Alternatives to Lux

ChatGPT Agent is an advanced AI system that bridges research and real-world action by using its own virtual computer to execute complex, multi-step workflows. It can browse websites visually and textually, interact with online tools, run terminal commands, and integrate with user apps through connectors like Gmail and GitHub. This unified agentic system merges the strengths of previous tools to provide end-to-end task completion, from data gathering and analysis to generating presentations and updated spreadsheets. Users remain fully in control, with the ability to monitor progress, provide feedback, and take over the browser at any point. ChatGPT Agent proactively requests permission before performing sensitive or impactful actions, ensuring transparency and safety. It significantly boosts efficiency by automating tasks such as meeting preparation, competitor analysis, and personal event planning. The model has achieved state-of-the-art results on benchmarks evaluating real-world task performance, often surpassing human experts. As a continuously evolving platform, it is designed to become more capable and useful over time.

The OpenAI Agents SDK allows developers to create agent-based AI applications in a streamlined and user-friendly manner, minimizing unnecessary complexities. This SDK serves as a polished enhancement of our earlier agent experimentation project, Swarm. It features a concise set of core components: agents, which are large language models (LLMs) with specific instructions and tools; handoffs, which facilitate task delegation among agents; and guardrails, which ensure that agent inputs are properly validated. By leveraging Python alongside these components, users can craft intricate interactions between tools and agents, making it feasible to develop practical applications without encountering a steep learning curve. Furthermore, the SDK includes integrated tracing capabilities that enable users to visualize, debug, and assess their agent workflows, as well as refine models tailored to their specific needs. This combination of features makes the Agents SDK an invaluable resource for developers aiming to harness the power of AI effectively.

Introducing the Gemini 2.5 Computer Use model, an advanced agent built upon the visual reasoning strengths of Gemini 2.5 Pro, specifically crafted for direct interaction with user interfaces (UIs). This model is accessible through a newly developed computer-use tool within the Gemini API, which takes inputs such as the user's request, a screenshot of the UI context, and a log of recent actions. It adeptly generates function calls relevant to UI tasks, including clicking, typing, or selecting, while also having the capability to seek user confirmation for tasks deemed higher risk. Following each performed action, the model receives updated feedback in the form of a new screenshot and URL to facilitate a continuous process until the task is either completed or stopped. Primarily fine-tuned for web browser navigation, it also shows potential for mobile UI interactions, although it currently lacks the capability for desktop OS-level management. In various benchmarks comparing web and mobile control tasks, the Gemini 2.5 Computer Use model demonstrates superior performance over leading competitors, achieving remarkable accuracy with reduced latency, and paving the way for future enhancements in interface interaction.

Claude, created by Anthropic, represents a cutting-edge conversational AI model that has recently introduced a groundbreaking feature known as computer use. This functionality enables Claude to engage with a computer similarly to how a human would, performing actions like moving a cursor, clicking on buttons, and typing text. The primary aim of this computer use feature is to streamline intricate workflows and manage tasks that necessitate interaction with various applications, such as completing forms or performing research. While it is currently in a public beta phase, this advancement signifies a major leap towards developing AI systems capable of operating autonomously within computing environments. Consequently, it enhances their adaptability for various business applications, including software testing, automation, and efficient task execution. As this technology evolves, it may redefine how businesses leverage AI for increased productivity and effectiveness.

Cua is a unified infrastructure for building and deploying computer-use AI agents that interact directly with operating systems and applications. Instead of automating through integrations, Cua agents work visually—understanding interfaces, clicking UI elements, typing text, and navigating software naturally. The platform supports Linux, Windows, and macOS sandboxes with cloud-based scaling. Developers can run agents via a managed UI or integrate them programmatically using the Python Agent SDK. Cua also provides dataset generation, trajectory recording, and benchmarking tools to train and evaluate agents. With pay-as-you-go pricing and smart model routing, Cua balances performance and cost efficiently. It is fully open source and designed for production-grade automation.

