Best RAG Tools and Frameworks

LlamaIndex is the tool we would start with for most RAG projects, because it is strongest at the step that breaks most systems: getting clean text out of messy documents. The open-source framework (MIT licence, about 52,300 GitHub stars) gives you loaders for many data sources, chunking, indexes, retrievers, rerankers and query engines in Python and TypeScript.

The paid side is LlamaParse, which turns PDFs, tables, charts and scanned pages into clean Markdown or JSON, extracts fields with citations and can host indexes for retrieval. LlamaIndex says it handles more than 130 file types. The Free plan includes 10,000 credits a month; Starter is $50/month with 40,000 credits and Pro is $500/month with 400,000. Extra credits cost $1.25 per 1,000.

The company is open that its main focus is now document parsing and extraction, not the framework. That is good news for RAG quality, but the hosted index features are modest (5 indexes of up to 50 files each on Free).

Pick it if your answers depend on complex PDFs, contracts or financial reports. Skip it if you want a finished chat app with a web interface; RAGFlow is closer to that.

LangChain is the most-starred framework on this list, with about 147,000 GitHub stars, and it covers RAG end to end: document loaders, text splitters, embedding wrappers, retrievers for dozens of vector databases, and chains that pass results to a model. Because most vector databases and model providers ship a LangChain integration, you can swap parts without rewriting your app. It is MIT-licensed and free.

For RAG that needs several steps, such as rewriting a question, searching twice or checking an answer before replying, teams usually add its sister project LangGraph for control flow and LangSmith for tracing and evaluation. LangSmith's Developer plan is free for one seat with 5,000 traces a month; Plus costs $39 per seat per month.

The downside is abstraction. LangChain wraps everything in its own classes, which can make a simple pipeline harder to debug, and document parsing is only as good as the third-party loader you choose.

Pick it if you want maximum choice of models and databases and may add agent steps later. Skip it if your main problem is messy documents (LlamaIndex) or you want a pipeline you can read top to bottom (Haystack). See our agent frameworks ranking for LangGraph.

RAGFlow is the best open-source choice if you want a complete RAG application rather than a code library. You run it with Docker, upload files through a web interface and get a chat assistant with citations, without writing a pipeline yourself. It is Apache 2.0 and has about 91,300 GitHub stars.

Its strength is document understanding. RAGFlow parses Word files, slides, spreadsheets, images, PDFs and web pages, lets you pick a chunking template per document type, and shows you the chunks so you can fix bad splits. It can also use MinerU or Docling as parsers, combines several retrieval methods with re-ranking, and syncs data from Confluence, S3, Notion and Google Drive. Recent releases add agent workflows, memory and MCP support.

The cost is hardware and upkeep. The project asks for at least 4 CPU cores, 16GB of RAM and 50GB of disk, and you handle upgrades and security yourself. It is still before version 1.0 (v0.27.2), so expect changes between releases.

Pick it if you want a private, self-hosted document assistant with little code. Skip it if you need a library to embed inside your own app; use LlamaIndex or Haystack instead.

Haystack, from deepset, is an open-source framework (Apache 2.0, about 26,600 GitHub stars) built around explicit pipelines. You connect components, such as a file converter, splitter, embedder, retriever, ranker and generator, and you can see and test every step. That makes it easier to reason about than more automatic frameworks, which matters when a RAG answer is wrong and you need to find out why.

It supports hybrid retrieval, rerankers, many model providers and vector stores, and agents for multi-step work. The project's README lists Apple, Meta, Databricks and Netflix among organisations that use it.

deepset sells two layers on top. The Haystack Enterprise Platform adds a visual pipeline builder, testing, observability and deployment. Its free Studio tier gives 1 user, 100 pipeline hours and 50 files; the Enterprise tier is custom-priced with cloud or self-hosted options. Enterprise Starter adds engineering support and deployment templates for the open-source framework.

Pick it if you want a clear, testable pipeline that you can run anywhere. Skip it if you need the largest integration catalogue (LangChain) or the strongest document parsing (LlamaIndex).

