Retrieval-augmented generation (RAG) is the pattern that lets an LLM answer questions using your documents instead of (or in addition to) whatever it memorized during training: you retrieve relevant chunks from an index, stuff them into the prompt, and let the model generate a grounded answer. Three open-source frameworks dominate how developers actually build this in 2026: LangChain, LlamaIndex, and Haystack. They overlap heavily, but they were built with different centers of gravity — and that shows up in how painless each one makes a RAG pipeline versus a general agent. This is a RAG-specific comparison. If you’re choosing a framework for autonomous multi-step agents rather than document retrieval, see our separate comparison of LangGraph, CrewAI, and AutoGen. And if you want the mechanics of how the vector search underneath any of these three actually works, we cover that in our explainer on vector databases and ANN search.
At a Glance
| Framework | License | Open Source | Best For |
|---|---|---|---|
| LangChain | MIT | Yes | General-purpose chain and agent orchestration, with RAG as one capability among many; best if you’ll also need tool-calling agents, not just retrieval |
| LlamaIndex | MIT | Yes | Data-centric, retrieval-first applications where ingesting, indexing, and querying your own documents is the primary job |
| Haystack | Apache 2.0 | Yes | Production NLP/RAG pipelines built by deepset, with an emphasis on modular, serializable pipelines for deployment at scale |
Short Verdict
All three are solid, genuinely open-source choices, and the “right” one depends on what your RAG pipeline needs to do beyond retrieval. LlamaIndex is the most retrieval-native of the three — its APIs are built around getting documents in and answers out, with layered high-level and low-level interfaces for customizing the retrieval step. LangChain is the most general-purpose: it treats RAG as one tool an agent can reach for, built on top of LangGraph for orchestration, which makes sense if your end goal is an agent that sometimes retrieves and sometimes does other things. Haystack sits closest to a production-engineering mindset, with serializable, modular pipelines, Kubernetes support, and hybrid retrieval with self-correction loops baked in — a good fit if you’re deploying a RAG system as a long-lived service rather than prototyping an app.
What Each One Is Optimized For
LangChain: Agent and Chain Orchestration First, RAG Included
LangChain’s own documentation frames the framework around `create_agent`, described as “a minimal, highly configurable harness” for composing a model, tools, a prompt, and middleware. Agents built this way run on LangGraph underneath, which provides durable execution, human-in-the-loop support, and persistence. RAG in LangChain is implemented as retrievers and vector store integrations that an agent (or a simpler chain) can call as a tool, rather than being the framework’s sole purpose. That said, LangChain’s retriever and vector-store abstractions are extensive: its current integrations list includes Pinecone, Chroma, Qdrant, Weaviate, Milvus, PGVector, Redis, Elasticsearch, Astra DB, Azure Cosmos DB, Google AlloyDB, FAISS, Neo4j, and dozens more, which makes swapping the underlying store a low-friction decision.
LlamaIndex: Data Indexing and Retrieval First
LlamaIndex describes itself as a framework for building LLM-powered agents and applications “over your own data,” and its documentation is explicit that the framework is built around getting external data into a model at answer time — RAG is its best-known pattern, though the docs now present it as one tool an agent can call rather than the only thing the framework does. Its core primitives are data connectors, indexes, vector stores, and query/chat engines, offered through both a high-level API (documents to answers in a few lines) and a lower-level API for customizing or replacing individual components. For messy source documents, LlamaIndex’s companion product LlamaParse handles complex parsing — scanned PDFs, tables, embedded charts — and feeds parsed output straight into an index. Its documented vector store integrations include Pinecone, Qdrant, Chroma, Weaviate, Milvus, Elasticsearch, and multiple pgvector-based options.