Upsonic is an open-source framework designed to streamline the development of AI agents tailored for business applications. It empowers developers to create, manage, and deploy agents utilizing integrated Model Context Protocol (MCP) tools, both in cloud and local settings. By incorporating built-in reliability features and a service client architecture, Upsonic significantly reduces engineering efforts by 60-70%. The framework employs a client-server model that effectively isolates agent applications, ensuring the stability and statelessness of existing systems. This architecture not only enhances the reliability of agents but also provides the necessary scalability and a task-oriented approach to address real-world challenges. Furthermore, Upsonic facilitates the characterization of autonomous agents, enabling them to set their own goals and backgrounds while integrating functionalities that allow them to perform tasks in a human-like manner. With direct support for LLM calls, developers can connect to models without needing abstraction layers, which accelerates the completion of agent tasks in a more economical way. Additionally, Upsonic's user-friendly interface and comprehensive documentation make it accessible for developers of all skill levels, fostering innovation in AI agent development.

Calljmp is a developer-first AI runtime for building and running long-lived, stateful agent workflows in production. Unlike AI agent frameworks that focus mainly on authoring logic in code, Calljmp provides a managed runtime that handles execution concerns by default. This includes durable state persistence, pause and resume for human-in-the-loop workflows, safe retries with idempotency, and built-in observability across every step of an agent’s execution. Calljmp is designed for teams using TypeScript who want to ship production-grade AI systems without stitching together queues, databases, custom state machines, and monitoring infrastructure. Developers write agent workflows as code, while the runtime guarantees reliable execution over time, even across crashes, restarts, and long waits. Calljmp targets the gap between developer-first agent frameworks and heavy workflow engines, offering a practical path from prototype to production for real-world AI agents.

OpenAGI provides a modern framework for building intelligent agents that behave more like autonomous digital workers rather than simple prompt-driven LLM tools. Unlike standard AI apps that only retrieve or summarize information, OpenAGI agents can plan ahead, make decisions, reflect on their work, and perform actions independently. The system is built to support specialized agent development across domains ranging from personalized education to automated financial analysis, medical assistance, and software engineering. Its architecture is intentionally flexible, enabling developers to orchestrate multi-agent collaboration in sequential, parallel, or adaptive workflows. OpenAGI also introduces streamlined configuration processes to eliminate infinite loops and design bottlenecks commonly seen in other agent frameworks. Both auto-generated and fully manual configuration options are available, giving developers the freedom to build quickly or fine-tune every detail. As the platform evolves, OpenAGI aims to support deeper memory, improved planning skills, and stronger self-improvement abilities in agents. The vision is to empower developers everywhere to create agents that learn continuously and handle increasingly complex real-world tasks.

Claude Sonnet 4.6 represents a comprehensive upgrade to Anthropic’s Sonnet model line, delivering expanded capabilities across coding, reasoning, computer interaction, and professional knowledge tasks. With a beta 1M token context window, the model can process massive datasets such as full repositories, extended legal agreements, or multi-document research projects in a single request. Developers report improved reliability, better instruction adherence, and fewer hallucinations, making long working sessions smoother and more predictable. Early users preferred Sonnet 4.6 over its predecessor in the majority of tests and often selected it over Opus 4.5 for practical coding work. The model’s computer-use skills have advanced significantly, enabling it to navigate spreadsheets, complete web forms, and manage multi-tab workflows with near human-level competence in many cases. Benchmark evaluations show consistent performance gains across reasoning, coding, and long-horizon planning tasks. In competitive simulations like Vending-Bench Arena, Sonnet 4.6 demonstrated strategic capacity-building and profit optimization over time. On the developer platform, it supports adaptive and extended thinking modes, context compaction, and improved tool integration for greater efficiency. Claude’s API tools now automatically execute filtering and code-processing steps to enhance search and token optimization. Sonnet 4.6 is available across Claude.ai, Cowork, Claude Code, the API, and major cloud providers at the same starting price as Sonnet 4.5.

