RAG frameworks compared: LangChain, LlamaIndex, Haystack, DSPy, Semantic Kernel, txtai, RAGFlow, Verba, Flowise, Langflow

What is this about?

A RAG framework is the bracket that ties an embedding model, a vector database, and a language model into a working answer pipeline. In theory you do not need a framework – a RAG pipeline can be written by hand in 200 lines of Python. In practice the question is whether you want to carry maintenance, extension, and onboarding of new developers over years. That is exactly where frameworks pay off.

In May 2026 there are roughly ten serious options. Three are industry standard for code-first setups (LangChain, LlamaIndex, Haystack), two are visual builders for no-code teams (Flowise, Langflow), two are enterprise-focused (Semantic Kernel, deepset Haystack), two are slim specialists (txtai, Verba), and one is an academic break with the prompt-engineering tradition (DSPy).

For a Swiss SME, three questions decide: how much abstraction makes sense, how much lock-in are you willing to accept, and how mature is the framework in May 2026? A RAG pipeline running on LangChain today is not trivially portable – the decision sticks for several years.

Why it matters

Three axes decide the right choice: abstraction level, production maturity, and lock-in risk.

Abstraction level: frameworks differ in how much logic they hide. LangChain has hundreds of classes with helper-helper-helper structures – comfortable for a first prototype, often "helper hell" in production. LlamaIndex is more clearly structured. txtai and Verba are extremely slim and leave more to the developer. DSPy goes the other way: you program tasks, not prompts – the system learns optimal prompts itself.

Production maturity: as of May 2026 LangChain is still the most-used choice but has a reputation of bringing breaking changes every other release. LlamaIndex 0.10+ is significantly more stable. Haystack from deepset is enterprise-driven and stresses stability. RAGFlow is fast-growing in May 2026 but still young. Flowise and Langflow as visual builders are good for prototypes, rarely first choice for serious production.

Lock-in risk: starting with LangChain and wanting out 3 years later means rebuilding the pipeline. LlamaIndex and Haystack are similarly integrated but cleaner modularised – migration is feasible, not trivial. txtai and a custom build with direct API calls (Qdrant + OpenAI) carry the lowest lock-in risk but the highest maintenance effort. The clean decision is usually: LlamaIndex for code-first teams, Haystack under enterprise compliance pressure.

The ten frameworks in detail

LangChain (MIT, Python + JS): the industry default. May 2026 in version 0.3+, a huge ecosystem with hundreds of integrations (vector DBs, LLM providers, tools, memory). Strong at prototyping, weak on stability and code quality. Has earned a "bloated" reputation. Still the most-used choice because community and docs are large.

LlamaIndex (MIT, Python + TS): RAG-specialised framework. Originally started as GPT-Index, by May 2026 the cleanest industry framework for pure RAG pipelines. Clearer abstractions than LangChain, good docs, more stable API. v0.10+ is production-ready. Our default recommendation for code-first teams with a RAG focus.

Haystack (Apache 2.0, Python, deepset): enterprise RAG framework from Berlin. The pipeline concept (components as nodes in a graph) is very clean, production-ready for years. May 2026 in version 2.x with focus on multi-modal and agent workflows. Best choice when deepset support or enterprise security is mandatory.

DSPy (MIT, Python, Stanford): breaks with the prompt-engineering tradition. Instead of writing prompts, you define tasks (signatures) and DSPy optimises the prompts automatically via few-shot sampling or bootstrapping. In May 2026 an academically very interesting approach increasingly finding production use. High learning curve, big reward for complex multi-step pipelines.

Semantic Kernel (MIT, Microsoft, .NET + Python + Java): Microsoft answer to LangChain. Strongly integrated with Azure OpenAI, Microsoft Graph, Office 365. May 2026 first choice for companies already on the Microsoft stack. Little point outside the Microsoft ecosystem.

txtai (Apache 2.0, Python, NeuML): slim RAG toolkit. A single Python module import, built-in vector DB (on SQLite/DuckDB), embedded LLM integration. Very easy to start, good for prototypes and small corpora (< 100k documents). May 2026 version 8.x with multi-modal support.

RAGFlow (Apache 2.0, Python, self-host): open-source RAG system with a web UI. Fast-growing in May 2026 (release 0.15+), offers document parsing, chunking, embedding, and answer pipeline in one bundled application. Good choice when you do not want to integrate a framework but need a finished product – for instance an internal knowledge base.

Verba (BSD-3, Python, Weaviate): open-source RAG UI from Weaviate. Built-in Weaviate vector DB connection, finished chat interface. May 2026 stable, good for demos and small knowledge bases. Less flexible than RAGFlow.

