Retrieval-augmented generation is simple in theory: retrieve relevant documents, stuff them into a prompt, get a grounded answer. In practice, the retrieval step is where most RAG applications fail. The documents retrieved are not relevant enough, the chunks are poorly sized, the re-ranking is absent, and the answer quality suffers. RAG frameworks exist to make the retrieval step configurable, testable, and improvable.

Three frameworks have emerged as serious options: LlamaIndex, Haystack, and Semantic Kernel. They all do retrieval-augmented generation. They differ in philosophy, flexibility, and the kind of developer they are designed for.

LlamaIndex: Data-First RAG

LlamaIndex was built specifically for RAG. Its core abstraction is the index — a data structure built from your documents that supports efficient retrieval. Vector indexes, tree indexes, keyword indexes, knowledge graph indexes — LlamaIndex provides multiple indexing strategies and lets you choose the one that fits your data.

The indexing options are LlamaIndex’s primary strength. Most RAG applications default to chunk-embed-store: split documents into chunks, embed each chunk, store the embeddings in a vector database, and retrieve by similarity. This approach works for many cases but fails when the question requires information that spans multiple chunks or when the document structure (tables, hierarchies, relationships) carries meaning that chunking destroys.

LlamaIndex’s tree index, for example, builds a hierarchical summary of your documents and retrieves at the appropriate level of granularity. A question about a specific fact retrieves from the leaf level. A question about the overall theme retrieves from a higher level. This hierarchical retrieval produces better answers for complex questions than flat vector search.

The knowledge graph index extracts entities and relationships from your documents and builds a graph. Retrieval then traverses the graph to find relevant context, which handles questions about relationships between entities (“how does X relate to Y?”) that vector search misses entirely.

LlamaIndex’s limitation is that the framework assumes you want to optimize retrieval. If your RAG application is simple — chunk, embed, retrieve, answer — LlamaIndex’s additional indexing strategies add complexity without proportional value. The framework’s documentation is extensive but sprawling, and the API surface area is large enough that new users often feel overwhelmed.

LlamaIndex’s integration with vector databases is broad. Pinecone, Weaviate, Qdrant, Milvus, Chroma, and others are supported through connectors. The embedding model integrations cover OpenAI, Cohere, Hugging Face, and local models. The flexibility is there, but the configuration surface for connecting all the pieces is large.

Haystack: Pipeline-First RAG

Haystack (by deepset) approaches RAG as a pipeline problem. Instead of focusing on indexing strategies, Haystack focuses on the pipeline that connects retrieval to generation. Each step in the pipeline — document loading, splitting, embedding, retrieval, re-ranking, prompting, generation — is a component that you connect together.

The pipeline architecture is Haystack’s strength. You can swap components without changing the rest of the pipeline. Want to change from dense retrieval to hybrid retrieval? Replace the retriever component. Want to add a re-ranking step? Insert a re-ranker component between retrieval and generation. The composability is clean because each component has a well-defined interface.

Haystack’s evaluation capabilities are more mature than LlamaIndex’s. You can define evaluation datasets, run your pipeline against them, and measure retrieval quality (precision, recall, MRR) and generation quality (faithfulness, relevance, correctness) separately. This separation matters because improving retrieval and improving generation require different interventions.

Haystack’s production deployment story is stronger than LlamaIndex’s. deepset Cloud provides a managed platform for deploying and monitoring Haystack pipelines, with built-in support for A/B testing, caching, and scaling. If you want to go from prototype to production on a managed platform, Haystack’s deployment path is more complete.

The limitation is that Haystack’s indexing options are narrower than LlamaIndex’s. The default is chunk-embed-retrieve, with support for sparse retrieval (BM25) and hybrid retrieval (combining dense and sparse). The hierarchical and knowledge graph indexing strategies that LlamaIndex offers are not part of Haystack’s core. You can build them as custom components, but they are not first-class.