ComputerX is an advanced AI-powered agent that simplifies computer usage by performing tasks on your behalf based on natural language instructions. You just type what you need, and ComputerX interprets your request to automate processes, conduct web research, or create various deliverables. It removes the complexity of manual computer operations, allowing users without technical expertise to get things done faster and more accurately. Whether it’s compiling information, automating routine tasks, or preparing presentations and documents, ComputerX handles it seamlessly. The platform enhances productivity by reducing the time spent switching between apps or searching for data. Its user-friendly interface invites anyone to leverage automation without learning coding or commands. ComputerX is designed to empower users to focus on higher-level work while it manages the details. It’s like having a personal digital assistant for all your computer needs.

Kagent is a versatile, open-source framework specifically designed for cloud-native AI agents, allowing teams to construct, deploy, and operate autonomous agents within Kubernetes clusters to streamline complex operational processes, troubleshoot cloud-native infrastructures, and oversee workloads with minimal human oversight. This framework empowers DevOps and platform engineers to develop intelligent agents capable of comprehending natural language, planning strategically, reasoning effectively, and executing a series of actions across Kubernetes environments by utilizing integrated tools and Model Context Protocol (MCP)-compatible integrations for various functions, including metric queries, pod log displays, resource management, and service mesh interactions. Additionally, Kagent facilitates communication between agents to orchestrate intricate workflows and includes observability features that enable teams to track and assess agent performance and behavior. Furthermore, its compatibility with multiple model providers, such as OpenAI and Anthropic, enhances its versatility and adaptability within diverse operational contexts.

LaVague is an open-source framework that empowers developers to effortlessly create and deploy AI-based web agents with minimal coding requirements. Utilizing Large Action Models (LAMs), LaVague facilitates the automation of intricate web tasks through natural language commands. By allowing developers to define goals in simple terms, agents can be built to navigate websites, gather data, and execute actions. The framework is compatible with various drivers, such as Selenium and Playwright, and offers adaptable configurations for a wide range of applications. In addition, LaVague includes tailored tools for quality assurance professionals, like LaVague QA, which simplifies test creation by transforming Gherkin specifications into runnable tests. This platform prioritizes flexibility, user privacy, and high performance, enabling agents to leverage local models and integrate smoothly with current systems. Furthermore, its user-friendly design ensures that even those with limited coding experience can effectively harness its capabilities.

Vogent serves as a comprehensive platform designed to create intelligent and lifelike voice agents that efficiently handle tasks. This innovative technology features a remarkably authentic, low-latency voice AI capable of conducting phone conversations lasting up to an hour while also managing subsequent tasks. It is particularly beneficial for sectors such as healthcare, construction, logistics, and travel, where it streamlines communication. The platform is equipped with a complete end-to-end system for transcription, reasoning, and speech, ensuring conversations that are both humanlike and timely. Notably, Vogent's proprietary language models, refined through extensive training on millions of phone interactions across diverse task categories, demonstrate performance that rivals that of human agents, especially when fine-tuned with a few examples. Developers benefit from the ability to initiate thousands of calls using minimal code and automate various workflows based on specific outcomes. Additionally, the platform features robust REST and GraphQL APIs, along with a user-friendly no-code dashboard that allows users to craft agents, upload knowledge bases, monitor calls, and export conversation transcripts, making it an invaluable tool for enhancing operational efficiency. With these capabilities, Vogent empowers businesses to revolutionize their customer interaction processes.

ServiceNow's AI Agents are self-sufficient systems integrated into the Now Platform, aimed at executing repetitive tasks that were once managed by human workers. These agents engage with their surroundings to gather information, make informed decisions, and carry out tasks, leading to improved efficiency over time. By utilizing specialized large language models along with a powerful reasoning engine, they gain a comprehensive understanding of various business contexts, which fosters ongoing enhancements in performance. Functioning natively across diverse workflows and data platforms, AI Agents promote complete automation, thereby increasing team productivity by coordinating workflows, integrations, and actions within the organization. Companies have the option to implement pre-existing AI agents or create personalized ones to meet their unique requirements, all while operating smoothly on the Now Platform. This seamless integration not only streamlines processes but also enables employees to devote their attention to more strategic initiatives by relieving them of mundane tasks, ultimately driving innovation and growth within the organization. As a result, the implementation of AI Agents represents a significant step towards transforming workplace efficiency.

