12 Best RAG Frameworks and Libraries in 2026

The best RAG frameworks in 2026: LlamaIndex for data-heavy RAG and hard document parsing, LangChain with LangGraph for multi-step orchestration and agents, Haystack for production pipelines with real evaluation, Dify for low-code internal apps, RAGFlow for scanned and table-heavy documents, LightRAG for knowledge-graph retrieval, and RAGAS for measuring whether any of it actually works. txtai wins when you want one Python package instead of a stack, Kotaemon when you want a self-hosted document chat app with citations, and Pathway when your index must update as data changes. Verba and Cognita stay on the list for teams already running them, but neither is maintained upstream anymore.

RAG stopped being a novelty in 2024 and became table stakes by 2026. The hard part is that "RAG framework" now covers wildly different categories. Some are orchestration libraries (LangChain, LlamaIndex). Some are end-to-end platforms with UIs (Dify, RAGFlow, Kotaemon). Some build a knowledge graph next to the vector index (LightRAG). Some focus narrowly on evaluation (RAGAS) or streaming pipelines (Pathway). Pick the wrong category and you will either fight the framework for months or outgrow it in a quarter. The marketing pages will not tell you this, and most of them claim to do everything.

This guide compares the 12 best RAG frameworks and libraries in 2026. Real features, honest pricing where it is publicly known, pros and cons, and a framework to pick the right tool for your retrieval stack. We checked every project's license, latest release and maintenance status on its GitHub repo on September 26, 2026. We did not run hands-on benchmarks for this list.

Best RAG frameworks: a brief overview

• LlamaIndex: Best for data-heavy RAG with deep ingestion connectors and advanced indexing strategies.
• LangChain RAG: Best for teams already on LangChain who want maximum composability and ecosystem reach.
• Haystack: Best for production NLP pipelines with strong evaluation and modular components from deepset.
• Dify: Best for low-code RAG apps with a visual builder and self-hostable backend.
• RAGFlow: Best for document-heavy RAG with deep parsing of PDFs, tables, and scanned files.
• LightRAG: Best for knowledge-graph RAG when answers depend on links between entities across documents.
• txtai: Best for lightweight embedded RAG when you want a single Python package instead of a stack.
• RAGAS: Best for evaluating RAG pipelines on faithfulness, answer relevancy, and context precision.
• Kotaemon: Best for a self-hosted "chat with your documents" app with citations and multi-user login.
• Verba (Weaviate): Best for teams already running a Verba deployment on Weaviate. The project is discontinued.
• Cognita (TrueFoundry): Best for teams already running Cognita services. The project is no longer maintained.
• Pathway: Best for real-time, streaming RAG where the index must update as data changes.

1. LlamaIndex, best for data-heavy RAG and advanced indexing

LlamaIndex is the framework most teams reach for when their RAG problem is fundamentally a data problem: hundreds of PDFs, mixed structured and unstructured sources, knowledge graphs, or domain-specific schemas. It started as GPT Index in 2022 and has grown into a full data framework for LLMs, with hundreds of connectors via LlamaHub, advanced indexing strategies (vector, summary, tree, knowledge graph, composable), and a managed offering, LlamaCloud, for parsing, ingestion, and retrieval at scale.

By 2026 LlamaIndex has become the default choice for agentic RAG: workflows where an agent decides which index to query, when to re-rank, and when to fall back to a different retriever. The recent push into AgentWorkflow and Workflows 1.0 made multi-step retrieval pipelines easier to express without dropping into ad-hoc orchestration code.