Haystack: Production Pipelines by deepset
Haystack is maintained by deepset GmbH and is described on its own site as “Open Source AI framework for building production-ready, LLM-powered agents and applications,” built around modular components, serializable pipelines, and Kubernetes support for production deployment. Its RAG capabilities are explicitly called out as “Advanced RAG,” including hybrid retrieval and self-correction loops, alongside standardized tool calling for agents and support for multimodal tasks like image processing and audio transcription. Haystack’s document-store integration list is the broadest of the three by raw count: dedicated vector databases (Pinecone, Qdrant, Weaviate, Chroma, Milvus, LanceDB, FAISS, Vespa, Upstash Vector, Amazon S3 Vectors), relational stores with vector support (pgvector, Supabase, AlloyDB, MariaDB, Oracle AI Vector Search, SingleStore), search engines (Elasticsearch, OpenSearch, Solr, Azure AI Search), and several cloud/NoSQL databases (AstraDB, DynamoDB, Azure CosmosDB, MongoDB, Neo4j, and others).
Licensing and Paid Hosted Offerings
| Framework | Core License | GitHub Stars (approx.) | Paid Hosted Offering |
|---|---|---|---|
| LangChain | MIT | ~149k (langchain-ai/langchain) | LangSmith (observability/evals, free dev tier then $39/seat/mo+) and LangGraph Platform (agent deployment, bundled into LangSmith’s Plus/Enterprise plans) |
| LlamaIndex | MIT | Widely used; exact star count not independently verified here | LlamaCloud (LlamaParse document parsing/extraction plus a hosted indexing pipeline), free tier of 10,000 credits/month (roughly 1,000 pages), paid tiers beyond that |
| Haystack | Apache 2.0 | ~27k (deepset-ai/haystack) | Haystack Enterprise Platform (formerly marketed as deepset Cloud) — cloud or self-hosted deployment, governance/RBAC/audit logging, visual pipeline design, and Forward Deployed Engineer support; also a lighter “Haystack Enterprise Starter” tier |
All three frameworks themselves are free and unrestricted open source — none of them gate core RAG functionality behind a license key the way some observability tools do. The commercial products are additive layers: managed deployment, document processing at scale, governance, and support, not a different version of the open-source library. Worth noting precisely: we could not independently verify LlamaIndex’s exact current GitHub star count from its own site in this research pass, so that cell is left general rather than guessed.
Pros and Cons
LangChain
- Enormous ecosystem and integration surface — dozens of vector stores, model providers, and tool integrations
- Natural fit if your RAG app will grow into a multi-step agent that does more than retrieve-and-answer
- LangGraph underneath gives durable execution and human-in-the-loop for free
- More moving parts than you need if all you want is straightforward document retrieval
- Abstractions have changed significantly across versions, which can mean migration friction on older tutorials and codebases
- RAG-specific documentation is spread across a broader agent-first framework rather than presented as a first-class use case
LlamaIndex
- Purpose-built abstractions for indexing and querying data, with a genuinely simple high-level API for basic RAG
- LlamaParse handles messy real-world documents (scans, tables, charts) better than generic text loaders
- Layered API means you can start simple and drop down to custom retrieval logic without switching frameworks
- Less natural than LangChain if your end goal is a complex multi-tool agent rather than a data-grounded Q&A system
- LlamaCloud’s free tier (10,000 credits/month, ~1,000 pages) is modest for large document sets
- Smaller model-provider integration surface than LangChain in some areas, though vector store coverage is comparable
Haystack
- Apache 2.0 licensing is unambiguous and permissive, with no separate enterprise-edition carve-out inside the open-source repo
- Broadest documented list of document-store/vector-database integrations among the three
- Built with production deployment (Kubernetes, serializable pipelines, logging/monitoring) as a first-class concern, not an afterthought
- Smaller community and ecosystem than LangChain, meaning fewer third-party tutorials and templates
- Pipeline-based mental model has a steeper initial learning curve than LlamaIndex’s “few lines to a working RAG app” approach
- The enterprise platform rebrand (deepset Cloud → Haystack Enterprise Platform) means older articles referencing “deepset Cloud” may describe outdated branding
Who It’s a Good Fit For
LangChain fits teams building an agent that needs retrieval as one capability among several (web search, code execution, other tool calls), or teams already invested in the LangChain/LangGraph/LangSmith ecosystem who want RAG without adopting a second framework.
LlamaIndex fits teams whose primary problem is “how do we get an LLM to answer questions accurately from our documents,” especially when those documents are messy (PDFs, tables, scanned files) and parsing quality matters as much as the retrieval algorithm.
