# PayPal Technology Join our technology team at careers.pypl.com - Medium This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## PayPal Releases Agentic Toolkit to Accelerate Commerce DevFeed: [PayPal Releases Agentic Toolkit to Accelerate Commerce]() Original publisher: [Read original article]() Author: PayPal Tech Blog Team Published: 2025-04-14T20:27:50Z Content type: release Language: en Sources: [PayPal Technology]() Topics: [releases](), [SDKs](), [API](), [AI Agent](), [MCP](), [TypeScript](), [vercel ai sdk]() Tags: [agent](), [agentic-ai](), [ai](), [ai-agents](), [api](), [artificial-intelligence](), [model-context-protocol](), [paypal](), [releases](), [sdk](), [tasks](), [typescript](), [workflows]() ### AI overview PayPal introduces the PayPal Agentic Toolkit, a library that helps developers integrate PayPal commerce APIs into AI agent workflows. The toolkit provides pre-built tools for orders, invoices, disputes, shipment tracking, transaction search, and subscriptions, with support for TypeScript and compatibility with MCP servers and Vercel's AI SDK. Python support is described as coming soon. ### Source excerpt The following is a repost from the PayPal Developer Blog. Building on the release of PayPal's MCP servers, PayPal is excited to introduce the PayPal Agentic Toolkit*. This toolkit empowers developers to seamlessly integrate PayPal's comprehensive suite of APIs -- including those for managing orders, invoices, disputes, shipment tracking, transaction search and subscriptions -- into various AI frameworks. With PayPal Agentic Toolkit, developers can now build sophisticated agentic workflows that handle financial operations with intelligence and efficiency. Overview of PayPal Agent Toolkit The PayPal Agentic Toolkit is a library designed to simplify the integration of PayPal's core commerce functionalities into AI agent workflows. By providing a structured and intuitive interface, the toolkit bridges the gap between PayPal's powerful APIs and modern AI agent frameworks, allowing agents to perform tasks such as creating and managing orders, generating and sending invoices, and handling subscription lifecycles. It eliminates the need for developers to manually integrate with API calls and data formatting by offering pre-built tools and abstractions to streamline these interactions. Key Features of the Toolkit Easy integration with PayPal services, including functions that correspond to common actions within the orders, invoices, disputes, shipment tracking, transaction search and subscriptions APIs, eliminating the need to delve deep into the specifics of each API endpoint. Compatibility with popular frameworks and languages, including compatibility with leading AI agent frameworks, such as Model Context Protocol (MCP) servers and Vercel's AI SDK, ensuring broad applicability. The toolkit currently supports the Typescript programming language with Python support coming soon. Extensibility, allowing developers to build upon core PayPal functionalities, and enable use with 3rd party toolkits that allows for complex, multi-step operations tailored to their specific agentic us ## PayPal Begins Rollout of MCP Servers to Accelerate Agentic Commerce DevFeed: [PayPal Begins Rollout of MCP Servers to Accelerate Agentic Commerce]() Original publisher: [Read original article]() Author: PayPal Tech Blog Team Published: 2025-04-04T14:25:27Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [Model Context Protocol (MCP)](), [MCP Server](), [Artificial Intelligence](), [anthropic](), [Authentication](), [Large Language Model]() Tags: [ai](), [anthropic](), [artificial-intelligence](), [authentication](), [developer](), [invoicing](), [llms](), [mcp](), [mcp-server]() ### AI overview PayPal is beginning to roll out official Model Context Protocol (MCP) servers so developers can connect PayPal services to AI tools and MCP clients. The initial feature lets eligible merchants generate invoices and shareable invoice links conversationally, with remote MCP support and cloud authentication. ### Source excerpt The following is a repost from the PayPal Developer Blog by Prakhar Mehrotra, SVP of Artificial Intelligence, PayPal At PayPal, we strive to make it easier for developers to access our services. Today, we are taking the first step to allow developers to embrace the new paradigm of agentic commerce by adopting the Model Context Protocol (MCP) and placing our services on an MCP server. This puts the power of generative AI at our merchants' fingertips. MCP is a standard put forward by Anthropic that is being adopted by the world's leading AI companies to help standardize the way agents access data sources or third-party services, thereby enabling seamless integration-something that is paramount in AI-native, multi-agent systems. Starting today, developers can interact with PayPal's official MCP server to begin enabling next-generation, AI-driven capabilities for merchants. This includes remote MCP servers available with auth integration on the cloud. With remote MCP support, users can seamlessly continue their tasks across devices with simple logins after the authentication process. The availability of PayPal's MCP server will enable a range of conversational AI capabilities for merchants, which we will roll out in the coming months. To illustrate the