Latest news with #AgentsSDK
Geeky Gadgets
3 days ago
- Geeky Gadgets
How OpenAI's Agents SDK is Redefining Intelligent Systems Design
What if your software could think, collaborate, and adapt like a team of experts working in perfect harmony? OpenAI's Agents SDK introduces a new framework that transforms this vision into reality. By combining pre-built tools, customizable agents, and the innovative concept of agents-as-tools, this technology enables developers to build intelligent systems that tackle complex tasks with precision and efficiency. Whether it's automating workflows, managing dynamic interactions, or scaling solutions to meet evolving demands, the SDK offers a new paradigm for designing AI-driven systems. But with such fantastic potential comes the challenge of understanding how to harness it effectively. James Briggs takes you through the core features of OpenAI's Agents SDK, from the simplicity of pre-built tools to the flexibility of custom solutions and the collaborative power of agents-as-tools. You'll uncover how structured outputs and parameter validation ensure reliability and clarity, while modular agent collaboration opens doors to scalable, adaptable systems. Along the way, we'll discuss practical applications, best practices, and the trade-offs you need to consider. By the end, you'll not only understand what makes this SDK unique but also how it can redefine the way you approach problem-solving with AI. So, what does it take to unlock the full potential of this toolkit? Let's find out. Overview of OpenAI Agents SDK Pre-Built Tools: Simplifying Common Tasks The Agents SDK includes a selection of pre-built tools designed to handle routine tasks efficiently. These tools are optimized for seamless integration with OpenAI models, offering reliable, ready-to-use functionality for a variety of applications. While they are not compatible with external providers, their out-of-the-box capabilities make them ideal for rapid prototyping and straightforward implementations. Examples of pre-built tools include: A web search tool that retrieves real-time information from the internet. from the internet. A file search tool that locates and processes specific documents within a system. These tools are particularly useful when time constraints or resource limitations make building custom solutions impractical. However, for optimal performance, it is recommended to use at least mini models, as nano models may lack the computational power required for robust execution. Custom Tools: Tailored to Your Needs Custom tools offer the flexibility to define functionalities that align with your specific use case. By using the `function_tool` decorator, you can create tools for specialized tasks, such as generating detailed reports, automating workflows, or retrieving specific data points like the current time. These tools can include descriptive metadata and intuitive naming conventions, allowing language models to better understand their purpose and functionality. One of the standout features of custom tools is their ability to produce structured outputs. By using JSON schemas, you can define the format of a tool's responses, making sure consistency and clarity. Additionally, parameter validation allows you to specify required and optional inputs, reducing the likelihood of errors during execution. For example, a custom tool designed for scheduling meetings might require the following parameters: By validating these inputs, you can ensure the tool operates reliably, even in scenarios involving complex scheduling requirements. This level of customization makes custom tools invaluable for addressing unique challenges. OpenAI's Agents SDK : Features, Benefits and Applications Watch this video on YouTube. Here is a selection of other guides from our extensive library of content you may find of interest on AI Agents. Structured Outputs: Improving Interpretability Structured outputs are a key feature of the Agents SDK, allowing tools to deliver machine-readable responses that go beyond plain text. Instead of returning unstructured data, tools can provide JSON objects containing detailed elements such as: Confidence scores to quantify the reliability of a response. to quantify the reliability of a response. Reasoning behind decisions to enhance transparency. behind decisions to enhance transparency. Contextual information relevant to the task at hand. This structured approach not only improves interpretability for developers but also assists seamless integration with other systems. For instance, structured data can be directly consumed by downstream processes, allowing automation and reducing the need for manual intervention. This feature is particularly valuable in scenarios where precision and clarity are critical. Parameter Validation: Making sure Accurate Inputs Parameter validation is another essential capability of the SDK, allowing you to define expected inputs using JSON schemas. By enforcing data types, constraints, and default values, you can minimize runtime errors and ensure tools receive the correct data for processing. Consider a tool designed to calculate shipping costs. It might require the following parameters: Package weight Dimensions Destination By validating these inputs, you can prevent issues such as missing or incorrectly formatted data, making sure the tool delivers accurate and reliable results. This feature is particularly useful in applications where data integrity is