Key features

• Hundreds of data connectors via LlamaHub (Notion, Confluence, Slack, S3, SQL, GraphQL, etc.)
• Advanced indexing: vector, summary, tree, knowledge graph, composable, property graph
• AgentWorkflow and Workflows for event-driven RAG agents
• LlamaParse for high-fidelity PDF, table, and chart extraction
• First-class evaluation harness and integrations with RAGAS and Arize

Best for

• Teams with messy, mixed-source enterprise data
• Builders doing agentic RAG with multiple retrievers
• Anyone needing high-quality PDF and table parsing

Pricing

• Open-source core under MIT license
• LlamaCloud: usage-based (pages parsed, retrievals); free tier available

Pros

• Document parsing via LlamaParse is the strongest in the category
• Strong indexing abstractions beyond plain vector search
• Active release cadence and large community

Cons

• API surface is wide; learning curve is steeper than minimal frameworks
• LlamaCloud lock-in if you adopt managed parsing and indexing

2. LangChain RAG, best for ecosystem and composability

LangChain (MIT) had 147,108 GitHub stars for the whole project on 2026-09-26, and its RAG primitives (retrievers, vector stores, document loaders, multi-query and parent-document patterns) cover the standard patterns. LangGraph adds stateful, multi-step orchestration on top, which is how most serious teams build RAG agents today.

If you are already on LangChain for chains, tools, or agents, using its RAG layer is almost free. If you are starting fresh, you trade some elegance for an enormous ecosystem: every vector DB, every embedding provider, every reranker has a LangChain integration.

Key features

• 100+ vector store integrations and document loaders
• Retriever abstractions: multi-query, parent-document, self-query, ensemble
• LangGraph for stateful RAG agents with branching and replay
• LangSmith for tracing, evaluation, and dataset management
• Strong support for hybrid search and reranking

Best for

• Teams already running LangChain or LangGraph
• Apps that mix RAG with tools, function calling, and multi-agent flows
• Builders who value ecosystem breadth over a tight API

Pricing

• Open-source under MIT license
• LangSmith: free tier, then per-seat and usage-based

Pros

• Largest integration surface in the space
• LangGraph is genuinely good for multi-step RAG
• LangSmith tracing makes debugging tractable

Cons

• API has gone through several large redesigns; older tutorials are misleading
• Abstractions can leak; you still need to understand the underlying retriever

3. Haystack, best for production NLP pipelines

Haystack from deepset is the quiet workhorse of enterprise RAG. While LangChain and LlamaIndex chased mindshare, Haystack 2.x (and the 3.0 line released in July 2026) focused on a clean component model (pipelines as DAGs of typed components) that production teams find easier to reason about, test, and deploy. It has strong roots in semantic search and question answering, which shows in its mature evaluation tooling.

In 2026 Haystack is a particularly good fit for teams that need to combine classical NLP (NER, classification, summarization) with modern LLM-based retrieval, and that want a framework whose authors have shipped search systems at scale.

Key features

• Component-based pipelines with typed inputs and outputs
• Strong retriever, ranker, and reader components
• Built-in evaluation with multiple metrics
• deepset Cloud and Hayhooks for deployment
• Good fit for hybrid sparse+dense retrieval

Best for

• Enterprise teams building search and QA systems
• Organizations that need clean separation of components and tests
• Hybrid retrieval workloads

Pricing

• Open-source under Apache 2.0
• deepset Cloud: contract pricing for enterprise

Pros

• Cleanest pipeline abstraction in the category
• Strong evaluation tooling out of the box
• Production-minded API stability

Cons

• Smaller ecosystem than LangChain or LlamaIndex
• Less momentum in the agentic-RAG narrative

4. Dify, best for low-code RAG apps

Dify is the "build an internal RAG app this afternoon" framework. It pairs a visual workflow builder with a knowledge base, a prompt IDE, and chat and API endpoints, all self-hostable. By 2026 it has become the go-to for non-engineers and small teams who want a real RAG app, not a notebook, without writing every chunk-and-embed loop by hand.

It is not a drop-in replacement for code-first frameworks at scale, but for internal copilots, support assistants, and quick prototypes it ships in hours instead of weeks.