Amazon Bedrock AgentCore allows for the secure deployment and management of advanced AI agents at scale, featuring infrastructure specifically designed for dynamic agent workloads, robust tools for agent enhancement, and vital controls for real-world applications. It is compatible with any framework and foundation model, whether within or outside of Amazon Bedrock, thus eliminating the burdensome need for specialized infrastructure. AgentCore ensures complete session isolation and offers industry-leading support for prolonged workloads lasting up to eight hours, with seamless integration into existing identity providers for smooth authentication and permission management. Additionally, a gateway is utilized to convert APIs into tools that are ready for agents with minimal coding required, while built-in memory preserves context throughout interactions. Furthermore, agents benefit from a secure browser environment that facilitates complex web-based tasks and a sandboxed code interpreter, which is ideal for functions such as creating visualizations, enhancing their overall capability. This combination of features significantly streamlines the development process, making it easier for organizations to leverage AI technology effectively.

Agent S is an open-source framework designed to power autonomous AI agents capable of interacting directly with computers. Through its Agent-Computer Interface (ACI), the system enables models to observe graphical user interfaces, interpret on-screen elements, and perform tasks as a human operator would. Compatible with macOS, Windows, and Linux, it supports cross-platform automation for real-world applications. The latest version, Agent S3, exceeds human-level benchmarks on OSWorld, showcasing exceptional performance in long, multi-step workflows. The framework leverages advanced foundation models like GPT-5 alongside specialized grounding models such as UI-TARS to convert visual data into structured, executable actions. Its architecture emphasizes precise control, task decomposition, and intelligent decision-making across dynamic desktop environments. Agent S can be deployed flexibly via command-line interface, software development kits, or cloud-based infrastructure. It connects with major AI providers including OpenAI, Anthropic, Gemini, Azure, and Hugging Face, offering model flexibility and extensibility. Optional local code execution allows for secure and customizable task handling. Combined with built-in reflection and compositional planning systems, Agent S delivers a research-driven and production-ready solution for building high-performance computer-use agents.

With Letta, you can create, deploy, and manage your agents on a large scale, allowing the development of production applications supported by agent microservices that utilize REST APIs. By integrating memory capabilities into your LLM services, Letta enhances their advanced reasoning skills and provides transparent long-term memory through the innovative technology powered by MemGPT. We hold the belief that the foundation of programming agents lies in the programming of memory itself. Developed by the team behind MemGPT, this platform offers self-managed memory specifically designed for LLMs. Letta's Agent Development Environment (ADE) allows you to reveal the full sequence of tool calls, reasoning processes, and decisions that contribute to the outputs generated by your agents. Unlike many systems that are limited to just prototyping, Letta is engineered by systems experts for large-scale production, ensuring that the agents you design can grow in effectiveness over time. You can easily interrogate the system, debug your agents, and refine their outputs without falling prey to the opaque, black box solutions offered by major closed AI corporations, empowering you to have complete control over your development process. Experience a new era of agent management where transparency and scalability go hand in hand.

HappyRobot is an innovative operating system rooted in artificial intelligence, crafted to facilitate autonomous operations by coordinating customizable "AI workers" that comprehend your business, make smart decisions, and respond instantly. It is specifically designed to enhance enterprise workflows across various sectors such as logistics, supply chain, retail, and services, empowering you to develop AI agents capable of conversing, typing, reasoning, negotiating, scheduling tasks, processing documents, browsing systems, and escalating issues when necessary. These AI workers handle tasks through multiple communication channels, including voice calls, emails, and messages, leveraging sophisticated reasoning through large language models that are seamlessly integrated with your tools and workflows via APIs, webhooks, or browser agents. You can oversee this AI workforce from a unified "control tower," allowing you to deploy, monitor, and refine workflows in natural language or through user-friendly interfaces, providing clear insights into every task and decision made by the AI. Moreover, with the continuous evolution of AI capabilities, HappyRobot ensures your operations remain cutting-edge and adaptable to the ever-changing business landscape.