paramount. Agents-as-Tools: Allowing Modular Collaboration The concept of agents-as-tools introduces a new level of modularity and collaboration in multi-agent systems. In this paradigm, one agent can use another as a tool, allowing specialized agents to work together on complex tasks. This approach allows for the creation of highly flexible and scalable systems. For example: A content generation agent might rely on a social media management agent to publish posts across multiple platforms. A customer service system could use one agent to handle inquiries and another to process refunds, streamlining operations. To coordinate these interactions, a top-level orchestrator agent manages the flow of information between sub-agents. While this setup can be token-intensive due to the detailed exchanges required, it offers significant advantages in terms of modularity and adaptability. This makes agents-as-tools particularly well-suited for dynamic environments where tasks and requirements frequently evolve. Best Practices for Effective Implementation To maximize the effectiveness of the Agents SDK, consider the following best practices: Use descriptive docstrings to guide language models on how to use each tool effectively. to guide language models on how to use each tool effectively. Provide detailed parameter descriptions , especially for tools with complex functionalities. , especially for tools with complex functionalities. Avoid unnecessary complexity in multi-agent setups unless the use case demands it, as simpler designs are often more efficient and easier to maintain. Adhering to these guidelines ensures that your systems remain efficient, maintainable, and scalable, even as they grow in complexity. Limitations and Considerations While the Agents SDK offers a robust set of features, it is not without limitations. The token-heavy nature of agents-as-tools can impact performance, particularly in resource-constrained environments. Additionally, the SDK's documentation often provides simplified examples, which may require further customization to address real-world challenges effectively. It is also important to note that the SDK is optimized for use with OpenAI models, limiting its compatibility with other providers. This may necessitate additional development efforts if your project requires integration with external systems. Unlocking the Potential of OpenAI's Agents SDK OpenAI's Agents SDK is a versatile framework that enables you to build intelligent systems capable of tackling a wide range of tasks. By using features such as pre-built tools, custom tools, structured outputs, and agents-as-tools, you can design solutions that are both flexible and scalable. While the SDK has its limitations, its capabilities open up new possibilities for innovation in multi-agent systems, allowing you to address complex challenges with confidence and precision. Media Credit: James Briggs Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Arabian Post
26-06-2025
- Business
- Arabian Post
Cloudflare and OpenAI Unite to Power Persistent AI Agents
Cloudflare and OpenAI have unveiled a powerful integration enabling developers to build intelligent, stateful AI agents that combine OpenAI's reasoning with Cloudflare's scalable execution infrastructure. By pairing the OpenAI Agents SDK with Cloudflare's new Agents SDK and foundational technologies like Durable Objects and Workers, the collaboration delivers global reach, persistent memory, and human‑in‑the‑loop interaction, all within a serverless framework. The synergy addresses a key shortcoming of stateless AI agents. OpenAI's Agents SDK offers advanced cognition—planning, tool‑calling, decision‑making—yet leaves execution environment and persistence to the developer. Cloudflare's solution fills that gap: its Agents SDK runs atop Workers and Durable Objects, providing each agent a unique identity, durable memory store, built‑in scheduling, WebSocket connectivity and global low‑latency execution. Durable Objects act as the agent container. Each instantiation—based on a name or unique ID—carries its own state and storage, enabling multi‑session workflows, memory hydration, and asynchronous execution. Developers can create one agent per user, task, or domain, avoiding state entanglement while fostering modular, composable agent systems. For instance, one could build a triage agent that routes queries to specialist agents, each maintaining separate memory and logic. ADVERTISEMENT A standout feature is scalability through human‑in‑the‑loop control. Cloudflare's architecture enables agents to pause mid‑workflow, await human judgment, and resume—persisting intermediate steps and context across sessions. Knock, a third‑party messaging layer, exemplifies this. Developers have built virtual card‑issuing workflows where the AI agent pauses for approval before issuing a card—managed via Knock plus Cloudflare's SDK. Another innovation: agents are addressable beyond HTTP. Cloudflare's system supports Twilio‑backed phone‑call integrations, WebSocket real‑time sessions, email and pub/sub. This opens rich, multimodal use cases—voice, text, email—bound by a globally unique agent identity. Complementing these developments, a remote Model Context Protocol server has been introduced. Cloudflare now allows agents to host MCP servers directly, enabling structured tool integration and external service access via authenticated, remote endpoints using MCPAgent. The MCP feature dovetails neatly with Cloudflare's