Best Free Retrieval-Augmented Generation (RAG) Software of 2025

LM-Kit.NET effortlessly incorporates generative AI into your software solutions. Tailored for C# and VB.NET, it boasts enterprise-level capabilities that simplify the development, personalization, and deployment of intelligent agents, establishing a benchmark for swift AI integration. One of its key highlights is the sophisticated Retrieval-Augmented Generation (RAG) feature. This technology allows for the dynamic collection and merging of pertinent external information with internal context, significantly enhancing text generation to produce highly precise and contextually relevant responses. This method not only improves the logical flow of AI-generated content but also enriches it with up-to-date, factual information. Leverage the capabilities of RAG through LM-Kit.NET to create more intelligent and flexible applications. Whether you're enhancing customer service, streamlining content generation, or advancing data analysis, the RAG integration within LM-Kit.NET ensures your solutions stay agile and well-informed in a constantly evolving data environment.

Mistral AI stands out as an innovative startup in the realm of artificial intelligence, focusing on open-source generative solutions. The company provides a diverse array of customizable, enterprise-level AI offerings that can be implemented on various platforms, such as on-premises, cloud, edge, and devices. Among its key products are "Le Chat," a multilingual AI assistant aimed at boosting productivity in both personal and professional settings, and "La Plateforme," a platform for developers that facilitates the creation and deployment of AI-driven applications. With a strong commitment to transparency and cutting-edge innovation, Mistral AI has established itself as a prominent independent AI laboratory, actively contributing to the advancement of open-source AI and influencing policy discussions. Their dedication to fostering an open AI ecosystem underscores their role as a thought leader in the industry.

Cohere is a robust enterprise AI platform that empowers developers and organizations to create advanced applications leveraging language technologies. With a focus on large language models (LLMs), Cohere offers innovative solutions for tasks such as text generation, summarization, and semantic search capabilities. The platform features the Command family designed for superior performance in language tasks, alongside Aya Expanse, which supports multilingual functionalities across 23 different languages. Emphasizing security and adaptability, Cohere facilitates deployment options that span major cloud providers, private cloud infrastructures, or on-premises configurations to cater to a wide array of enterprise requirements. The company partners with influential industry players like Oracle and Salesforce, striving to weave generative AI into business applications, thus enhancing automation processes and customer interactions. Furthermore, Cohere For AI, its dedicated research lab, is committed to pushing the boundaries of machine learning via open-source initiatives and fostering a collaborative global research ecosystem. This commitment to innovation not only strengthens their technology but also contributes to the broader AI landscape.

Lettria presents a robust AI solution called GraphRAG, aimed at improving the precision and dependability of generative AI applications. By integrating the advantages of knowledge graphs with vector-based AI models, Lettria enables organizations to derive accurate answers from intricate and unstructured data sources. This platform aids in streamlining various processes such as document parsing, data model enhancement, and text classification, making it particularly beneficial for sectors including healthcare, finance, and legal. Furthermore, Lettria’s AI offerings effectively mitigate the occurrences of hallucinations in AI responses, fostering transparency and confidence in the results produced by AI systems. The innovative design of GraphRAG also allows businesses to leverage their data more effectively, paving the way for informed decision-making and strategic insights.

Prophecy expands accessibility for a wider range of users, including visual ETL developers and data analysts, by allowing them to easily create pipelines through a user-friendly point-and-click interface combined with a few SQL expressions. While utilizing the Low-Code designer to construct workflows, you simultaneously generate high-quality, easily readable code for Spark and Airflow, which is then seamlessly integrated into your Git repository. The platform comes equipped with a gem builder, enabling rapid development and deployment of custom frameworks, such as those for data quality, encryption, and additional sources and targets that enhance the existing capabilities. Furthermore, Prophecy ensures that best practices and essential infrastructure are offered as managed services, simplifying your daily operations and overall experience. With Prophecy, you can achieve high-performance workflows that leverage the cloud's scalability and performance capabilities, ensuring that your projects run efficiently and effectively. This powerful combination of features makes it an invaluable tool for modern data workflows.

