Compare the Top RAG Frameworks

Looking for the ideal RAG Solution? Effortlessly compare features, and more to discover the Best RAG Framework for you.

2024 Top RAG Frameworks

Introduction

Retrieval-Augmented Generation (RAG) combines information retrieval with language models to generate accurate, data-driven responses. Choosing the right RAG framework is crucial for optimizing AI performance. This page offers a concise comparison of 2024'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.

Pathway

Pathway (GitHub) is a high-throughput, low-latency framework designed to quickly put in production AI applications that offer high-accuracy RAG at scale using the most up-to-date knowledge available in your data sources. With over 350 connectors, it automatically connects and syncs with data sources from Enterprise file systems, Kafka, real-time data APIs, to Sharepoint, S3, PostgreSQL, Google Drive, etc. - with no infrastructure dependencies. It promotes a unified approach where the retriever and LLM are jointly tuned to the specific use case, unlike traditional RAG+LLM solutions that rely on separately built components like embedders, retrievers, and LLMs. This integration minimizes issues such as limited understanding of personalized facts, hallucinations, and miscalibrated confidence, resulting in more accurate and reliable outputs. Pathway's scalable application templates handle millions of documents with consistent performance. It supports both local and cloud deployments through Docker, allowing flexibility in scaling and deployment.

Cohere

Cohere delivers advanced RAG capabilities with its language models, focusing on high-throughput, multilingual use cases in enterprise settings. Its cloud-agnostic AI solutions ensure seamless integration with existing data infrastructures and uphold the highest levels of security, privacy, and customization, including options for on-premises and private cloud deployments. This makes it suitable for businesses with stringent security and operational flexibility requirements.

LlamaIndex SaaS

LlamaIndex is a SaaS-based framework that enhances RAG capabilities with efficient indexing and advanced retrieval capabilities. It supports various index types—such as Tree, List, Vector Store, and Keyword Table Indexes—catering to diverse data retrieval needs from large-scale datasets. It integrates with popular LLMs and vector storage solutions, allowing for rapid and accurate semantic searches and data retrieval. Key features include hierarchical indexing for complex queries, straightforward linear searches for smaller datasets, and fast similarity searches ideal for document retrieval and recommendation systems.

LangChain

LangChain is an orchestrator designed to integrate LLMs with external data sources and APIs. Its modular architecture allows for flexible adaptation and scaling. LangGraph, part of LangChain, facilitates building stateful, multi-actor applications, allowing the creation of agent and multi-agent workflows. This enables the development of advanced, interactive applications. LangChain simplifies the LLM lifecycle by offering open-source building blocks and third-party integrations for development, LangSmith for monitoring and evaluating chains in production, and LangGraph Cloud for deploying production-ready APIs and assistants.

Haystack

Haystack is a flexible open-source AI framework designed for building end-to-end applications powered by large language models (LLMs). It simplifies complexity and enables development at a higher level of abstraction. Components like models, vector databases, and file converters can be connected to create pipelines or agents that interact with data. With advanced retrieval methods, Haystack is suited for tasks such as Retrieval-Augmented Generation (RAG), question answering, semantic search, and chatbots.

DSPY

DSPy is a framework designed to optimize language model (LM) prompts and weights, especially when LMs are used multiple times within a pipeline. Traditionally, building complex systems with LMs involves breaking down tasks, manually tuning prompts for each step, and fine-tuning models to cut costs—processes that become time-consuming and error-prone as pipelines evolve. DSPy simplifies this by separating the program’s flow from its parameters, introducing optimizers that algorithmically adjust prompts and weights based on target metrics. It enhances the reliability of models like GPT-4, T5-base, or Llama2-13b, ensuring higher-quality results while reducing manual prompting and tuning.

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

The Assistants API enables developers to build AI-powered assistants that leverage OpenAI’s models, tools, and files to handle complex tasks. It supports three main tools: Code Interpreter, File Search, and Function Calling. Assistants can run code produced by the LLM through the Code Interpreter, search and retrieve files, and interact with external systems via Function Calling, which developers set up to execute specific functions. Assistants can use multiple tools in parallel and operate within persistent threads, which store conversation history and manage context over time. The API tracks each assistant's actions, providing a clear breakdown of tasks through run steps.

Comparison table

Name Pathway Cohere LlamaIndex SaaS Langchain Haystack DSPY OpenAI API
with the ability to upload files (Assistant API)
Deployment
Cloud-native Deployment ✅ ❌ ❌ ✅ ✅ ❌ ❌
Local Deployment (Web server support) ✅ ❌ (Not Applicable) ❌ ✅ ⚠️ Via Ray ❌ ❌
Built-in VectorDB options ✅ ❌ ✅ ✅ ✅ ✅ ⚠️
Deployment packaging and handoff (provide templates) ✅ ❌ ✅ ✅ ✅ ✅ ❌
Data sources and Connectors
Static data connection ✅ ❌ ✅ ✅ ⚠️ User managed ❌ ⚠️ Via file upload
Dynamic data connection ✅ ❌ ✅ in the Enterprise version (LlamaHub) ❌ ❌ ❌ ❌
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
✅ Vector / Full-text / Hybrid in standard library.
Build & customize indexes as needed. Native support for graph representation.
More flexible knowledge graph creation on the roadmap.
 ❌ ✅ ✅ ⚠️ Hybrid only ❌ ❌
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 ✅ ❌ ✅ (Cloud) ❌ ❌ ❌ ❌
Advanced prompting and evaluation
Agentic RAG ✅ with LlamaIndex ✅ 🐌 via API only ✅ ✅ ⚠️ ✅ ⚠️ Via Assistant API
Observability (Only considering LLM specialized observability, such as TraceLoop) ✅ Via OpenTelemetry for now ❌ ⚠️ No built-in platforms but can integrate with third-party platforms ✅ ⚠️ ⚠️ ✅
End-to-end quality evaluation / Integration with evaluation libraries (RAGAS) ⚠️ No built-in integration, but easily achievable. ❌ ✅ ✅ ✅ ✅ ❌
Customization
Fully customizable and source-available data pipelines - with YAML and Python ✅ ❌ ✅ No YAML ❌ ⚠️ Not fully customizable ❌ ❌
Customizability of core modules - Parse, Chunk, Embed, Data Sources ✅ ❌ ✅ ✅ ✅ ⚠️ Not Applicable ❌
Creating Custom flows & logic ✅ for power-users. ❌ ✅ ⚠️ ✅ ✅ ⚠️

Caption:

Yes - ✅

Complicated - ⚠️

No - ❌

Not scalable - 🐌

TL;DR

This guide compares the top 2024 RAG solutions 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.