recent release of a free tier for Durable Objects and general availability of multi‑step Workflows, lowering the entry barrier for developers. Addition of the OpenAI Agents SDK and Responses API further enriches the landscape. OpenAI's Responses API supports dynamic web search, file system access and system‑level tasks; the Agents SDK coordinates multi‑agent orchestration. Paired with Cloudflare's persistent runtime, this empowers developers to build AI agents capable of real‑time research, memory‑backed workflows and inter‑agent communication. Underpinning this integration is Cloudflare's acquisition of Outerbase in April, a database platform company. The acquisition strengthens data infrastructure within Workers, Durable Objects and the Agents SDK—helping developers build rich, contextual, database‑backed AI systems. This move boosts long‑term memory storage and retrieval critical for agents maintaining evolving user context. Industry observers are taking notice. A Medium commentary described the duo as 'perfect complements: OpenAI's Agents SDK gives you the brain, the other gives you the body'. Cloudflare's CEO Matthew Prince emphasised that these developments remove 'cost and complexity barriers' to agent deployment, calling the MCP server release 'the industry's first remote MCP server'. Developers working with the Agents SDK can bootstrap agent projects via common workflows: installing via npm or using the agents‑starter template, extending the core Agent class to handle HTTP, WebSocket, scheduled tasks, SQL storage, and tool invocation. Integration with front‑end frameworks is supported through useAgent and useAgentChat React hooks, offering real‑time UI connections. Looking ahead, Cloudflare promises further enhancements: evaluation tooling, voice and video interactivity via WebRTC, richer email integration for human supervision, self‑hosting capabilities, structured output support, and deeper embedding with Worker AI, Vectorize, Log Explorer and AI Gateway. The evolving field of AI agents is entering a new phase—no longer demonstrations, but operational systems able to remember, adapt, collaborate, and operate at global scale. By combining cognitive reasoning with robust orchestration and persistence, developers are empowered to deploy production‑ready agents that are stateful, interactive and distributed. That shift stands to redefine automation, customer support, education, workflows and more—lowering development barriers, increasing resilience, and enabling agents that truly work on behalf of users across time, platforms and modalities.
Geeky Gadgets
09-05-2025
- Geeky Gadgets
How OpenAI's Agents SDK is Changing Task Management : Multi-Agent Systems Guide
What if you could design a system where multiple specialized agents work together seamlessly, each tackling a specific task with precision and efficiency? This isn't just a futuristic vision—it's the core promise of multi-agent systems powered by OpenAI's Agents SDK. Imagine an orchestrator delegating tasks like web searches, document retrieval, or even secure code execution to a network of sub-agents, each optimized for its role. This modular approach doesn't just streamline workflows; it transforms how we think about automation, allowing scalable, adaptable systems that can evolve alongside your needs. Whether you're a developer exploring innovative AI tools or a team leader seeking smarter task management, the possibilities are both exciting and practical. In this comprehensive tutorial, James Briggs explains how to set up and optimize a multi-agent system using OpenAI's Agents SDK. From understanding the orchestrator-sub-agent architecture to crafting precise prompts and integrating specialized tools, this guide walks you through every step. You'll learn how to design workflows that balance complexity with performance, debug systems effectively, and harness the SDK's advanced features to build reliable, scalable solutions. By the end, you'll not only grasp the technical mechanics but also gain insights into how these systems can transform your approach to automation. So, how do you create a system where collaboration between agents feels almost effortless? Let's explore. Building Multi-Agent Workflows Understanding Multi-Agent Systems in OpenAI's Agents SDK OpenAI's Agents SDK is a versatile tool for creating multi-agent systems, building on earlier frameworks like the Swarm package. At its core, the SDK enables you to design systems where an orchestrator coordinates multiple sub-agents, each specializing in a specific task. This orchestrator-sub-agent architecture ensures efficient task management and is particularly suited for workflows requiring diverse functionalities. The orchestrator acts as the central controller, delegating tasks to sub-agents based on their specific capabilities. This modular approach not only enhances scalability but also allows for seamless integration of new functionalities as your workflow evolves. By using this architecture, you can create systems that are both flexible and efficient. The Role and Functionality of Sub-Agents Sub-agents are the building blocks of multi-agent systems, each designed to handle a specific task. Their modularity ensures that the system remains efficient and adaptable to changing requirements. Below are three common types of sub-agents and their roles: Web Search Sub-Agent: This sub-agent integrates with web search APIs, such as LinkUp, to retrieve and summarize information. By