Haystack’s Python API is cleaner and more consistent than LlamaIndex’s, which makes it easier to learn for teams that are new to RAG. The documentation is more focused because the feature set is more focused. The trade-off is that advanced retrieval strategies require more custom work.

Semantic Kernel: Microsoft’s Integration Play

Semantic Kernel is Microsoft’s entry into the RAG framework space. It is not specifically a RAG framework — it is an SDK for building AI applications that integrates with the Microsoft ecosystem. Azure AI Search for retrieval, Azure OpenAI for generation, Azure AI Studio for management.

The Microsoft integration is Semantic Kernel’s defining feature. If your organization runs on Azure, Semantic Kernel provides the most natural path to RAG. Azure AI Search handles indexing and retrieval with enterprise features (access control, compliance, geo-replication) that self-managed vector databases lack. Azure OpenAI provides the generation step with SLAs and compliance guarantees.

Semantic Kernel’s “planner” concept adds agentic capabilities on top of RAG. Instead of a fixed retrieve-and-answer pipeline, the planner can decide which tools to call, whether to retrieve additional context, and how to structure the answer. This makes Semantic Kernel more suitable for complex question-answering workflows where a single retrieval pass is insufficient.

The limitation is Azure coupling. Semantic Kernel works outside Azure — you can use it with OpenAI’s API directly, with other vector databases, and with non-Microsoft embedding models — but the deepest integrations and the most complete feature set are Azure-specific. If you are not on Azure, much of what makes Semantic Kernel valuable is unavailable.

The RAG-specific features (indexing strategies, retrieval quality, chunking options) are less mature than LlamaIndex’s or Haystack’s. Semantic Kernel’s retrieval abstraction is thinner because it delegates to Azure AI Search for the heavy lifting. If your primary concern is optimizing retrieval quality, Semantic Kernel gives you fewer knobs to turn.

Retrieval Quality: Where It Matters Most

The quality of your RAG application is bounded by the quality of your retrieval. All three frameworks support the standard retrieval flow: chunk documents, embed chunks, store in a vector database, retrieve by similarity. The differences emerge in what they offer beyond this baseline.

LlamaIndex offers the most retrieval strategies: vector search, keyword search, tree-based retrieval, knowledge graph retrieval, and combinations of these. If your documents are complex (technical documentation with tables and hierarchies, legal documents with cross-references, research papers with citations), LlamaIndex’s advanced retrieval strategies can significantly improve answer quality.

Haystack offers the best pipeline for iterating on retrieval quality. The evaluation framework lets you measure retrieval precision and recall independently of generation quality, which makes it easier to diagnose whether a poor answer is a retrieval problem or a generation problem. The re-ranking components are mature and easy to integrate.

Semantic Kernel offers the best integration with enterprise search infrastructure. Azure AI Search provides features that self-managed vector databases lack: access control (only retrieve documents the user is authorized to see), built-in OCR for scanned documents, and linguistic analysis for non-English languages. If these features matter, Semantic Kernel’s retrieval foundation is stronger even if the framework’s own retrieval options are thinner.

Decision Framework

Use LlamaIndex when retrieval quality is your primary concern and your documents are complex enough to benefit from advanced indexing strategies. Best for teams that are willing to invest time in optimizing the retrieval step and need hierarchical or knowledge-graph-based retrieval.

Use Haystack when you want a clean pipeline architecture, need strong evaluation capabilities, and prefer a more opinionated framework with a smaller API surface. Best for teams that value the ability to swap components and iterate on pipeline quality systematically.

Use Semantic Kernel when your organization is on Azure and needs enterprise features (access control, compliance, SLAs) in the retrieval layer. Best for teams that want the fastest path to production within the Microsoft ecosystem and are willing to accept the Azure coupling.

For teams that are not on Azure and want maximum control over retrieval quality, LlamaIndex is the strongest starting point. For teams that want a cleaner developer experience and strong evaluation tooling, Haystack is the better fit. The right framework is the one that matches your infrastructure, your team’s experience, and the complexity of your retrieval problem.