Key features

• Visual workflow builder with RAG nodes
• Built-in knowledge base with chunking, embedding, and reranking
• Prompt IDE with versioning and A/B testing
• Self-hostable backend (Docker, Kubernetes)
• API and embeddable chat widget out of the box

Best for

• Internal RAG copilots and support bots
• Non-engineering teams who still want self-hosting
• Rapid prototyping before a code-first rewrite

Pricing

• Self-hosted community edition under Dify's modified Apache 2.0 license, which requires a commercial license to run a multi-tenant service or to remove the Dify logo from the frontend
• Dify Cloud: Sandbox (free), Pro, Team, Enterprise tiers

Pros

• Shortest path from idea to working RAG app
• Good UX for content owners managing knowledge bases
• Active commercial company behind the project

Cons

• Less flexibility than code-first frameworks at the edges
• Workflow builder hits limits on complex agentic logic

5. RAGFlow, best for deep document parsing

RAGFlow's pitch is simple: most RAG fails because document parsing is bad, not because retrieval is bad. It puts an unusually heavy emphasis on layout-aware parsing of PDFs, scans, tables, and forms: the kinds of documents that quietly destroy retrieval quality when you treat them as flat text.

If your corpus is annual reports, contracts, invoices, manuals, or scanned forms, RAGFlow's parser will often outperform a generic chunker plus embeddings on the same documents.

Key features

• Layout-aware deep parsing of PDFs, DOCX, scanned images, and tables
• Visual citation and chunk inspection UI
• Multi-recall and re-ranking out of the box
• Self-hostable with Docker Compose
• REST API and chat UI

Best for

• Financial, legal, and regulatory document RAG
• Workflows where citations and traceability matter
• Teams with scanned or image-heavy corpora

Pricing

• Open-source under Apache 2.0
• Self-hosted; no official managed tier at time of writing

Pros

• Parsing quality on hard documents is a real differentiator
• Citation UX is genuinely useful for end users
• Permissive license

Cons

• Heavier to deploy than a single Python library
• Smaller community than LangChain or LlamaIndex

6. LightRAG, best for knowledge-graph RAG across documents

LightRAG is an MIT-licensed Python framework that builds a knowledge graph and a vector index side by side from your documents, then retrieves from both at query time. Its README describes it as a lightweight alternative to Microsoft GraphRAG. It ships as a library and as LightRAG Server, which adds a web UI for inserting documents, querying and browsing the graph.

Plain chunk retrieval struggles with questions whose answer is spread across many files, such as "which suppliers appear in both the contracts and the incident reports". LightRAG extracts entities and relationships with an LLM during indexing, so those links become part of the index. The authors report that it beats naive RAG and GraphRAG on their benchmarks; that is the maker's evaluation, not ours.

Key features

• Five query modes: local, global, hybrid, naive (plain vector search) and mix, the default
• Production storage on PostgreSQL (all four storage types), MongoDB or OpenSearch, or Neo4j, Memgraph, Milvus and Qdrant for specific layers
• LightRAG Server with a web UI and an Ollama-compatible API
• Document deletion with automatic graph regeneration, reranker support and citations
• Multimodal parsing through MinerU or Docling since RAG-Anything was merged in May 2026
• Integrations with RAGAS for evaluation and Langfuse for tracing

Best for

• Corpora where answers depend on relationships between entities across documents (legal, financial, research)
• Teams exploring graph RAG who want a permissive license and a server out of the box

Pricing

• Open-source under MIT license; latest release v1.5.7 on September 2, 2026
• No hosted tier in the repo; you pay for LLM and embedding calls and your storage

Pros

• Graph and vector retrieval in one package, with naive mode available as a plain-RAG baseline
• Wide choice of production storage backends
• Frequent releases through 2026

Cons

• Indexing runs LLM entity and relationship extraction on every chunk, so it costs more and needs a more capable model than chunk-and-embed RAG (per the README)
• The default in-memory storage is for testing only, and the embedding model is fixed once you index; switching means re-embedding

Skip it if your questions are mostly single-document lookups. Plain vector retrieval in LlamaIndex or Haystack is cheaper to index and run.

7. txtai, best for embedded and lightweight RAG

txtai is a single-package Python framework that bundles vector search, graph search, and a RAG layer in one dependency. While the rest of the field has grown into stacks of five to ten services, txtai stayed disciplined: one pip install, a SQLite or DuckDB backend by default, and a full RAG feature set in one package (the 9.13.0 wheel on PyPI is 341 KB, though a standard install also pulls in PyTorch, Transformers and Faiss).