using asynchronous programming, it can handle multiple API calls simultaneously, reducing latency and improving response times. This sub-agent integrates with web search APIs, such as LinkUp, to retrieve and summarize information. By using asynchronous programming, it can handle multiple API calls simultaneously, reducing latency and improving response times. Internal Docs Sub-Agent: Acting as a retrieval-augmented generation (RAG) tool, this sub-agent processes internal documents to answer queries. It ensures secure and efficient access to private data, making it ideal for sensitive information retrieval. Acting as a retrieval-augmented generation (RAG) tool, this sub-agent processes internal documents to answer queries. It ensures secure and efficient access to private data, making it ideal for sensitive information retrieval. Code Execution Sub-Agent: Designed for tasks requiring mathematical or logical operations, this sub-agent uses secure code execution tools. It emphasizes accuracy and security, particularly for operations involving sensitive data. Each sub-agent operates independently but communicates with the orchestrator to ensure smooth task execution. This separation of responsibilities allows for better error handling and easier debugging, as issues can be isolated to specific sub-agents. Multi-Agent Systems in OpenAI's Agents SDK Watch this video on YouTube. Uncover more insights about multi-agent systems in previous articles we have written. Setting Up and Optimizing the Orchestrator The orchestrator is the central component of a multi-agent system, responsible for managing communication between the user and sub-agents. Its primary role is to route queries to the appropriate sub-agent, making sure tasks are executed efficiently. To set up an effective orchestrator: Convert sub-agents into callable tools: Ensure that each sub-agent is accessible to the orchestrator as a distinct tool, simplifying task delegation. Ensure that each sub-agent is accessible to the orchestrator as a distinct tool, simplifying task delegation. Craft precise prompts: Develop clear and specific prompts to guide the orchestrator's behavior. This ensures it understands user intent and delegates tasks effectively. Develop clear and specific prompts to guide the orchestrator's behavior. This ensures it understands user intent and delegates tasks effectively. Integrate sub-agents into a unified workflow: Establish seamless communication between the orchestrator and sub-agents to enable efficient collaboration. Optimization is key to making sure the orchestrator performs reliably. OpenAI provides tracing tools to monitor workflows, identify bottlenecks, and resolve issues. By refining prompts and optimizing sub-agent behaviors, you can enhance the overall performance of your system. Debugging and Performance Enhancement Building a reliable multi-agent system requires continuous debugging and performance optimization. OpenAI's tracing tools are invaluable for monitoring workflows and identifying areas for improvement. Here are some strategies to enhance system performance: Refine orchestrator prompts: Clear and concise prompts improve the orchestrator's ability to understand and delegate tasks. Clear and concise prompts improve the orchestrator's ability to understand and delegate tasks. Optimize sub-agent operations: For instance, reduce latency in asynchronous calls for the web search sub-agent to improve response times. For instance, reduce latency in asynchronous calls for the web search sub-agent to improve response times. Test workflows regularly: Simulate various scenarios to identify potential issues and refine system behavior. By adopting these strategies, you can ensure your multi-agent system operates efficiently and delivers accurate results. Balancing Complexity and Performance Designing multi-agent systems involves balancing functionality with performance. While the orchestrator-sub-agent pattern is ideal for managing complex workflows, it can introduce latency due to the coordination of multiple sub-agents. For simpler tasks, a single-agent approach may be more efficient. Understanding these trade-offs is crucial for selecting the right architecture for your specific use case. Practical demonstrations can help validate your system's design. For example, simulate a multi-step workflow where the orchestrator delegates a web search task to one sub-agent and a document retrieval task to another. Analyze the system's responses to identify areas for improvement and ensure accurate, efficient outputs. Key Insights for Effective Multi-Agent Systems The orchestrator-sub-agent pattern is highly effective for managing workflows involving multiple specialized tasks. Clear prompting and seamless tool integration are essential for optimal system performance. Regular debugging and performance optimization are critical for building reliable, efficient systems. By following these best practices, you can use OpenAI's Agents SDK to create flexible, scalable workflows that handle diverse tasks with precision and efficiency. Whether managing web searches, processing internal documents, or executing secure code, these strategies will help you design systems that meet your needs effectively. Media Credit: James Briggs Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Geeky Gadgets
06-05-2025
- Geeky Gadgets
OpenAI's Agents SDK Lets You Easily Build AI Voice Assistants