power of this new technology, we're starting with PayPal Invoice feature, which is available today to eligible PayPal merchants. Our First MCP Feature: Invoicing Developers can now enable merchants who wish to utilize their preferred AI tools -- including LLMs -- to automatically generate invoices and shareable invoice links to send to their clients within their MCP host. This eliminates the need for merchants to visit the PayPal website or integrate using PayPal APIs for manual invoice creation, making the process of creating an invoice for a customer much faster, more intuitive, and easier to integrate into existing MCP clients. Let's say a PayPal merchant needs to create an invoice for a customer. Instead of creating one ## Estimating Incremental Lift in Customer Value (Delta CV) using Synthetic Control DevFeed: [Estimating Incremental Lift in Customer Value (Delta CV) using Synthetic Control]() Original publisher: [Read original article]() Author: Mahshid Moha Published: 2025-02-14T20:41:49Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [Data Science](), [Transactions](), [digital wallet]() Tags: [adoption](), [causal-inference](), [customer-value](), [customers](), [data-science](), [digital-wallet](), [margin](), [monetization](), [paypal](), [products](), [revenue](), [synthetic-control](), [transactions]() ### AI overview This article explains PayPal's Delta CV metric, which estimates the incremental profit margin generated during the first year after a customer adopts a product or completes an action. It distinguishes Delta CV from customer lifetime value and describes direct and halo effects on engagement, revenue, and margin. ### Source excerpt How we measure the impact of user actions and product adoptions at PayPal In today's competitive digital landscape, understanding user interactions with your products is essential for driving revenue and building lasting customer relationships. At PayPal, our Data Science teams use causal inference to evaluate the impact of key customer actions, such as adopting a new product or adding a credit card to their wallet, on engagement (measured by Transactions per Account, or TPA), revenue, and margin to help make data-driven strategic decisions. The direct profit from a product adoption or a user action on the app could be $0 if viewed in isolation. However, that does not necessarily mean that these events are not driving engagement and monetization across the PayPal ecosystem; they can change the user engagement with other PayPal products in such a way that the user starts generating more profit. To measure the overall impact of user actions or product adoption, we introduced Delta CV (delta in Customer Value), and we defined it as a customer's incremental profit margin in the first year after adoption of a new product or completing an action. For example, if the average Delta CV for adoption of Crypto is $20, we expect customers who adopt Crypto for the first time to bring an additional $20 in margin on average in the next 12 months after the adoption. We define Delta Revenue (or TPA) in the same manner except we calculate the incremental lift in revenue (or TPA) instead of profit margin. The concept of Delta CV is very different from customer life-time value (CLV) which estimates the total profit generated by a customer over the course of their relationship with PayPal. Delta CV gives us a wholistic view on how new adoptions affect the engagement and value of an existing PayPal user. Adoption of a new product or completing an action can increase the customer's value in a few ways: The product itself may be a profit generating (Direct effect). For example, paying with ## Scaling PayPal's AI Capabilities with PayPal Cosmos.AI Platform DevFeed: [Scaling PayPal's AI Capabilities with PayPal Cosmos.AI Platform]() Original publisher: [Read original article]() Author: Jun Yang Published: 2024-05-02T16:27:03Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [AI Platform](), [Development](), [MLOps](), [Deployment](), [Machine learning]() Tags: [ai-ml](), [ai-platform](), [artificial-intelligence](), [deployment](), [development](), [enterprise-technology](), [generative-ai-tools](), [mlops](), [paypal]() ### AI overview This article describes PayPal Cosmos.AI Platform, an internal enterprise AI/ML platform conceived around 2020 and generally available since mid-2022. It aims to support the end-to-end machine learning development lifecycle by scaling development, deployment, and operations with MLOps capabilities. The article states that the platform has been adopted across business domains, with thousands of users and hundreds of deployed models serving real-time and batch inference requests. ### Source excerpt By Jun Yang, Zhenyin Yang, and Srinivasan Manoharan, based on the AI/ML modernization journey taken by the PayPal Cosmos.AI Platform team in the past three years. Source: Dall-E 3 AI is a transformative technology that PayPal has been investing in as a company for over a decade. Across the enterprise, we leverage AI/ML responsibly to address a wide range of use cases -- from fraud detection; improving operational efficiencies; providing personalized service and offers to customers; meeting regulatory obligations, and many more things. To accelerate PayPal's innovation and deliver incredible value