Haystack fits teams, especially ones with existing NLP/MLOps practices, who are deploying a RAG pipeline as a durable production service and want modular, inspectable, Kubernetes-friendly pipelines plus an enterprise support path if they need it later.
Who Should Look Elsewhere
If you need a fully autonomous multi-agent system with complex role delegation rather than retrieval-grounded Q&A, none of these three is the primary tool for the job — see our LangGraph vs. CrewAI vs. AutoGen comparison instead, since RAG frameworks assume retrieval is central and agent orchestration is secondary (or absent). If your team has no NLP/ML engineering experience and just wants a no-code RAG chatbot over a handful of PDFs, a hosted no-code platform may get you there faster than adopting any of these three open-source libraries directly.
Code Example: Minimal RAG Query Engine in LlamaIndex
LlamaIndex’s high-level API gets you from a folder of documents to a queryable RAG pipeline in a handful of lines:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
# Load documents from a local folder
documents = SimpleDirectoryReader("./data").load_data()
# Build an in-memory vector index over the documents
index = VectorStoreIndex.from_documents(documents)
# Create a query engine and ask a question grounded in your data
query_engine = index.as_query_engine()
response = query_engine.query("What does the Q3 report say about churn?")
print(response)
The Bottom Line
LangChain, LlamaIndex, and Haystack are all permissively licensed, actively maintained, and genuinely capable of building a solid RAG pipeline — the differences are about where each one puts its center of gravity. LangChain is agent-first with RAG as a tool. LlamaIndex is data-and-retrieval-first with agents as an extension. Haystack is production-pipeline-first, built by an NLP company that cares about what happens after your RAG system ships. None of them require a commercial license to use fully; the paid products (LangSmith/LangGraph Platform, LlamaCloud, Haystack Enterprise Platform) all sit on top as optional managed services, not gated versions of the open-source core.
FAQ
Are LangChain, LlamaIndex, and Haystack all free to use commercially?
Yes. LangChain and LlamaIndex are both MIT licensed, and Haystack is Apache 2.0 licensed — all three are permissive open-source licenses that allow commercial use without royalties or a required license purchase. The paid products each vendor sells (LangSmith, LlamaCloud, Haystack Enterprise Platform) are optional hosted/managed add-ons, not different licensing terms on the core framework.
Can I use LangChain and LlamaIndex together?
Yes, and plenty of teams do — for example, using LlamaIndex for document parsing and indexing, then handing retrieved context into a LangChain/LangGraph agent for the rest of the orchestration. They are not mutually exclusive, since both expose standard retriever interfaces.
Which framework is easiest for a first RAG prototype?
Based on each framework’s own documentation, LlamaIndex’s high-level API is explicitly designed to get from documents to answers “in a few lines,” which makes it the fastest on-ramp for a first prototype. LangChain and Haystack both require slightly more setup (chains/agents in LangChain, pipeline definitions in Haystack) before you get a working retrieval loop.
Is deepset Cloud still a thing, or has it been replaced?
As of this research, deepset’s own site markets its commercial hosted/managed offering as the “Haystack Enterprise Platform” (with a lighter “Haystack Enterprise Starter” tier), rather than using the “deepset Cloud” name. If you see “deepset Cloud” referenced elsewhere, treat it as the predecessor branding for what is now sold as the Haystack Enterprise Platform.
Do any of these frameworks require a specific vector database?
No. All three are vector-database agnostic and document dozens of supported stores — Pinecone, Chroma, Qdrant, Weaviate, Milvus, pgvector-based options, Elasticsearch, and more appear across all three frameworks’ integration lists. For background on how the underlying search algorithms (like HNSW and IVF) actually work, see our vector databases and ANN search explainer.
Sources
- LangChain overview – docs.langchain.com
- LangChain vector store integrations – docs.langchain.com
- langchain-ai/langchain on GitHub
- LangChain/LangSmith pricing – langchain.com/pricing
- LlamaIndex framework docs – developers.llamaindex.ai
- LlamaIndex / LlamaCloud – llamaindex.ai
- Haystack – haystack.deepset.ai
- Haystack document store integrations – haystack.deepset.ai
- deepset-ai/haystack on GitHub
- deepset – Haystack Enterprise Platform