What if your technology could truly listen to you—not just hear your words, but understand them, respond intelligently, and even speak back with human-like clarity? The rise of AI voice assistants has brought us closer than ever to this vision, and OpenAI's Agents SDK is leading the charge. Imagine a hands-free assistant that schedules your day, answers complex queries, or even helps you learn a new language—all through natural, conversational exchanges. But building such systems has often been a daunting task, requiring intricate setups and specialized expertise. That's where the Agents SDK steps in, offering developers a streamlined framework to create voice-enabled AI solutions that feel intuitive and responsive. In this perspective, James Briggs explores how OpenAI's Agents SDK is transforming the way developers approach voice-first AI systems. From configuring Python environments to designing real-time conversational pipelines, this guide breaks down the essential steps to bring your voice assistant ideas to life. You'll discover how to handle audio seamlessly, fine-tune language models for context-aware responses, and customize speech-to-text and text-to-speech features for a truly human-like interaction. Whether you're building tools for accessibility, education, or productivity, the possibilities are as exciting as they are practical. So, how will you harness the power of voice to redefine what's possible? Building Voice AI Interfaces What is OpenAI's Agents SDK? OpenAI's Agents SDK is a powerful tool designed to simplify the development of AI agents capable of understanding and responding to natural language. By incorporating voice interfaces, these agents become more intuitive and accessible, bridging the gap between advanced language models and voice input/output. The SDK creates a conversational loop that feels natural and human-like, making it ideal for applications in education, accessibility, productivity, and beyond. Whether you're developing tools for interactive learning or hands-free assistance, the SDK provides the foundation for building voice-first AI solutions. Preparing Your Python Environment for Voice AI Development Before building a voice-enabled AI agent, setting up your Python environment is a critical first step. Proper preparation ensures a smooth development process and minimizes potential issues. Follow these steps to get started: Install the required libraries using pip , including the Agents SDK and audio processing packages. , including the Agents SDK and audio processing packages. Ensure dependencies like sounddevice (for audio input/output) and numpy (for data manipulation) are installed and up to date. (for audio input/output) and (for data manipulation) are installed and up to date. Test your environment to verify that all components are configured correctly and compatible with your system. A well-prepared environment lays the groundwork for efficient development and ensures your voice interface operates reliably. Using OpenAI's SDK to Create AI Voice Assistants Watch this video on YouTube. Find more information on AI voice assistants by browsing our extensive range of articles, guides and tutorials. Audio Handling in Python: The Foundation of Voice Interfaces Audio handling is a cornerstone of any voice interface, as it enables the system to capture and deliver clear, high-quality sound. Python's sounddevice library simplifies this process, offering tools to manage audio input and output effectively. Key considerations include: Device configuration: Properly set up input and output devices to ensure accurate audio capture and playback. Properly set up input and output devices to ensure accurate audio capture and playback. Sample rate settings: Choose appropriate sample rates to maintain high-quality audio data without unnecessary processing overhead. Choose appropriate sample rates to maintain high-quality audio data without unnecessary processing overhead. Data conversion: Convert audio data into arrays for seamless integration with speech-to-text and text-to-speech systems. By mastering these elements, you can create a robust audio foundation that supports the entire voice pipeline. Designing and Customizing the Voice Pipeline The voice pipeline is the backbone of any voice-enabled AI system, consisting of three interconnected components that work together to process and respond to user input. These components include: Speech-to-text conversion: Transforms spoken language into text for processing by the AI agent. Transforms spoken language into text for processing by the AI agent. Language model (LM) processing: Interprets the text input and generates contextually relevant responses. Interprets the text input and generates contextually relevant responses. Text-to-speech generation: Converts the AI agent's response into spoken language for output. Customizing the pipeline allows you to tailor the system to specific use cases. For example, adjusting text-to-speech settings such as tone, tempo, and emotional inflection can enhance user engagement and make interactions feel more natural. A well-designed pipeline ensures smooth communication between the user and the AI agent. Integrating Voice Functionality with OpenAI's Agents SDK Integrating voice capabilities into the Agents SDK involves configuring your AI agents to handle voice input and output seamlessly. The SDK provides tools to streamline this process, including: Real-time audio handling: Manage streamed audio events for immediate processing and