Best Retrieval-Augmented Generation (RAG) Software for Visual Studio Code

Compare the Top Retrieval-Augmented Generation (RAG) Software that integrates with Visual Studio Code as of August 2025

This a list of Retrieval-Augmented Generation (RAG) software that integrates with Visual Studio Code. Use the filters on the left to add additional filters for products that have integrations with Visual Studio Code. View the products that work with Visual Studio Code in the table below.

What is Retrieval-Augmented Generation (RAG) Software for Visual Studio Code?

Retrieval-Augmented Generation (RAG) tools are advanced AI systems that combine information retrieval with text generation to produce more accurate and contextually relevant outputs. These tools first retrieve relevant data from a vast corpus or database, and then use that information to generate responses or content, enhancing the accuracy and detail of the generated text. RAG tools are particularly useful in applications requiring up-to-date information or specialized knowledge, such as customer support, content creation, and research. By leveraging both retrieval and generation capabilities, RAG tools improve the quality of responses in tasks like question-answering and summarization. This approach bridges the gap between static knowledge bases and dynamic content generation, providing more reliable and context-aware results. Compare and read user reviews of the best Retrieval-Augmented Generation (RAG) software for Visual Studio Code currently available using the table below. This list is updated regularly.

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    LM-Kit.NET
    LM-Kit RAG adds context-aware search and answers to C# and VB.NET with one NuGet install and an instant free trial that needs no signup. Hybrid keyword plus vector retrieval runs on local CPU or GPU, feeds only the best chunks to the language model, slashes hallucinations, and keeps every byte inside your stack for privacy and compliance. RagEngine orchestrates modular helpers: DataSource unifies documents and web pages, TextChunking splits files into overlap-aware pieces, and Embedder converts each piece into vectors for lightning-fast similarity search. Workflows run sync or async, scale to millions of passages, and refresh indexes in real time. Use RAG to power knowledge chatbots, enterprise search, legal discovery, and research assistants. Tune chunk sizes, metadata tags, and embedding models to balance recall and latency, while on-device inference delivers predictable cost and zero data leakage.
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  • 2
    Databricks Data Intelligence Platform
    The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker.
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