List of the Top 8 AI Tools for Jupyter Notebook in 2025

Windsurf is an innovative IDE built to support developers with AI-powered features that streamline the coding and deployment process. Cascade, the platform’s intelligent assistant, not only fixes issues proactively but also helps developers anticipate potential problems, ensuring a smooth development experience. Windsurf’s features include real-time code previewing, automatic lint error fixing, and memory tracking to maintain project continuity. The platform integrates with essential tools like GitHub, Slack, and Figma, allowing for seamless workflows across different aspects of development. Additionally, its built-in smart suggestions guide developers towards optimal coding practices, improving efficiency and reducing technical debt. Windsurf’s focus on maintaining a flow state and automating repetitive tasks makes it ideal for teams looking to increase productivity and reduce development time. Its enterprise-ready solutions also help improve organizational productivity and onboarding times, making it a valuable tool for scaling development teams.

Tabnine serves as an AI-driven coding assistant that empowers development teams of all sizes to leverage artificial intelligence, thereby enhancing and streamlining the software development lifecycle while ensuring privacy, security, and compliance are maintained. By automating various coding tasks, Tabnine elevates engineering productivity, improves code quality, and increases developer satisfaction, offering tailored AI solutions that cater to each team's unique needs. With over a million developers relying on it, Tabnine has gained trust across countless organizations. Providing top-tier AI code completion and a chat feature, Tabnine accommodates a wide array of applications, including code generation, code explanation, unit test creation, documentation generation, code debugging, refactoring, and overall code maintenance. It seamlessly integrates with all major integrated development environments (IDEs) like VS Code, JetBrains, Visual Studio, and Eclipse, while also supporting more than 80 programming languages and frameworks, such as JavaScript, TypeScript, Python, Java, C, C++, C#, Go, PHP, Ruby, Kotlin, Dart, Rust, and many others. This extensive compatibility enables developers to enhance their coding practices across diverse projects, making Tabnine a vital tool in the modern development landscape.

BLACKBOX AI code search is designed for developers to efficiently locate optimal code snippets across a wide array of over 20 programming languages, such as Python, JavaScript, TypeScript, Ruby, Go, and more. This tool is integrated with popular IDEs like VS Code and GitHub Codespaces, as well as platforms including Jupyter Notebook and Paperspace. With support for languages such as C#, Java, C++, SQL, PHP, and TypeScript, users can effortlessly search for code fragments within their coding environment without needing to switch applications. BLACKBOX enables users to select code from any video and seamlessly transfer it to their text editor while maintaining proper indentation. The Pro plan further extends the functionality, allowing access to copy text from over 200 programming languages, making it an invaluable resource for developers striving to build exceptional products and streamline their workflows. Additionally, this versatility ensures that developers are equipped with a comprehensive toolset for their diverse coding needs.

Enhance your coding journey with AI by generating top-notch code effortlessly and rapidly. With the help of AI Autocomplete, you can quickly create prototypes and elevate your code to meet production quality standards without hassle. Our cutting-edge neural networks minimize time spent on repetitive tasks and searching for answers on sites like Stack Overflow. You have the ability to easily refactor, document, and add types to your codebase, while also issuing direct commands to the AI for any changes you need. If crafting tests isn’t your favorite aspect of development, you can relax; we offer the automatic creation of unit tests using AI and metaprogramming methods. We foresee a future where AI significantly revolutionizes software development. Our primary objective is to enable developers to operate at the speed of thought, thereby nurturing both innovation and productivity. Furthermore, we believe that emerging advancements in AI will introduce new programming approaches that move away from traditional IDEs and version control systems, fundamentally altering the way we engage with coding. This transformation will not only streamline workflows but also unlock new potentials in software creation that we have yet to fully explore.