response. Manage streamed audio events for immediate processing and response. Customizable audio parameters: Adjust sample rates, buffer sizes, and other settings to optimize performance. Adjust sample rates, buffer sizes, and other settings to optimize performance. Voice-specific configurations: Enable features like voice activity detection to improve responsiveness and accuracy. These features allow you to create a conversational loop where the AI agent processes user input and delivers coherent, voice-based responses in real time. By using the SDK's capabilities, you can build systems that feel intuitive and responsive. Developing Real-Time Conversational AI Creating a real-time conversational AI involves designing a continuous loop that captures audio input, processes it through the voice pipeline, and generates spoken responses. To achieve this, consider the following: Speech-to-text accuracy: Ensure the system reliably captures user input, even in noisy environments. Ensure the system reliably captures user input, even in noisy environments. Language model fine-tuning: Optimize the model to provide context-aware and relevant responses tailored to your application. Optimize the model to provide context-aware and relevant responses tailored to your application. Natural text-to-speech output: Focus on timing, clarity, and tone to maintain a conversational flow that feels human-like. Iterative testing and refinement are essential to enhance the system's performance and ensure a seamless user experience. By addressing these factors, you can build a conversational AI that meets the demands of real-world applications. Applications and Opportunities for Voice Interfaces Voice interfaces offer fantastic potential across various industries, providing unique advantages over traditional text-based systems. Some notable applications include: Language learning: Develop interactive tools that help users practice pronunciation, improve fluency, and engage in conversational exercises. Develop interactive tools that help users practice pronunciation, improve fluency, and engage in conversational exercises. Accessibility: Create hands-free solutions for individuals with mobility or vision impairments, allowing greater independence and convenience. Create hands-free solutions for individuals with mobility or vision impairments, allowing greater independence and convenience. Productivity tools: Design voice-driven systems for scheduling, task management, and information retrieval, streamlining workflows and saving time. As voice-based AI continues to evolve, exploring its applications positions you to create innovative, user-friendly solutions that address real-world challenges. By using OpenAI's Agents SDK, you can unlock new possibilities and drive the development of next-generation voice interfaces. Media Credit: James Briggs Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Tahawul Tech
09-04-2025
- Business
- Tahawul Tech
Cloudflare acquires Outerbase to expand capabilities for database and agent developer experience
Cloudflare, Inc., the leading connectivity cloud company, recently announced the acquisition of Outerbase, a developer database company, to dramatically enhance the developer database experience across Cloudflare Workers. With this acquisition, building database-backed applications will be more approachable — enabling more teams to build and deploy full-stack, AI-enabled applications on Cloudflare's global network. Databases are key to building any modern application. And, with the rapid growth of AI and AI agents, more software will be built over the next five years than in the past 20. Almost all of those applications will need a database to maintain context, store conversations, and act on data. Ensuring that those databases can be built and managed quickly, easily, and at scale by developers of all backgrounds will help more developers to build those applications on Cloudflare. 'Businesses are racing to build AI-powered applications to be as productive, innovative, and competitive as possible. Our goal is to make it easy and accessible for any developer, regardless of expertise, to build database-backed applications that can scale', said Matthew Prince, co-founder and CEO of Cloudflare. 'Outerbase's technology and design expertise are an important factor in accelerating this improved developer experience'. Outerbase itself is built on Cloudflare Workers, making it easy for the technology to rapidly be incorporated directly into Durable Objects, D1 and Cloudflare's Agents SDK to provide easy to use interfaces and frameworks to interact with data and build database-dependent applications. With this acquisition, Cloudflare aims to democratize data access and make it easier for teams to manage databases without extensive SQL knowledge. 'At Outerbase, our mission has always been to make working with data easier for developers. Joining Cloudflare allows us to keep doing that, but faster and at a much larger scale', said Brandon Strittmatter, co-founder and CEO of Outerbase. 'We've built Outerbase on top of Cloudflare, so this next step feels natural. Now, we get to take what we've built and make it part of the platform itself. I'm beyond excited about this opportunity — not just because of what it means for the team, but how as part of Cloudflare, we will shape the way people are building developer tools and AI applications'. To learn more, please check out the resources below: Image Credit Cloudflare