Weaviate is an open-source vector database that has grown RAG features on top. Its hybrid search runs keyword and vector search in one query and blends the scores, it can call embedding and generative models for you, and its multi-tenancy suits apps that keep each customer's data apart. See our vector databases ranking for the database side.

The RAG piece is the Query Agent, generally available since 17 September 2025. You ask a question in plain English and it breaks the request down, builds searches, filters and aggregations across your collections, reranks results and answers with citations back to the source objects. Weaviate Cloud's free plan includes 1,000 Query Agent requests a month, Flex (from $45/month) includes 30,000, and Premium includes unlimited requests.

The gap is ingestion. Weaviate stores and searches what you give it, but it does not parse PDFs or sync SharePoint for you, so you still need a parser such as LlamaParse or RAGFlow's.

Pick it if you want your retrieval layer to be open source, self-hostable and strong at hybrid search. Skip it if you want one product that also handles document parsing and connectors.

Amazon Bedrock Knowledge Bases is the natural choice for teams on AWS. The newer Managed Knowledge Base, generally available since 17 June 2026, handles the whole pipeline: it connects to Amazon S3, SharePoint, Confluence, Google Drive, OneDrive and a web crawler, syncs automatically, picks default embedding, reranking and answer models, and manages the vector storage.

Retrieval is up to date. It offers hybrid search, reranking and agentic retrieval, which splits a complex question into parts, searches for each, checks whether it has enough evidence and repeats if needed. Smart Parsing chooses a parsing approach for each file, including images, audio and video. It also plugs into Bedrock AgentCore for agents.

If you want more control, classic Knowledge Bases still let you choose your own vector store, including Amazon S3 Vectors, which AWS says cuts vector costs by up to 90%.

Pricing is the weak spot. The managed version bills on data stored and retrievals, but we could not find the rates on AWS's pricing page, and you also pay for the answer model and reranking ($2 per 1,000 queries for Cohere Rerank 3.5).

Pick it if your data and apps already live in AWS. Skip it if you need to run outside AWS or want to predict costs precisely.

Azure AI Search is Microsoft's search engine for RAG and the retrieval layer under Foundry IQ, the knowledge service for agents in Microsoft Foundry. It is a mature engine with keyword, vector and hybrid search, a semantic reranker, and indexers that pull content in from Azure storage and other sources.

The big 2026 change is agentic retrieval. A knowledge base can use an LLM to break a complex question into subqueries, run them in parallel, rerank each one and merge the results with source references. The basic version became generally available in the 2026-04-01 API; LLM query planning, answer synthesis and some source types still need a preview API. Knowledge bases can also be queried through an MCP endpoint.

Costs come in several parts. The free tier gives 50MB of storage, and paid tiers are billed per search unit, with rates shown only in the Azure calculator. Semantic ranking includes 1,000 free requests a month and agentic retrieval 50 million free tokens, while LLM query planning is billed separately through Azure OpenAI.

Pick it if you run on Microsoft 365 and Azure and want strong, permission-aware enterprise search. Skip it if you want simple, predictable pricing or a cloud-neutral tool.

Pinecone Assistant is one of the fastest ways to get a working RAG API without running anything. You upload files, it chunks and embeds them, and a chat endpoint returns grounded answers with citations. A separate context API returns only the retrieved snippets, so you can send them to your own model. Metadata filters narrow searches to the right documents.

Pricing is published and starts free. The Starter plan includes 500,000 input tokens, 300,000 output tokens, 500,000 context tokens and 1GB of storage a month. Builder is $20/month flat with larger allowances. On Standard ($50/month minimum) you pay $8 per million input tokens, $15 per million output tokens, $5 per million context tokens and $3 per GB per month.

For large companies, Pinecone now also sells Nexus (generally available 6 August 2026), a knowledge engine that runs inside the customer's own cloud. Pinecone claims it cuts token costs by more than 90% compared with agentic RAG.

The trade-offs are lock-in and control. Assistant is closed source, runs only on Pinecone, and gives you little say over parsing and chunking.