Qwen3-Max represents Alibaba's cutting-edge large language model, featuring a staggering trillion parameters aimed at enhancing capabilities in tasks that require agency, coding, reasoning, and managing lengthy contexts. This model is an evolution of the Qwen3 series, leveraging advancements in architecture, training methods, and inference techniques; it integrates both thinker and non-thinker modes, incorporates a unique “thinking budget” system, and allows for dynamic mode adjustments based on task complexity. Capable of handling exceptionally lengthy inputs, processing hundreds of thousands of tokens, it also supports tool invocation and demonstrates impressive results across various benchmarks, including coding, multi-step reasoning, and agent evaluations like Tau2-Bench. While the initial version prioritizes instruction adherence in a non-thinking mode, Alibaba is set to introduce reasoning functionalities that will facilitate autonomous agent operations in the future. In addition to its existing multilingual capabilities and extensive training on trillions of tokens, Qwen3-Max is accessible through API interfaces that align seamlessly with OpenAI-style functionalities, ensuring broad usability across applications. This comprehensive framework positions Qwen3-Max as a formidable player in the realm of advanced artificial intelligence language models.

GPT-5.3-Codex is a next-generation AI agent built to expand Codex beyond code writing into full-spectrum professional execution. It unifies advanced coding intelligence with reasoning, planning, and computer-use capabilities. The model delivers faster performance while handling more complex workflows across development environments. GPT-5.3-Codex can autonomously iterate on large projects while remaining interactive and steerable. It supports tasks such as debugging, deployment, performance optimization, and system monitoring. The model demonstrates state-of-the-art results across real-world coding benchmarks. It also excels at web development, generating production-ready applications from minimal prompts. GPT-5.3-Codex understands intent more effectively, producing stronger default designs and functionality. Its agentic nature allows it to operate like a collaborative teammate. This makes it suitable for both individual developers and large teams.

Project Mariner is an innovative research prototype created by Google DeepMind, utilizing their sophisticated AI model, Gemini 2.0. This project investigates the potential for enhanced human-agent interaction by automating a variety of tasks directly within a user's web browser. With its ability to understand multiple forms of information, Project Mariner can analyze and reason through diverse browser components, such as text, code snippets, images, and online forms. This functionality empowers it to adeptly navigate intricate websites, streamline repetitive workflows, and supply users with visual updates. The system is also capable of interpreting voice commands, providing real-time task progress updates and ensuring that users stay informed and maintain control over their activities. Furthermore, Project Mariner excels at deciphering complex instructions by deconstructing them into manageable steps, grasping the interconnections between different web elements, and delivering coherent plans and actions to users. Currently, the initiative is undergoing testing with a limited number of selected users, and those wishing to engage in future testing can express their interest by joining a waitlist. This approach not only fosters user engagement but also helps refine the system based on real-world feedback.

Strands Agents presents a streamlined, code-oriented framework aimed at facilitating the creation of AI agents, which capitalizes on the advanced reasoning skills of contemporary language models to ease the development process. With just a few lines of Python code, developers can swiftly construct agents by outlining a prompt and specifying a set of tools, empowering the agents to carry out intricate tasks independently. The framework is compatible with various model providers, such as Amazon Bedrock (with Claude 3.7 Sonnet as the default), Anthropic, OpenAI, among others, providing users with diverse options for model selection. An adaptable agent loop is a standout feature, managing user inputs, determining appropriate tool usage, executing those tools, and crafting responses, thereby accommodating both streaming and non-streaming interactions. Furthermore, the inclusion of built-in tools, along with the option to create custom tools, enables agents to undertake a broad spectrum of activities that extend well beyond mere text generation, enhancing their utility in various applications. This versatility positions Strands Agents as an innovative solution in the realm of AI agent development.

