Compare the Top 7 RAG Frameworks in 2025

Looking for the ideal RAG Solution?

Effortlessly compare features, and more to discover the Best RAG Framework for you.

What Are RAG Frameworks & What Do They Do?

Retrieval-Augmented Generation (RAG) combine information retrieval with large language models to generate accurate, data-driven responses. RAG extends the already powerful capabilities of LLMs to specific domains or an organization's internal knowledge base, all without the need to retrain the model.

As the demand for productionizing AI solutions grows, choosing the right RAG framework becomes crucial. This page offers a concise comparison of 2025's top RAG frameworks, highlighting key features like deployment options, data connectors, and advanced RAG capabilities.

Whether you need a highly scalable enterprise solution or a flexible open-source framework, this guide helps you quickly identify the best fit for your needs.

The Top 7 RAG Frameworks in 2025: Comparison Table

Name	Pathway	Cohere	LlamaCloud	LangChain	Haystack	DSPY	OpenAI API
Deployment
Cloud-native Deployment	✅	❌	❌	✅	✅	❌	❌
Local Deployment (Web server support)	✅	❌	❌	✅	⚠️	❌	❌
Built-in VectorDB options	✅	❌	✅	✅	✅	✅	⚠️
Deployment packaging and handoff (provide templates)	✅	❌	✅	✅	✅	✅	❌
Data sources and Connectors
Static data connection	✅	❌	✅	✅	⚠️	❌	⚠️
Dynamic data connection	✅	❌	✅	❌	❌	❌	❌
Connector ecosystem	✅	❌	✅	✅	❌	❌	❌
RAG features
Compatible with different Model vendors (including Open source models for LLM and embedding)	✅	❌	✅	✅	✅	✅	❌
Advanced Index Support, Knowledge-graph creation & retrieval, Hybrid Index	✅	❌	✅	✅	⚠️	❌	❌
Document Processing (Parsing, chunking) support	✅	❌	✅	✅	✅	❌	⚠️
Specialized use cases
Custom document and data ingestion & transformation workflows	✅	❌	❌	❌	❌	❌	❌
Custom RAG and problem specific Q&A solutions	✅	❌	✅	✅	✅	❌	❌
Specialized end-user UI available	✅	❌	✅	❌	❌	❌	❌
Advanced prompting and evaluation
Agentic RAG	✅	✅🐌	✅	✅	⚠️	✅	⚠️
Observability (Only considering LLM specialized observability, such as TraceLoop)	✅	❌	⚠️	✅	⚠️	⚠️	✅
End-to-end quality evaluation / Integration with evaluation libraries (RAGAS)	⚠️	❌	✅	✅	✅	✅	❌
Customization
Fully customizable and source-available data pipelines - with YAML and Python	✅	❌	✅	❌	⚠️	❌	❌
Customizability of core modules - Parse, Chunk, Embed, Data Sources	✅	❌	✅	✅	✅	⚠️	❌
Creating Custom flows & logic	✅	❌	✅	⚠️	✅	✅	⚠️

Caption:

Yes - ✅

Complicated - ⚠️

No - ❌

Not scalable - 🐌

More Information about the Top 7 RAG Frameworks in comparison

Pathway

Website: https://pathway.com

Pathway (GitHub) is a high-throughput, low-latency framework designed for building and deploying RAG-powered AI applications at scale. It offers a cloud-agnostic, container-based approach with over 350 data source connectors. It integrates YAML, Python, and SQL for flexible configuration and supports 300+ data connectors, including S3, Delta Lake, Iceberg, Kafka, NATS, and SharePoint.
It eliminates infrastructure complexity by unifying data ingestion, unstructured parsing, vector/hybrid indexing and LLM-driven retrieval into a single, integrated workflow. By jointly optimizing the retriever and language model, it reduces hallucinations and boosts accuracy. Its lightweight ETL and data‑sync engine enables near‑real‑time updates, while built-in AI templates simplify the setup for PDF parsing, file system ingestion, and more.
Choose Pathway if you need scalable, real‑time data processing and end‑to‑end RAG without juggling separate tools for ETL, indexing and LLM orchestration. Its cross‑cloud deployment model provides flexibility, while the ability to “glue” application logic with YAML/Python/SQL keeps your workflow configurable and transparent. It supports both local and cloud deployments through Docker, allowing flexibility in scaling and deployment.

Cohere

Website: https://cohere.com

Cohere is a leading provider of enterprise-grade LLMs and AI tools designed for building secure, private, and scalable language model applications. Their platform includes state-of-the-art models for text generation, search ranking, embeddings, and retrieval-augmented generation (RAG).
Cohere offers the Command family of generative models for chat, summarization, and copywriting, along with Rerank for intelligent search and Embed for improved classification, clustering, and retrieval. Developers can integrate Cohere’s AI via an API, cloud AI platforms (AWS, GCP, Azure, Oracle), private cloud, or even on-premise deployments, ensuring flexibility across different security needs.
Cohere is ideal for enterprise AI teams seeking high-quality LLMs with fine-tuning capabilities, RAG optimization, and enhanced privacy options, including VPC and on-premise deployments. Their multi-platform accessibility and powerful search and retrieval tools make them a strong choice for building advanced, production-ready AI applications.