to our customers through technology, we are working to put the innovative power of artificial intelligence into the hands of more employees across different disciplines and accelerate time-to-market for building and deploying AI/ML powered applications, in service of our customers and in alignment with our Responsible AI principles. The blog below details a key internal platform that is helping us do this: PayPal Cosmos.AI Platform. AI/ML Platform: The Enterprise Approach Over the years, many engineering systems and tools have been developed to facilitate this practice at PayPal, addressing various needs and demands individually as they emerged. Several years ago, as we began to expand AI/ML across all core business domains, it became increasingly evident that the prevailing approach of having a collection of bespoke tools, often built and operated in their own silos, would not meet the needs for large scale AI/ML adoptions across the enterprise. Conceived around 2020, PayPal Cosmos.AI Platform aims at providing end-to-end Machine Learning Development Lifecycle (MLDLC) needs across the enterprise by modernizing and scaling AI/ML development, deployment and operation, with streamlined end-to-end Machine Learning operations (MLOps) capabilities and experiences, making it faster, easier, and more cost effective to enable businesses through AI/ML solutions. Since its official GA ## Leveraging Spark 3 and NVIDIA's GPUs to Reduce Cloud Cost by up to 70% for Big Data Pipelines DevFeed: [Leveraging Spark 3 and NVIDIA's GPUs to Reduce Cloud Cost by up to 70% for Big Data Pipelines]() Original publisher: [Read original article]() Author: Ilay Chen Published: 2024-02-21T16:42:14Z Content type: tutorial Language: en Sources: [PayPal Technology]() Topics: [Apache Spark](), [GPU](), [RAPIDS](), [Cloud](), [Data pipelines](), [migration](), [upgrade](), [Open Source]() Tags: [apache-spark](), [big-data](), [cloud](), [cloud-computing](), [data-pipelines](), [data-processing](), [gpu](), [machine-learning](), [migration](), [rapids](), [upgrade]() ### AI overview A PayPal engineering blog explains how upgrading from Apache Spark 2 to Spark 3 and migrating workloads to GPU clusters with NVIDIA Spark RAPIDS can accelerate selected big-data processing tasks and potentially reduce cloud costs by up to 70%. It covers the migration, parameter tuning, challenges, and reported benefits. ### Source excerpt By Ilay Chen and Tomer Akirav At PayPal, hundreds of thousands of Apache Spark jobs run on an hourly basis, processing petabytes of data and requiring a high volume of resources. To handle the growth of machine learning solutions, PayPal requires scalable environments, cost awareness and constant innovation. This blog explains how Apache Spark 3 and GPUs can help enterprises potentially reduce Apache Spark's jobs cloud costs by up to 70% for big data processing and AI applications. Our journey will begin with a brief introduction of Spark RAPIDS -- Apache Spark's accelerator that leverages GPUs to accelerate processing via the RAPIDS libraries. We will then review PayPal's CPU-based Spark 2 application, our upgrade to Spark 3 and its new capabilities, explore the migration of our Apache Spark application to a GPU cluster, and how we tuned Spark RAPIDS parameters. We will then discuss some challenges we encountered and the benefits of the updates. Libra scales in the cloud, generated by AIBackground GPUs are everywhere, and their parallelism characteristics are perfect for processing AI and graphics applications, among other things. For those unfamiliar: what makes GPUs different from CPUs, computation-wise, is that CPUs have a limited amount of very strong cores, whereas GPUs have thousands, or even tens of thousands or more, relatively weak cores that work together very well. PayPal has been leveraging GPUs to train models for some time now, and so we decided to evaluate if the parallelism of the GPU can be helpful with processing big data applications based on Apache Spark. In our research, we encountered NVIDIA's Spark RAPIDS open-source project. It has many purposes, however we focused on Spark RAPIDS's cost reduction potential, because enterprises like PayPal spend lots of money on running Spark jobs in the cloud. Using Spark with GPUs isn't common in the industry yet, but according to our findings as described in this blog, the potential benefits could be enorm ## Declarative Feature Engineering at PayPal DevFeed: [Declarative Feature Engineering at PayPal]() Original publisher: [Read original article]() Author: Marina Lyan Published: 2023-12-11T15:56:51Z Content type: tutorial Language: en Sources: [PayPal Technology]() Topics: [Feature Engineering](), [Machine learning](), [data](), [Requirements]() Tags: [declarative-programming](), [engineering](), [feature](), [feature-engineering](), [fraud](), [machine-learning](), [paypal](), [production](), [real-time](), [requirements](), [scale]() ### AI overview PayPal describes its declarative, or config-based, feature engineering approach for machine learning. The approach lets data scientists declare feature definitions while execution details are handled separately, helping engineers address scale, predictable time to market, and total cost of ownership. The article introduces feature-cost metrics and strategies for reusing