It is the framework to reach for when you want RAG inside a CLI tool, a desktop app, a Jupyter notebook, or an edge device, not a Kubernetes cluster.

Key features

• Embedded vector + graph search in a single package
• Sentence-transformers, llama.cpp, and Hugging Face integrations
• Pipelines for summarization, transcription, translation, and RAG
• API server and Docker images available
• Workflow YAML for declarative pipelines

Best for

• Embedded apps, CLIs, and notebooks
• Small to medium corpora where a vector DB is overkill
• Researchers and data scientists who hate stack sprawl

Pricing

• Open-source under Apache 2.0

Pros

• Smallest blast radius of any framework on this list
• Surprisingly capable for its size
• Works fully offline with local models

Cons

• Not aimed at multi-tenant, multi-billion-vector workloads
• Smaller ecosystem of third-party integrations

8. RAGAS, best for evaluating RAG pipelines

RAGAS is not a RAG framework. It is the framework that tells you whether your RAG framework is any good. It scores pipelines on faithfulness, answer relevancy, context precision, context recall, and a growing list of metrics, using both LLM-as-judge and reference-based methods.

By 2026 RAGAS has become the de facto standard for RAG eval in CI. If you are shipping retrieval to production without RAGAS or an equivalent harness, you are flying blind.

Key features

• Faithfulness, answer relevancy, context precision and recall metrics
• LLM-as-judge and reference-based evaluation
• Synthetic test set generation
• Integrations with LangChain, LlamaIndex, Haystack, and LangSmith
• Dataset and experiment tracking

Best for

• Any team shipping RAG to production
• CI gates and regression tests on retrieval quality
• Comparing chunking, embedding, and prompt strategies

Pricing

• Open-source under Apache 2.0

Pros

• Well-defined, widely cited metrics
• Plays nicely with every major RAG framework
• Synthetic test-set generation saves real time

Cons

• LLM-as-judge metrics are only as good as the judge model
• Requires discipline to integrate into CI correctly

9. Kotaemon, best for a self-hosted document chat app with citations

Kotaemon is an Apache 2.0 Python project from Cinnamon that gives you a working RAG web app for chatting with your documents, plus the pipeline code underneath if you want to build your own. It runs from a Docker image, supports multi-user login with private and public file collections, and shows each answer's citations in an in-browser PDF viewer with highlights.

It covers the same need Verba used to: an internal "ask our PDFs" app you can put in front of non-engineers this week. It is not tied to one vector database, and it runs with API models or local ones through Ollama and llama-cpp-python. The UI is built on Gradio, so customizing it means working in Python.

Key features

• Hybrid retrieval (full-text plus vector) with re-ranking by default
• Citations with relevance scores in a PDF viewer, plus a warning when retrieval returns low-relevance results
• Multi-user login, private and public collections, shareable chats
• API providers (OpenAI, Azure OpenAI, Cohere, Groq) or local models via Ollama and llama-cpp-python
• Question decomposition and agent reasoning (ReAct, ReWOO) for multi-hop questions
• A GraphRAG indexing pipeline included as an example

Best for

• Teams that want an internal document QA app without writing a frontend
• Private or offline deployments using local models (there is a Docker image with Ollama bundled)

Pricing

• Open-source under Apache 2.0; latest release v0.12.0 on May 31, 2026
• No paid tier in the repo; you pay for hosting and model calls

Pros

• Citation preview lets reviewers check the exact source page
• Lite and full Docker images for linux/amd64 and linux/arm64
• One codebase serves end users and developers

Cons

• Customizing the Gradio UI means Python and Gradio work, not your usual web frontend stack
• Files other than PDF, HTML, MHTML and XLSX need the Unstructured package (or the larger full image)

Skip it if you need a branded, customer-facing chat product or only want a library. LlamaIndex or Haystack behind your own frontend fits that better.