Pick it if you want a hosted RAG API today. Skip it if you need to self-host or tune every step.

Google's Agent Search is the product long known as Vertex AI Search, and before that as Enterprise Search, Generative AI App Builder and AI Applications. Google renamed it again after Vertex AI became the Gemini Enterprise Agent Platform in April 2026. The renames matter in practice, because tutorials and forum answers use all of the old names.

Google describes it as a Google Search-quality retrieval engine for your own data. You point it at websites, documents or structured data, and it handles parsing, chunking, embedding, ranking and grounded answers generated with Gemini. Developers who want more control can use lower-level APIs for search and RAG on the same platform.

Pricing is pay as you go. Google's docs give the Standard Edition rate as $1.50 per 1,000 queries, and the free trial includes 10,000 queries per account. Advanced features cost more, and a configurable subscription model suits steady workloads.

Pick it if your data is on Google Cloud and you want high search quality with little tuning. Skip it if you want to see and control every step of the pipeline, or you need to run outside Google Cloud.

OpenAI's File Search tool is the simplest RAG you can build if you already use OpenAI models. You create a vector store, upload files and add the file_search tool to a Responses API call. OpenAI handles chunking, embedding and a combined semantic and keyword search, and the answer comes back with file citations. You can filter by file metadata and cap the number of results to save tokens.

Pricing is easy to read: $2.50 per 1,000 file search calls, plus $0.10 per GB of storage per day after the first free GB. On top of that you pay normal token prices for the model.

The limits are control and portability. It supports 18 file types but has no connectors for SharePoint or Google Drive, gives you little say over parsing and chunking, and works only with OpenAI's models. Daily storage billing also adds up: keeping 10GB for 30 days costs 9 x $0.10 x 30 = $27.

Pick it if you are prototyping an OpenAI-based assistant over a few hundred documents. Skip it if you need other models, data connectors or fine control over retrieval.

Progress Agentic RAG is the former Nuclia, a RAG-as-a-service platform that Progress Software bought in 2025. It targets teams that want a managed, no-code knowledge base with answers they can check. Progress says it offers more than 30 retrieval strategies you can switch without changing code, and an evaluation system called REMi that scores every answer for relevance, context and groundedness.

Plans are flat-rate with unlimited users. Starter costs $700/month with 5GB of indexed data (or 15,000 resources), text files only and a 14-day free trial. Pro costs $1,925/month with 25GB (or 80,000 resources) and all file types. Enterprise adds hybrid cloud deployment. Usage beyond the included allowance is billed in Progress's own tokens at $0.008 each.

Progress lists SOC 2 Type II, ISO 27001 and GDPR compliance. It also courted customers of Ragie after that service announced it would shut down on 19 July 2026, offering free Pro-tier tokens to switchers.

Pick it if you want a hosted, no-code RAG service with built-in answer scoring and predictable monthly bills. Skip it if your budget is under $700 a month; Pinecone Assistant or an open-source framework costs far less to start.

Vectara is an enterprise RAG and agent platform built around trust. Its best-known work is on hallucinations: the Hughes Hallucination Evaluation Model (HHEM) scores whether an answer is supported by the source text. An open version, HHEM-2.1-Open, is free on Hugging Face, and Vectara runs a public leaderboard of how often popular models hallucinate when summarising. In April 2025 it also released Open RAG Eval, an open-source framework for testing RAG systems.

The platform bundles its own retrieval model (Boomerang), its own generation model (Mockingbird) or your choice of ChatGPT, Claude or Gemini, plus hallucination detection and governance at runtime. You can run it as SaaS, in your own VPC or fully on-premises, which suits regulated buyers such as government agencies.

The price rules out small teams. After a 30-day free trial, SaaS starts at $100,000 a year, VPC at $250,000 and on-premises at $500,000.

Pick it if you are a regulated enterprise that needs on-premises RAG with hallucination checks built in. Skip it if you are a startup or a single team; the open-source frameworks above do the core job for free, and anyone can use HHEM-2.1-Open alongside them.