Airbyte is a data integration platform that operates on an open-source model, aimed at assisting organizations in unifying data from diverse sources into their data lakes, warehouses, or databases. With an extensive library of over 550 ready-made connectors, it allows users to craft custom connectors with minimal coding through low-code or no-code solutions. The platform is specifically designed to facilitate the movement of large volumes of data, thereby improving artificial intelligence processes by efficiently incorporating unstructured data into vector databases such as Pinecone and Weaviate. Furthermore, Airbyte provides adaptable deployment options, which help maintain security, compliance, and governance across various data models, making it a versatile choice for modern data integration needs. This capability is essential for businesses looking to enhance their data-driven decision-making processes.

Whether you're developing an AI assistant, chatbot, or improving your current application with LLMs, Graphlit simplifies the process. It operates on a serverless, cloud-native architecture that streamlines intricate data workflows, encompassing data ingestion, knowledge extraction, LLM interactions, semantic searches, alert notifications, and webhook integrations. With Graphlit's workflow-as-code methodology, you can systematically outline every phase of the content workflow. This includes everything from data ingestion to metadata indexing and data preparation, as well as from data sanitization to entity extraction and data enrichment. Ultimately, it facilitates seamless integration with your applications through event-driven webhooks and API connections, making the entire process more efficient and user-friendly. This flexibility ensures that developers can tailor workflows to meet specific needs without unnecessary complexity.

Swirl effortlessly integrates with your enterprise applications, offering real-time data access. It enables secure retrieval-augmented generation from your corporate data without storing any information, while functioning effectively within your firewall. Additionally, Swirl can easily link to your proprietary language models. With Swirl Search, your organization gains an innovative solution that delivers rapid access to all necessary information across various data sources. The platform features multiple connectors designed for popular applications and services, allowing for a seamless connection. There’s no need for data migration, as Swirl harmonizes with your existing systems to maintain data security and uphold privacy standards. Tailored specifically for enterprises, Swirl recognizes that transferring data solely for search and AI integration can be costly and inefficient. By offering a federated and unified search experience, Swirl provides a superior alternative for businesses looking to optimize their data utilization. This approach not only enhances productivity but also streamlines the search process across diverse data environments.

HyperCrawl is an innovative web crawler tailored specifically for LLM and RAG applications, designed to create efficient retrieval engines. Our primary aim was to enhance the retrieval process by minimizing the time spent crawling various domains. We implemented several advanced techniques to forge a fresh ML-focused approach to web crawling. Rather than loading each webpage sequentially (similar to waiting in line at a grocery store), it simultaneously requests multiple web pages (akin to placing several online orders at once). This strategy effectively eliminates idle waiting time, allowing the crawler to engage in other tasks. By maximizing concurrency, the crawler efficiently manages numerous operations at once, significantly accelerating the retrieval process compared to processing only a limited number of tasks. Additionally, HyperLLM optimizes connection time and resources by reusing established connections, much like opting to use a reusable shopping bag rather than acquiring a new one for every purchase. This innovative approach not only streamlines the crawling process but also enhances overall system performance.

Introducing an open-source AI model that can be fine-tuned, distilled, and deployed across various platforms. Our newest instruction-tuned model comes in three sizes: 8B, 70B, and 405B, giving you options to suit different needs. With our open ecosystem, you can expedite your development process using a diverse array of tailored product offerings designed to meet your specific requirements. You have the flexibility to select between real-time inference and batch inference services according to your project's demands. Additionally, you can download model weights to enhance cost efficiency per token while fine-tuning for your application. Improve performance further by utilizing synthetic data and seamlessly deploy your solutions on-premises or in the cloud. Take advantage of Llama system components and expand the model's capabilities through zero-shot tool usage and retrieval-augmented generation (RAG) to foster agentic behaviors. By utilizing 405B high-quality data, you can refine specialized models tailored to distinct use cases, ensuring optimal functionality for your applications. Ultimately, this empowers developers to create innovative solutions that are both efficient and effective.