10. Verba (Weaviate), best for existing Weaviate starter deployments

Status, checked September 26, 2026: the Verba GitHub repository is archived, and its README says the project is discontinued with no further updates, bug fixes, security patches or new features. Existing deployments keep working. For a new project, look at Kotaemon (#9) instead.

Verba is Weaviate's open-source "golden retriever": a polished, opinionated RAG app you can clone, point at your data, and demo in an afternoon. It targets the gap between "notebook RAG" and "we built our own React frontend": a working chat UI, a working ingestion flow, and a working hybrid-search backend, all wired together.

It is especially useful as a reference architecture for teams building on Weaviate who want a sane starting point instead of a blank repo.

Key features

• Full-stack RAG app: ingestion, chat UI, evaluation
• Hybrid search via Weaviate (BM25 + vector)
• Multiple data import flows: files, URLs, GitHub, etc.
• Configurable generators (OpenAI, Anthropic, local, etc.)
• Docker-based deployment

Best for

• Weaviate users who want a working starter app
• Internal demos and stakeholder previews
• Teams evaluating hybrid search on their own data

Pricing

• Open-source under BSD-3
• Weaviate has its own OSS and Cloud pricing

Pros

• Genuinely usable out of the box
• Good demo of Weaviate hybrid retrieval
• Clear codebase to fork

Cons

• Coupled to Weaviate as the backend
• Less flexible than a code-first library
• Discontinued: no security patches going forward, per the repo README

11. Cognita (TrueFoundry), best for existing modular RAG services

Status, checked September 26, 2026: the Cognita GitHub repository is archived, and its README says the project is no longer actively maintained. The code still works as a reference, but plan a migration if you run it in production.

Cognita, from TrueFoundry, is what happens when a platform team that ships ML to production writes a RAG framework. It is modular by default (data loaders, parsers, embedders, vector DBs, rerankers, and query controllers are all swappable) and it is designed from day one to be deployable as a service rather than imported as a library.

In 2026 Cognita is a strong fit for engineering teams that already think in terms of services, not notebooks, and that want a RAG framework with deployment baked in.

Key features

• Modular components: parser, embedder, vector DB, reranker, query controller
• API-first design with FastAPI backend
• UI for managing collections and queries
• Native deployment via TrueFoundry (Kubernetes-based)
• Multi-collection and multi-tenant support

Best for

• Platform teams standardizing RAG across multiple apps
• Workloads that need a service, not a library
• Multi-tenant internal RAG platforms

Pricing

• Open-source under Apache 2.0
• TrueFoundry platform pricing for managed deployment

Pros

• Production-shaped from the start
• Clean separation of concerns
• Good fit for internal platform engineering

Cons

• Less community content than LangChain or LlamaIndex
• Tighter alignment with TrueFoundry for the managed path
• No longer maintained, per the repo README

12. Pathway, best for real-time and streaming RAG

Pathway is the framework you want when your RAG index can't be a nightly batch job. It is a Python-first streaming data framework with a built-in LLM and RAG layer, designed so that indexes update as source data changes: files added to S3, rows changed in Postgres, events landing in Kafka.

For use cases like operations copilots, trading research, observability assistants, or anything where "answers must reflect the world as of two minutes ago" is a real requirement, Pathway is the pick on this list.

Key features

• Streaming Python data framework with incremental computation
• LLM and RAG primitives (retrievers, indexes, prompts) on top of streams
• Connectors for Kafka, Postgres, S3, Sharepoint, Google Drive
• Always-fresh vector and full-text indexes
• Self-hostable, on-prem-friendly

Best for

• Real-time operations and analytics copilots
• Use cases where stale answers are unacceptable
• Teams with strong streaming data backgrounds

Pricing

• Source-available under the Business Source License 1.1, not an OSI open-source license. An additional use grant allows free production use on one machine per installation, excluding use as a stream data processing service offered to third parties
• Pathway Enterprise: contract pricing

Pros

• Genuinely solves the "stale index" problem
• Strong fit with event-driven architectures
• Python-first, no separate streaming language

Cons

• Streaming mental model is a learning curve for batch-trained teams
• Overkill if a nightly reindex is good enough
• The BSL 1.1 license limits free production use to a single machine per installation; check the LICENSE file before you scale out

How to choose the best RAG framework

1) Is your bottleneck retrieval, parsing, or evaluation?