Agent Builder is a component of OpenAI’s suite designed for creating agentic applications, which are systems that leverage large language models to autonomously carry out multi-step tasks while incorporating governance, tool integration, memory, orchestration, and observability features. This platform provides a flexible collection of components—such as models, tools, memory/state, guardrails, and workflow orchestration—which developers can piece together to create agents that determine the appropriate moments to utilize a tool, take action, or pause and transfer control. Additionally, OpenAI has introduced a new Responses API that merges chat functions with integrated tool usage, alongside an Agents SDK available in Python and JS/TS that simplifies the control loop, enforces guardrails (validations on inputs and outputs), manages agent handoffs, oversees session management, and tracks agent activities. Furthermore, agents can be enhanced with various built-in tools, including web search, file search, or computer functionalities, as well as custom function-calling tools, allowing for a diverse range of operational capabilities. Overall, this comprehensive ecosystem empowers developers to craft sophisticated applications that can adapt and respond to user needs with remarkable efficiency.

QVeris serves as an infrastructure for AI agents tailored for developers, focusing on MCP and skills-oriented agents. It offers a cohesive API that allows users to discover, access, and utilize over 10,000 real-world tools, APIs, and data resources, empowering LLMs and AI agents to analyze, fetch information, and perform real-world tasks efficiently and at scale. This innovative approach significantly enhances the capabilities and versatility of AI applications in various domains.

Step 3.5 Flash is a cutting-edge open-source foundational language model designed for advanced reasoning and agent-like capabilities, optimized for efficiency; it utilizes a sparse Mixture of Experts (MoE) architecture that activates only approximately 11 billion of its nearly 196 billion parameters per token, ensuring high-density intelligence and quick responsiveness. The model features a 3-way Multi-Token Prediction (MTP-3) mechanism that allows it to generate hundreds of tokens per second, facilitating complex multi-step reasoning and task execution while efficiently managing long contexts through a hybrid sliding window attention method that minimizes computational demands across extensive datasets or codebases. Its performance on reasoning, coding, and agentic tasks is formidable, often matching or surpassing that of much larger proprietary models, and it incorporates a scalable reinforcement learning system that enables continuous self-enhancement. Moreover, this innovative approach positions Step 3.5 Flash as a significant player in the field of AI language models, showcasing its potential to revolutionize various applications.

Portia AI is an open-source developer framework that includes optional cloud services, enabling teams to quickly create, deploy, and oversee stateful, authenticated AI agents while maintaining full visibility and control over the process. Developers initiate the process by using the SDK to generate clear, organized multi-step "plans" that integrate LLM reasoning with various tool calls, executing these plans incrementally and enhancing the plan state at each step, while also allowing for pauses to seek clarifications, whether from human users or machine inputs, when authentication or additional information is necessary. With its cohesive authentication framework and an easily customizable tool catalog, Portia automatically manages the credentials and permissions needed for remote API and MCP tool calls. Furthermore, the accompanying cloud solution provides persistent storage for plan execution states, historical log tracking, telemetry dashboards, and managed scaling, ensuring that production deployments remain dependable, traceable, and compliant with regulatory standards. This comprehensive approach not only simplifies the development process but also enhances the overall efficiency and effectiveness of AI agent deployments.

Instruct enables users to create AI agents rapidly by simply articulating the intended goals in plain language, eliminating the need for coding or intricate logic. The platform seamlessly integrates with a multitude of external tools and services, allowing these agents to perform tasks that can be initiated either manually or automatically. It encompasses the entire lifecycle of agent utilization, starting with defining the agent's objectives, followed by linking relevant accounts and workflows, and culminating in the immediate or trigger-based deployment of the agent. These agents are capable of functioning across various sectors, including finance, sales, operations, and marketing, autonomously executing complex multi-step tasks. Designed for resilience, they adapt to changes and manage unforeseen circumstances without faltering. With a focus on outcome-driven intelligence, users set the success criteria while the agent determines the most efficient route to achieve those goals. Ultimately, this innovative approach encourages users to harness AI capabilities without the barriers typically associated with technology.