Flowise (Apache 2.0, Node.js, self-host + cloud): visual drag-and-drop builder on LangChain. You drag components onto a canvas and wire them. Very popular in the no-code camp in May 2026. Good for fast prototypes and non-technical teams, but under the hood LangChain runs with all its drawbacks.

Langflow (MIT, Python, self-host + cloud): similar to Flowise, visual builder. May 2026 increasingly backed by Datastax (now IBM). Functionally comparable to Flowise, but Python-based instead of Node.js. Pick by preferred stack.

Selection workflow in 6 steps

1. 01Code-first or no-code? Developers in-house -> LlamaIndex/Haystack. No developers -> Flowise/Langflow/RAGFlow.
2. 02Estimate volume: < 10k documents -> txtai or custom build. 10k-1M -> LlamaIndex. > 1M -> Haystack.
3. 03Compliance pressure: high enterprise requirements -> Haystack with deepset support. Microsoft stack -> Semantic Kernel. CH fiduciary standard -> LlamaIndex.
4. 04Pipeline complexity: standard RAG -> LlamaIndex/Haystack. Multi-hop, reasoning -> DSPy. Visual flow -> Langflow/Flowise.
5. 05Lock-in tolerance: high -> LangChain (largest ecosystem). Low -> custom build with Qdrant + OpenAI client directly.
6. 06PoC with real data: load 5k documents, run 30 real example questions, measure latency and answer quality. Only then full integration.

Recommendation by use-case

Swiss SME code-first, RAG for a client knowledge base, 5k-500k documents: LlamaIndex. Clear API, good docs, cleaner code, more stable releases than LangChain. May 2026 v0.10+ production-ready. Setup effort 3-7 days depending on data variety.

Enterprise under compliance pressure with 1M+ documents: Haystack from deepset. Pipeline concept clearly documented, deepset offers commercial support. Good for banks, insurance, regulated industries.

Microsoft stack setup, Azure OpenAI, Office 365 integration: Semantic Kernel. First choice when client data sits in SharePoint/OneDrive and Azure is the set cloud provider.

Fast prototype without code, 1-2 day setup: Flowise or Langflow. Visual builder, finished pipeline via drag and drop. On success, migrate to LlamaIndex for production.

Finished RAG product for an internal knowledge base, no custom code: RAGFlow self-host. Web UI, document upload, chat. Up and running in a few hours for smaller companies.

Research, complex multi-step pipelines, few-shot optimisation: DSPy. Academically grounded, now production-capable. Worth it when the RAG pipeline goes beyond simple retrieve-and-generate (multi-hop, reasoning, chains-of-thought).

Very small knowledge base (< 10k documents), solo developer: txtai. A Python library, all-inclusive. Enough for simple FAQ bots and personal tools.

When these frameworks are wrong

If your RAG pipeline is simple enough that you can write it by hand in an hour (embedding -> vector DB -> prompt -> LLM), you do not need a framework. Direct code with the Qdrant client and OpenAI SDK is shorter, faster, easier to maintain, and has zero lock-in. For small Swiss fiduciary setups we often write exactly this 200-line variant.

LangChain is the wrong choice for the smallest SME setups – learning curve and complexity are disproportionate to the value. For pure RAG pipelines, LlamaIndex is the cleaner alternative.

Flowise and Langflow are the wrong choice when you want to run a production pipeline at high volume. Visual builders are excellent for prototypes, but versioning, testing, debugging, and performance tuning are clearly better in code-first frameworks.

DSPy is the wrong choice for standard RAG without optimisation needs – the learning curve pays off only when the task is complex enough that you would otherwise spend several hours on prompt tuning. For a simple Q&A pipeline LlamaIndex is faster done.

Semantic Kernel is the wrong choice outside the Microsoft ecosystem – its Azure and Office integrations are an advantage there, ballast elsewhere. If you have not bet on .NET or Azure OpenAI, pick LlamaIndex or Haystack.

Verba is the wrong choice when you do not use Weaviate as the vector DB – the binding is hard-wired. For Qdrant or pgvector setups, Verba is pointless.

RAGFlow is the wrong choice with high custom requirements (own chunking logic, special source adapters, multi-tenancy) – as a finished product it is less flexible than a framework like LlamaIndex.

Trade-offs

FAQ

Related topics

Sources

1. LangChain Documentation · 2026-05
2. LlamaIndex Documentation – v0.10+ · 2026-05
3. Haystack 2.x by deepset · 2026-04
4. DSPy – Stanford NLP Group · 2026-04
5. Semantic Kernel – Microsoft Learn · 2026-04
6. RAGFlow – open-source RAG system · 2026-05
7. Flowise – visual LLM builder · 2026-04
8. Langflow – Datastax/IBM-backed builder · 2026-05