If your bottleneck is parsing (bad PDFs, tables, scans), start with LlamaIndex (LlamaParse) or RAGFlow. They will move your numbers more than a fancier retriever ever will. If the bottleneck is retrieval (embeddings, hybrid search, reranking), LangChain RAG and Haystack give you the most knobs, and LightRAG adds a knowledge graph when plain vector search misses connections between documents. If the bottleneck is evaluation, meaning you simply do not know whether you are getting better, bolt RAGAS on top of whatever framework you already use before you change anything else. The AY Automate team almost always pairs a primary framework with RAGAS in CI on AI agent development builds.

2) Notebook, app, or platform?

If you are still in a notebook and need to validate the idea, txtai or LlamaIndex in a single file is the fastest path. If you want a working app this week (chat UI, ingestion flow, knowledge base), Dify, Kotaemon, or RAGFlow get you there. If you are building a platform that will host many RAG apps, a LangChain + LangGraph or Haystack stack on top of a managed vector DB and Supabase is a more honest starting point. Picking a heavyweight platform framework for a notebook problem is the most common mistake we see.

3) Batch index or live index?

Almost every RAG tutorial assumes a batch index: load documents, embed, store, query. That is fine for documentation, knowledge bases, and legal corpora. It is wrong for operations, trading, observability, or anything where "as of two minutes ago" matters. Pathway is the framework on this list built around live indexes. With most of the others you keep the index fresh with cron jobs and webhooks, and past a certain update rate you will be fighting the framework.

4) Python-only or polyglot?

Most of this list is Python-first. If your stack is TypeScript-heavy, LangChain's JS port and LlamaIndex.TS are the most established options in 2026, and even then most teams put a thin Python service in front for the heavy lifting. If you need to call RAG from Go, Rust, or .NET, you will end up wrapping a Python service behind a REST or gRPC API regardless of framework. See our best Python AI agent frameworks breakdown for a deeper take on that decision.

Build your RAG stack with AY Automate

AY Automate builds production RAG systems on LlamaIndex, LangChain, LangGraph, and Claude Code, wired into Supabase, pgvector, Weaviate, or your existing data warehouse, with RAGAS gates in CI and clean handoff documentation. We have shipped retrieval-backed copilots in English, French, and Arabic, with citation UIs that legal and compliance teams will actually sign off on. If you want a partner that treats RAG as a system to operate, not a demo to ship, start with a free consultation and we will scope the right framework, vector store, and eval stack for your data, or tell you, honestly, that you do not need RAG at all. See AI agent development for the full service.

Where AY Automate fits

We are not one of the tools on this list. We place AI engineers who use tools like these to generate revenue for your business, that is the offer. For RAG, that means an engineer who scopes the right framework, vector store, and eval stack for your actual data. See AI engineer placement or explore our related service.

FAQ

What is a RAG framework?

A RAG (retrieval-augmented generation) framework is a library or platform that handles the four core steps of retrieval-augmented generation: ingesting and parsing source data, chunking and embedding it into a searchable index, retrieving relevant context at query time, and feeding that context to an LLM along with the user's question. Some frameworks cover all four steps; others specialize in one part of the pipeline.

How is a RAG framework different from a vector database?

A vector database (Weaviate, Pinecone, pgvector, Qdrant, Milvus) stores and searches embeddings. A RAG framework is the layer above: it decides what to embed, how to chunk it, which retriever and reranker to use, how to assemble the prompt, and how to evaluate the result. You almost always use both: a RAG framework on top of a vector database. A few frameworks ship with a default backend (txtai and LightRAG embed one, Verba is built on Weaviate); most are agnostic. Our guide on when AI agents actually need a vector database covers the storage side.

How do I verify a RAG framework will scale?