Ragie simplifies the processes of data ingestion, chunking, and multimodal indexing for both structured and unstructured data. By establishing direct connections to your data sources, you can maintain a consistently updated data pipeline. Its advanced built-in features, such as LLM re-ranking, summary indexing, entity extraction, and flexible filtering, facilitate the implementation of cutting-edge generative AI solutions. You can seamlessly integrate with widely used data sources, including Google Drive, Notion, and Confluence, among others. The automatic synchronization feature ensures your data remains current, providing your application with precise and trustworthy information. Ragie’s connectors make integrating your data into your AI application exceedingly straightforward, allowing you to access it from its original location with just a few clicks. The initial phase in a Retrieval-Augmented Generation (RAG) pipeline involves ingesting the pertinent data. You can effortlessly upload files directly using Ragie’s user-friendly APIs, paving the way for streamlined data management and analysis. This approach not only enhances efficiency but also empowers users to leverage their data more effectively.

Oversees the complete lifecycle of developing, testing, deploying, and operating LLM applications seamlessly, eliminating the need to integrate various systems. This approach ensures the lowest total cost of ownership (TCO). It incorporates a vector database and search engine that surpasses all major competitors, boasting query latency that is 10 times faster, query throughput that is five times greater, and costs that are three times lower. It represents a cutting-edge data and knowledge infrastructure that adeptly handles extensive, multi-modal unstructured and structured data. You can rest easy knowing that outdated information will never be an issue. Effortlessly integrate with advanced, modular, agentic RAG and GraphRAG techniques without the necessity of writing complex plumbing code. Thanks to CI/CD-style evaluations, you can make configuration modifications to your AI applications confidently, without the fear of introducing regressions. This enables you to speed up your iterations, allowing you to transition to production within days instead of months. Additionally, it features fine-grained access control based on roles and privileges, ensuring that security is maintained throughout the process. This comprehensive framework not only enhances efficiency but also fosters a more agile development environment.

The latest iteration of the open-source AI model, which can be fine-tuned and deployed in various environments, is now offered in multiple versions, including 1B, 3B, 11B, and 90B, alongside the option to continue utilizing Llama 3.1. Llama 3.2 comprises a series of large language models (LLMs) that come pretrained and fine-tuned in 1B and 3B configurations for multilingual text only, while the 11B and 90B models accommodate both text and image inputs, producing text outputs. With this new release, you can create highly effective and efficient applications tailored to your needs. For on-device applications, such as summarizing phone discussions or accessing calendar tools, the 1B or 3B models are ideal choices. Meanwhile, the 11B or 90B models excel in image-related tasks, enabling you to transform existing images or extract additional information from images of your environment. Overall, this diverse range of models allows developers to explore innovative use cases across various domains.

Vectorize is a specialized platform that converts unstructured data into efficiently optimized vector search indexes, enhancing retrieval-augmented generation workflows. Users can import documents or establish connections with external knowledge management systems, enabling the platform to extract natural language that is compatible with large language models. By evaluating various chunking and embedding strategies simultaneously, Vectorize provides tailored recommendations while also allowing users the flexibility to select their preferred methods. After a vector configuration is chosen, the platform implements it into a real-time pipeline that adapts to any changes in data, ensuring that search results remain precise and relevant. Vectorize features integrations with a wide range of knowledge repositories, collaboration tools, and customer relationship management systems, facilitating the smooth incorporation of data into generative AI frameworks. Moreover, it also aids in the creation and maintenance of vector indexes within chosen vector databases, further enhancing its utility for users. This comprehensive approach positions Vectorize as a valuable tool for organizations looking to leverage their data effectively for advanced AI applications.