existing features. ### Source excerpt Photo by fabio on Unsplash PayPal supports over 400 million active consumers and merchants worldwide. Every minute there are several thousand payment transactions. To prevent fraud in real-time at such a scale, we need to streamline our ML workflow and feature engineering processes to build strong predictors of behaviors and risk indicators. On top of that, it must be done with consistently predictable Time to Market (TTM) and sustainable Total Cost of Ownership (TCO). What is declarative feature engineering? While the declarative feature engineering term was first introduced by Zipline AirBnB in 2019, we have successfully used this paradigm in PayPal for the last decade, though we know it as config-based feature engineering. The idea is to allow data scientists to write a declaration of what their features look like rather than explicitly specify how to construct them on top of different execution platforms. In this way, the concerns of feature construction and execution are abstracted away from scientists so that engineers can worry about those complexities. This post is the first in a series of two posts that will outline how the declarative feature engineering approach helps our engineers to address scale, TTM, and TCO requirements. Let's start with the definitions of metrics that measure our success. TTM of the ML Feature TTM of the Machine Learning (ML) feature is the length of time from the feature concept until it is released to production. Predictable TTM is a paramount need for overall AI maturation and business-first AI strategies. Cost of the ML Feature To qualify and estimate TCO, we break down the cost of the feature as follows: Layers of feature cost The main insight from the feature cost analysis is that we have to reuse existing features across the teams whenever possible. Otherwise, we just pay twice for every aspect. To approach the above challenges, we separate features into three levels of complexity so we can define tailored strategies for scal ## Streamlining Developer Productivity with the PayPal Visual Studio Code Extension DevFeed: [Streamlining Developer Productivity with the PayPal Visual Studio Code Extension]() Original publisher: [Read original article]() Author: Asish Mohanty Published: 2023-12-07T20:51:45Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [VS Code Extension](), [Visual Studio Code](), [Developer experience](), [Development](), [API](), [configuration]() Tags: [api](), [configuration](), [developer](), [extension](), [logs](), [productivity](), [visual-studio-code]() ### AI overview The article describes a PayPal Visual Studio Code extension that helps developers integrate PayPal checkout solutions within VS Code. It highlights sample applications, preconfigured API code snippets, quick links to PayPal developer resources, and command-palette access to extension features. ### Source excerpt In the ever-evolving landscape of software development, productivity and efficiency have become paramount to success. Developers are constantly juggling multiple tasks, from navigating complex codebases to integrating third-party services, all while maintaining the highest quality standards. To address these challenges, it is crucial to equip developers with the right tools and capabilities to optimize their workflows and enhance their overall experience. How the PayPal Visual Studio Code Extension Simplifies Developer Experience The PayPal Visual Studio (VS) Code extension offers a streamlined way for developers to integrate PayPal checkout solutions into their projects. This tool enhances productivity by allowing the management of PayPal features directly within the VS Code environment, making the integration process more efficient and seamless. The extension includes pre-written code samples, code snippets, and quick links, significantly reducing the effort required to integrate PayPal payment solutions. These features enable developers to concentrate on their primary tasks, improving productivity, expediting development cycles, and shortening the time-to-market for their PayPal-enabled applications. The PayPal VS Code extension is designed to empower developers with a collection of features to enhance efficiency. Its key features are listed below. List of Features PayPal Samples: Developers can access and clone sample application code for PayPal's supported integrations within the VS Code environment. 2. Code Snippets: Developers can access a library of pre-configured code snippets for API resources to speed up the integration process. These snippets can be easily inserted into their code, reducing manual effort and ensuring correct implementation. 3. PayPal Quick Links: Access developer dashboard links directly from the extension to seamlessly manage apps configuration, Sandbox data, API call logs and Webhooks. 