PostgresML is an all-encompassing platform embedded within a PostgreSQL extension, enabling users to create models that are not only more efficient and rapid but also scalable within their database setting. Users have the opportunity to explore the SDK and experiment with open-source models that are hosted within the database. This platform streamlines the entire workflow, from generating embeddings to indexing and querying, making it easier to build effective knowledge-based chatbots. Leveraging a variety of natural language processing and machine learning methods, such as vector search and custom embeddings, users can significantly improve their search functionalities. Moreover, it equips businesses to analyze their historical data via time series forecasting, revealing essential insights that can drive strategy. Users can effectively develop statistical and predictive models while taking advantage of SQL and various regression techniques. The integration of machine learning within the database environment facilitates faster result retrieval alongside enhanced fraud detection capabilities. By simplifying the challenges associated with data management throughout the machine learning and AI lifecycle, PostgresML allows users to run machine learning and large language models directly on a PostgreSQL database, establishing itself as a powerful asset for data-informed decision-making. This innovative methodology ultimately optimizes processes and encourages a more effective deployment of data resources. In this way, PostgresML not only enhances efficiency but also empowers organizations to fully capitalize on their data assets.

Forloop is an innovative no-code platform specifically crafted to streamline the automation of external data processes. This solution empowers users to escape the limitations of internal data sources and access the latest market insights, facilitating rapid adjustments, tracking of market changes, and enhancement of pricing strategies. By utilizing external data, organizations can uncover deeper insights that extend beyond their current repositories. With Forloop, you won't have to compromise between tools ideal for initial prototypes and those that are fully functional within your preferred cloud environment. The platform allows for efficient data access and extraction from non-API sources, such as websites, maps, and third-party services. It also offers customized suggestions for data cleaning, joining, and aggregation, adhering to leading data science practices. You can take advantage of its no-code features to quickly clean, merge, and format data for modeling, implementing smart algorithms to tackle data quality issues effectively. Users have reported remarkable enhancements in their key performance indicators, with some achieving increases of up to ten times. By integrating fresh data, you can significantly refine your decision-making processes and stimulate business growth. Additionally, Forloop is available as a desktop application for easy download and local testing, allowing users to gain practical experience with its robust features and functionalities. This accessibility ensures that anyone, regardless of technical expertise, can harness the power of data automation for their organizational needs.

HyperCrawl represents a groundbreaking web crawler specifically designed for applications involving LLM and RAG, aimed at developing highly efficient retrieval engines. The main objective was to optimize the retrieval process by reducing the time required to crawl diverse domains. We introduced a variety of advanced methodologies to create a novel machine learning-oriented strategy for web crawling. Instead of sequentially loading web pages—comparable to waiting in line at a supermarket—the crawler requests multiple pages at once, similar to making several online purchases simultaneously. This approach effectively eliminates downtime, allowing the crawler to tackle other tasks concurrently. By maximizing concurrent operations, the crawler adeptly handles a multitude of tasks simultaneously, greatly speeding up the retrieval process in contrast to managing only a few tasks at a time. Additionally, HyperCrawl enhances connection efficiency and resource management by reusing existing connections, akin to choosing a reusable shopping bag instead of acquiring a new one with every transaction. This cutting-edge method not only refines the crawling procedure but also significantly boosts overall system performance, leading to faster and more reliable data retrieval. Furthermore, as technology continues to advance, HyperCrawl is poised to adapt and evolve, ensuring it remains at the forefront of web crawling innovation.

Easily transform your remarks into working code, as illustrated by our quick creation of a Plotly bar chart. You can build entire functions without the hassle of hunting down specific methods or parameters from libraries; for example, we constructed a function that uploads a DataFrame to an AWS bucket using the parquet format. Moreover, you can effortlessly write SQL queries by simply telling CodeSquire what data to extract, join, and arrange, much like when we pinpointed the top 10 most common names. CodeSquire is also adept at clarifying someone else's code; just ask for an explanation of the previous function, and you'll receive a concise, understandable summary. In addition, it can aid in developing complex functions that involve multiple logical steps, enabling you to brainstorm by beginning with fundamental ideas and gradually adding in more sophisticated features as your project evolves. This cooperative method not only simplifies coding but also enhances its intuitiveness, making it accessible to both novice and experienced programmers alike. As a result, you can focus more on your creative vision rather than getting bogged down by technical details.