LlamaIndex (with a Note on LlamaCloud)

Website: https://www.llamaindex.ai

LlamaIndex is an open-source Python library designed for building AI knowledge assistants. The same team behind LlamaIndex also offers LlamaCloud (a managed SaaS platform) that handles ingestion, parsing, and advanced retrieval as a service.
LlamaIndex enables data integration with enterprise sources such as PDFs, SharePoint, Google Drive, and databases, making unstructured data LLM-ready. It offers production-grade AI pipelines that enhance information retrieval, provide insights, and automate reporting. For heavier production workloads, LlamaCloud adds automated pipelines, specialized PDF parsing (via LlamaParse), and a user-friendly GUI to reduce engineering overhead.
LlamaIndex provides a flexible, scalable approach to knowledge retrieval, offering both open-source and managed solutions. With its enterprise integrations, built-in optimizations, and secure cloud offerings (via LlamaCloud), it is a good choice for businesses looking to deploy AI-powered solutions.

LangChain

Website: https://www.langchain.com

LangChain is a popular open-source framework (Python / TypeScript) for building complex LLM applications using a “chain of calls” approach. It was among the first to pioneer prompt orchestration and “agent” abstractions for LLMs.
LangChain helps developers structure their RAG pipeline as a series of composable steps: prompt engineering, calling an LLM, retrieving data from a vector store, performing additional reasoning steps, etc. It also integrates with many different LLM providers and data tools.
LangChain’s strength is in its modular design and large ecosystem. If you’re building an LLM app that requires more advanced chaining, tool usage, or multi-step reasoning, LangChain is a great choice. It has an active community and many contributed examples.

Haystack

Website: https://haystack.deepset.ai

Haystack is a AI orchestration framework for building production-ready LLM applications, retrieval-augmented generative pipelines and state-of-the-art search systems that work intelligently over large document collections.
It provides a modular architecture (with “components” and “pipelines”), allowing you to integrate advanced models (OpenAI, Cohere, Hugging Face, etc.), connect to multiple document stores, and handle everything from data ingestion to evaluation and logging.
Haystack is a fully open source solution for building and deploying LLM apps—from ingestion and preprocessing, to retrieval, to advanced generative flows. Its pipeline architecture makes it easy to experiment, customize, and scale; you can integrate with an ever‑growing list of document stores, model providers, and community‑built components.

DSPY

Website: https://dspy.ai

DSPy is an open-source framework for building modular, “programmed” language model systems (rather than just prompt-hacking). Created within the Stanford NLP community and backed by 250+ contributors, DSPy stands for Declarative Self-improving Python and centers on writing compositional AI code.
By letting you declare your AI components and goals in structured Python, DSPy auto-generates prompts and parses LM outputs behind the scenes. Its built-in optimizers (e.g., dspy.MIPROv2) fine-tune prompts or even model weights based on metrics you define—so you can rapidly iterate and improve your AI systems without endlessly tinkering with strings.
If you need faster iteration, maintainable code, and a broad research ecosystem, DSPy’s modular paradigm offers powerful tools to compose, optimize, and refine your LLM pipelines. It’s an ideal choice for teams who want fine-grained control over their AI systems’ behavior while taking advantage of a cutting-edge open-source community.

OpenAI API with the ability to upload files (Assistants API)

Website: https://openai.com/api

OpenAI’s Assistants API is a beta feature that enables developers to build AI “assistants” (chatbots or agents) within their own applications. These assistants can leverage OpenAI models, various tools (like code interpreter and file search), and persistent threads to handle user queries and maintain conversation context.
By integrating with the Assistants API, you can craft AI personalities and capabilities through instructions, upload and reference files, and chain calls to multiple tools or custom functions. Threads provide a straightforward way to store conversation histories without exceeding the model’s context window. As a result, developers can build more advanced, context-aware chat experiences and incorporate external data sources or tools seamlessly.
If you already rely on OpenAI’s models, the Assistants API offers a powerful extension for creating multi-step, tool-augmented AI workflows with minimal overhead. Its thread-based approach simplifies conversation management and makes it easy to persist context or reference files. For teams looking to leverage cutting-edge OpenAI features (like code execution or function calling) under one unified interface, the Assistants API can significantly accelerate and streamline development.

Looking to build an enterprise-grade RAG app?

This guide compares the top 2025 RAG frameworks/tools based on deployment flexibility, data connectors, and advanced features, helping you find the perfect fit. If you’re interested in exploring RAG with Pathway, we’ve got you covered.

Book a meeting to discuss how Pathway can fit your needs here.
Explore Pathway’s App Templates for a seamless start to RAG development for production applications.
Check out our developer documentation and LLM xpack page for technical resources here.

Pathway is trusted by industry leaders such as NATO and Intel, and is natively available on both AWS and Azure Marketplaces. If you’d like to explore how Pathway can support your RAG and Generative AI initiatives, we invite you to schedule a discovery session with our team.

Schedule a 15-minute demo with one of our experts to see how Pathway can be the right solution for your enterprise.