4. Command Palette: The extension supports various ## Managing Recurring Payments with Apple Pay Using PayPal DevFeed: [Managing Recurring Payments with Apple Pay Using PayPal]() Original publisher: [Read original article]() Author: PayPal Tech Blog Team Published: 2023-11-20T14:41:42Z Content type: article Language: en Sources: [PayPal Technology]() Topics: [SDKs](), [Mobile](), [Transactions](), [Authorization]() Tags: [apple](), [apple-pay](), [authorization](), [js-sdk](), [mobile-web](), [payments](), [subscriptions](), [token](), [transactions]() ### AI overview This article explains how merchants can support recurring payments with Apple Pay through PayPal's JS SDK and V3 Vault experience on mobile websites. It covers fixed or variable billing, consent details, authentication, transaction outcomes, and persistent tokens for future purchases. ### Source excerpt Recurring payments have become an integral part of the modern digital economy, offering convenience and predictability for both consumers and businesses. Our previous post highlighted different methods of integrating Apple Pay® with PayPal. This blog explores recurring payments using PayPal and Apple Pay, showing how using it can streamline transactions and benefits end users. How Recurring Payments with PayPal and Apple Pay Work A recurring payment is an agreement between a buyer and a merchant. It allows the merchant to charge the buyer's chosen payment instrument (typically a credit card) for a pre-approved amount at pre-approved intervals, which can be either fixed or variable. Consumers visiting a merchant's mobile website integrated with PayPal JS SDK and V3 Vault experience will find Apple Pay as one of the available payment methods for recurring payments. Once the consumer taps the Apple Pay button on the mobile website, the Apple Pay payment sheet is presented. This process mirrors the one-time Apple Pay experience for mobile web transactions. The merchant's website plays a crucial role in detailing the billing frequency and other terms of service. These details can also be reiterated on the payment sheet for added clarity. Recurring fees can take two forms: fixed amounts, such as monthly movie ticket subscriptions, or variable amounts like weekly grocery orders. The variability is subject to local authorizations. Depending on the specific merchant use case, customers may be presented with line items that reiterate billing frequency, discounts, and any additional upfront fees on the consent screen. To ensure transparency, the final payment amount is prominently outlined on the total line, making customers fully aware of the charge they are authorizing. The consumer can review the transaction details on the Apple Pay payment sheet. Once the consumer is ready, they authenticate using Face ID® or Touch ID®. When the transaction is complete, the consumer is sho ## Accept E-Commerce Payments Easily with PayPal's Buttons Component DevFeed: [Accept E-Commerce Payments Easily with PayPal's Buttons Component]() Original publisher: [Read original article]() Author: Arit Developer Published: 2023-10-24T16:07:59Z Content type: tutorial Language: en Sources: [PayPal Technology]() Topics: [API](), [servers](), [Front end](), [HTML](), [npm](), [App]() Tags: [api](), [back-end](), [code](), [credentials](), [demo](), [e-commerce](), [ecommerce](), [environment](), [front-end](), [github](), [html](), [javascript](), [npm](), [package-json](), [payments](), [payments-technology](), [paypal](), [programming]() ### AI overview A tutorial for integrating PayPal Standard Checkout and its Payment Buttons component into a simple HTML and NodeJS shopping application. It covers prerequisites, sandbox credentials, backend and frontend setup, checkout flow, and custom integration guidance. ### Source excerpt Accepting online payments is now a universal must-have, catering to everyone from solo entrepreneurs to massive global corporations. PayPal's Standard Checkout allows for seamless integration of PayPal's Payment Buttons component into your e-commerce app, granting you the power to accept online payments. In this guide, I am going to show how to add Standard Checkout to a simple shopping app, built with HTML and NodeJS. Guide Overview Part 1. Prerequisites Part 2a. Basic Integration -- Code Setup Part 2b. Basic Integration -- Checkout Flow Part 3a. Custom Integration -- Code Setup Part 3b. Custom Integration -- Checkout Flow Part 4. Conclusion Part 5. Additional PayPal Payment Integrations Part 1. Prerequisites To complete this integration, you will need the following information: API Credentials (Client ID and Secret) for a PayPal REST app: paste these from your Developer Dashboard. Since we are working in a development environment in this demo, ensure that the Sandbox Toggle is on. Obtain Test API Credentials The username and password for a Personal Sandbox account: you will need this to complete the checkout process. Navigate to Sandbox Accounts under Testing ToolsObtain Personal Sandbox Account login details You can grab the code for our completed Checkout Integration demo from the PayPal Developer GitHub repository. Part 2a. Basic Integration -- Code Setup Navigate to PayPal's Integrate Checkout documentation. The documentation shows you how to set your development environment up: installing npm, verifying your package.json file, and setting up your .env file. Next, you will set up your back-end server and front-end code. The documentation presents the contents of three files: server.js (for the back-end server), app.js (for the client-side logic) and checkout.html (for the user interface). Copy the Backend CodeCopy the Frontend Code Grab these code snippets and paste them into your IDE (Integrated Development Environment). I strongly recommend that you implement the