Inquir is a cutting-edge platform powered by artificial intelligence, designed to empower users in crafting bespoke search engines that cater specifically to their unique data requirements. The platform boasts features such as the ability to merge various data sources, create Retrieval-Augmented Generation (RAG) systems, and implement search functionalities that are sensitive to context. Notable characteristics of Inquir include its capacity for scalability, enhanced security measures with isolated infrastructure for each organization, and an API that is friendly for developers. Additionally, it offers a faceted search capability for streamlined data exploration and an analytics API that further enriches the search process. With flexible pricing options available, from a free demo access tier to comprehensive enterprise solutions, Inquir meets the diverse needs of businesses of all sizes. By leveraging Inquir, organizations can revolutionize product discovery, ultimately boosting conversion rates and fostering greater customer loyalty through swift and effective search experiences. With its robust tools and features, Inquir stands ready to transform how users interact with their data.

The newest version in the Llama series, Llama 3.3, represents a significant advancement in language models aimed at enhancing AI's capabilities in understanding and communication. It boasts improved contextual reasoning, superior language generation, and advanced fine-tuning features aimed at producing exceptionally accurate, human-like responses across a variety of uses. This iteration incorporates a more extensive training dataset, refined algorithms for deeper comprehension, and mitigated biases compared to earlier versions. Llama 3.3 stands out in applications including natural language understanding, creative writing, technical explanations, and multilingual interactions, making it a crucial asset for businesses, developers, and researchers alike. Additionally, its modular architecture facilitates customizable deployment in specific fields, ensuring it remains versatile and high-performing even in large-scale applications. With these enhancements, Llama 3.3 is poised to redefine the standards of AI language models.

RAGFlow is a publicly available Retrieval-Augmented Generation (RAG) system that improves the process of information retrieval by integrating Large Language Models (LLMs) with advanced document comprehension. This innovative tool presents a cohesive RAG workflow that caters to organizations of all sizes, delivering accurate question-answering functionalities supported by credible citations derived from a range of intricately formatted data. Its notable features comprise template-driven chunking, the ability to work with diverse data sources, and the automation of RAG orchestration, making it a versatile solution for enhancing data-driven insights. Additionally, RAGFlow's design promotes ease of use, ensuring that users can efficiently access relevant information in a seamless manner.

FastGPT is a versatile, open-source AI knowledge base platform that streamlines data processing, model invocation, and retrieval-augmented generation, as well as visual AI workflows, empowering users to create sophisticated large language model applications with ease. Users can develop specialized AI assistants by training models using imported documents or Q&A pairs, accommodating a variety of formats such as Word, PDF, Excel, Markdown, and links from the web. Additionally, the platform automates essential data preprocessing tasks, including text refinement, vectorization, and QA segmentation, which significantly boosts overall efficiency. FastGPT features a user-friendly visual drag-and-drop interface that supports AI workflow orchestration, making it simpler to construct intricate workflows that might incorporate actions like database queries and inventory checks. Furthermore, it provides seamless API integration, allowing users to connect their existing GPT applications with popular platforms such as Discord, Slack, and Telegram, all while using OpenAI-aligned APIs. This comprehensive approach not only enhances user experience but also broadens the potential applications of AI technology in various domains.

Supavec is an innovative open-source Retrieval-Augmented Generation (RAG) platform that empowers developers to create robust AI applications capable of seamlessly connecting with any data source, no matter the size. Serving as a viable alternative to Carbon.ai, Supavec grants users complete control over their AI infrastructure, offering the flexibility to choose between a cloud-based solution or self-hosting on personal systems. Utilizing advanced technologies such as Supabase, Next.js, and TypeScript, Supavec is designed for scalability and can efficiently manage millions of documents while supporting concurrent processing and horizontal scaling. The platform prioritizes enterprise-level privacy by implementing Supabase Row Level Security (RLS), which guarantees that your data is kept secure and private with precise access controls. Developers are provided with a straightforward API, extensive documentation, and seamless integration options, making it easy to set up and deploy AI applications quickly. Furthermore, Supavec's focus on user experience ensures that developers can innovate rapidly, enhancing their projects with cutting-edge AI capabilities.