Look for three signals. First, public benchmarks or case studies at the scale you care about: millions of chunks, hundreds of queries per second, multi-tenant isolation. Second, a clean separation between ingestion, retrieval, and generation, so you can scale each independently. Third, real evaluation tooling (or clean integration with RAGAS), because you will not catch quality regressions at scale without it. Marketing pages will not tell you the truth here; GitHub issues and Discord channels usually will.

How much do RAG frameworks cost in 2026?

Most frameworks on this list are free open-source libraries under MIT, Apache 2.0 or BSD-3 licenses. Two exceptions: Dify's self-hosted edition uses a modified Apache 2.0 license with multi-tenant and branding conditions, and Pathway is source-available under BSL 1.1. Real costs are infrastructure (vector DB, compute, storage), LLM API or self-hosted model costs, and managed-tier fees if you adopt LlamaCloud, deepset Cloud, Dify Cloud, LangSmith, or TrueFoundry. The bill depends mostly on model choice, query volume and hosting, so price a two-week pilot on your own documents before you commit.

How long does a RAG implementation take?

A working demo on clean documents takes a day with Dify, Kotaemon, or LlamaIndex in a notebook. If you're already building on Claude and don't need a full framework, see our guide to building RAG directly with Claude's native documents API for an even faster path to citation-backed retrieval. A production-grade system (proper parsing, hybrid retrieval, reranking, evaluation, monitoring, access control, and multi-tenant isolation) is a 6-12 week project for a small team, longer if your data is messy or your compliance bar is high. The first month is almost always parsing and chunking, not retrieval.

Is RAGAS or another eval tool really necessary?

Yes. Without an eval harness you cannot tell whether a change to chunking, embeddings, retrievers, or prompts made things better or worse. You will ship regressions, and your users will find them before you do. RAGAS is the most common choice in 2026, but TruLens, DeepEval, and Arize Phoenix are credible alternatives. Pick one and put it in CI.

Should we use LangChain or LlamaIndex?

If your problem is fundamentally data (many sources, messy formats, advanced indexing), start with LlamaIndex. If your problem is fundamentally orchestration (agents, tools, multi-step flows), start with LangChain and LangGraph. Many production stacks use both: LlamaIndex for ingestion and indexing, LangChain or LangGraph for the agent and tool layer. They are not mutually exclusive.

What is the best open-source RAG framework?

For most teams, LlamaIndex (MIT) is the best open-source RAG framework to start with, because it covers ingestion, parsing, indexing and retrieval in one library. Pick LangChain (MIT) for agent-heavy apps, Haystack (Apache 2.0) for strict, testable pipelines, and RAGFlow (Apache 2.0) for scanned or table-heavy documents. Check the license before you ship: Dify and Pathway are not plain OSI licenses.

What is the difference between LightRAG and Microsoft GraphRAG?

Both use an LLM to build a knowledge graph from your documents. Microsoft GraphRAG is a research project whose README says it is in maintenance mode, with no new features. LightRAG (MIT) pairs the graph with a vector index, offers five query modes and ships a server with a web UI. LlamaIndex also has knowledge graph indexes. See our GraphRAG explainer.

Should I use RAG or fine-tuning?

Use RAG when the model needs facts that change or that you must cite, such as documents, tickets, policies or product data. Use fine-tuning to change format, tone or behavior on a narrow, stable task. Many production systems combine both. Our fine-tuning vs RAG guide walks through the trade-offs with cost and maintenance in mind.

Which RAG frameworks are no longer maintained?

On this list, Verba and Cognita. Verba's GitHub repo is archived with a notice that the project is discontinued and gets no security patches. Cognita's README says it is no longer actively maintained, and its repo is also archived. Both checked September 26, 2026. Existing deployments still run, but start new projects on Kotaemon, LlamaIndex or Haystack instead.

Can a RAG framework train my internal team?

The frameworks themselves do not, but most have strong docs, courses, and community Discords. For internal enablement we usually pair a framework choice with a 2-4 week internal workshop: build one real RAG app together, set up RAGAS in CI, then hand off to the internal team with clear ownership. That handoff is part of every AY Automate AI agent development engagement, not an add-on.