to become a structured, maintainable production system without a rewrite. Open source Yes No Growing
Haystack RAG pipeline framework A good fit when a RAG system is non-trivial and you want every stage as an explicit, testable, swappable component. Open source Yes No Established
LLMWare Enterprise RAG framework A fit for enterprise RAG where small, self-hosted models and an end-to-end pipeline matter more than frontier-model quality. Open source Yes No Growing
PrivateGPT Offline RAG A fit when the absolute requirement is that documents and questions never touch a third-party service. Open source Yes Yes Established
Quivr RAG framework A fit when developers want a lightweight, opinionated RAG core to embed without designing a pipeline from scratch. Open source Yes No Growing
R2R RAG engine A fit when you want a deployable retrieval service (API, ingestion, and agentic search included) rather than a toolkit. Open source Yes Yes Growing
RAGFlow RAG application Strong when source documents are messy and layout-heavy, and you want a self-hosted, ready-to-run RAG app rather than a toolkit. Open source Yes No Growing
txtai Embeddings database A fit for Python projects that want semantic search and RAG from one self-contained library, without extra infrastructure. Open source Yes No Established
Verba RAG application A fit when you want a self-hosted document chatbot running quickly, especially if you already use Weaviate. Open source Yes No Growing

How to choose

Start from your situation. Where a line says “our”, it comes from Comet Lab’s own builds.

  1. Retrieval quality decides the project

    Deep control over ingestion and indexing, and loaders for messy sources. Our default when a project lives or dies on retrieval.

  2. The pipeline has several stages to test and swap

    Haystack makes every stage an explicit component; Cognita turns a prototype into a maintainable production system without a rewrite.

  3. You want a ready-to-run app, not a toolkit

    RAGFlow handles messy, layout-heavy documents; Verba suits teams already on Weaviate; R2R runs as a retrieval service with its own API.

  4. Nothing may touch a third-party service

    PrivateGPT answers questions over your documents fully offline; LLMWare builds enterprise pipelines around small, self-hosted models.

  5. Semantic search inside one Python project

    txtai is one self-contained library with no extra infrastructure; Quivr is an opinionated RAG core to embed.