Launch & train personalized AI-RAG Agents on Telegram. You can create RAG AI-powered personalized agents in minutes with scalerX, and they will be trained using your knowledge base. These AI agents can be integrated directly into Telegram including groups and channels. This is great for education, customer service, entertainment and sales. It also automates community moderation. Agents can act as chatbots for solo, groups, and channels. They support text-to text, text-to image, and voice. ACLs allow you to set up agent usage quotas, and permissions for authorized users. It's easy to train your agents: Create your agent, upload files to the bots knowledgebase, or auto-sync Dropbox, Google Drive, or scrape webpages.

Scalable Python framework designed to build real-time intelligent applications, data pipelines, and integrate AI/ML models

Create your RAG system using a more straightforward approach than options such as LangChain, enabling you to select from an extensive array of hosted and remote services for vector databases, datasets, Large Language Models (LLMs), and application integrations. Leverage SciPhi to implement version control for your system through Git and deploy it from any location. SciPhi's platform is utilized internally to efficiently manage and deploy a semantic search engine that encompasses over 1 billion embedded passages. The SciPhi team will support you in the embedding and indexing process of your initial dataset within a vector database. After this, the vector database will seamlessly integrate into your SciPhi workspace alongside your chosen LLM provider, ensuring a smooth operational flow. This comprehensive setup allows for enhanced performance and flexibility in handling complex data queries.

Cohere has introduced Command R+, its latest large language model designed to excel in conversational interactions and manage long-context tasks with remarkable efficiency. This model is tailored for organizations looking to transition from experimental phases to full-scale production. We suggest utilizing Command R+ for workflows that require advanced retrieval-augmented generation capabilities and the use of multiple tools in a sequence. Conversely, Command R is well-suited for less complicated retrieval-augmented generation tasks and scenarios involving single-step tool usage, particularly when cost-effectiveness is a key factor in decision-making.

Experience the power of localized and secure AI right on your desktop, providing you with in-depth insights while maintaining complete data security and privacy. Our innovative macOS-native application combines efficiency, privacy, and intelligence through its state-of-the-art AI functionalities. The RAG system is capable of tapping into data from a local knowledge base to enhance the capabilities of the large language model (LLM), allowing you to keep sensitive information on-site while improving the quality of responses generated by the model. To set up RAG locally, you begin by breaking down documents into smaller segments, encoding these segments into vectors, and storing them in a vector database for future use. This vectorized information will play a crucial role during retrieval operations. When a user submits a query, the system fetches the most pertinent segments from the local knowledge base, combining them with the original query to formulate an accurate response using the LLM. Additionally, we are pleased to offer individual users lifetime free access to our application. By prioritizing user privacy and data security, our solution stands out in a crowded market.

Provide exceptional responses through a vector database specifically designed for cutting-edge retrieval augmented generation (RAG) and contemporary search methodologies. Prioritize rapid growth with an enterprise-grade vector database that integrates security measures, compliance standards, and ethical AI practices. Enhance your applications with advanced retrieval techniques that are supported by extensive research and proven customer success. Quickly launch your generative AI application with effortless integrations of platforms and data for various sources, AI models, and frameworks. Facilitate the automatic upload of data from a diverse array of Azure and third-party options. Optimize vector data handling with integrated processes for extraction, chunking, enrichment, and vectorization, ensuring a seamless workflow. Offer support for multivector capabilities, hybrid approaches, multilingual options, and metadata filtering. Transition beyond just vector-based searching by incorporating keyword match scoring, reranking, geospatial search features, and autocomplete functionalities, thus ensuring a more comprehensive search experience. This robust system not only enhances retrieval efficiency but also empowers users to derive greater insights from their data.