OptiSol's Agentic Process Automation (APA) solutions aim to elevate the standard of task automation by integrating intelligent agents that can make decisions independently and optimize processes. These advanced agents are equipped to understand the context of their environment, predict potential outcomes, and carry out actions with minimal reliance on human operators, leading to improved efficiency across various sectors such as finance, operations, customer service, and supply chain management. Among the standout features of OptiSol's APA are context-sensitive decision-making, proactive management of workflows, ongoing optimization of processes, increased agility in business operations, and the ability to scale effectively. By harnessing these innovative capabilities, organizations can realize smarter automation, accelerate their operational processes, and ensure continuous improvement to maintain a competitive edge in the marketplace. Ultimately, this approach fosters a dynamic environment where businesses can adapt and thrive in response to changing demands.

Gobii is a cloud-based service that allows users to deploy fully managed browser automation agents through an API, facilitating the automation of web research, form submissions, data extraction, and complex workflows on a large scale. These agents function like perpetual employees, capable of navigating websites—even those without APIs—managing dynamic content, executing JavaScript, and automatically rotating proxies when necessary. Users have the ability to create these agents, assign them specific prompts or tasks, and obtain structured JSON outputs or real-time previews of the agents' browser activities. Gobii also accommodates both synchronous and asynchronous task execution, offers secret management for sensitive information like login credentials, and ensures output validation through schema enforcement. Furthermore, it integrates with widely used programming languages such as Python and Node.js for easy implementation. The platform places a strong emphasis on scalability, allowing for the execution of hundreds of tasks simultaneously, while also providing enterprise-level security features like audit logs, proxy management, and comprehensive task oversight. As a result, developers benefit from a streamlined experience that makes it easier to integrate complex automation into their workflows.

SpawnHQ is a SaaS platform that enables users to quickly deploy, configure, and manage autonomous AI agents within minutes, eliminating the need for coding or infrastructure setup. By providing a marketplace filled with pre-built, skill-based agents tailored to your brand's context, these agents operate continuously on managed computing resources and seamlessly integrate with various tools such as Discord, web chat widgets, Twitter, SEO services, and customer relationship management systems. Users can select specific skills, including a support bot for addressing customer inquiries, an SEO agent for tracking rankings and creating content, an outbound agent for lead generation and outreach, or social and content engines, and then set up the necessary integrations along with their brand context. Once configured, these agents can respond to natural language commands and function autonomously, managing tasks like research, CRM updates, content creation, and automated replies around the clock. The platform takes care of managed compute, AI model routing (including Claude, GPT, and Gemini), scheduling, logging, reporting, and implementing guardrails, which empowers the agents to think and act with a degree of independence. This capability allows businesses to streamline their operations and enhance efficiency without requiring extensive technical knowledge.

Adopt AI helps modern applications deliver an agentic experience to their end users within days. Using Adopt, end users of applications can execute complex actions across their application via natural language commands, automate workflows and unlock new possibilities for application innovation. In this future, AI agents will understand application/website workflows, know which components to call, create dynamic plans, and execute those plans to achieve desired outcomes. This approach means that humans will no longer need to learn how to use applications; instead, they can interact with AI in natural language to accomplish tasks or have AI automatically perform tasks based on schedules or triggers. Adopt AI is helping companies race against time to build their own AI copilot and set of autonomous/semi-autonomous agents.

Cortex AgentiX is an advanced AI agent orchestration platform from Palo Alto Networks that transforms how security teams automate and respond to threats. Built as the next generation of Cortex XSOAR®, it enables organizations to deploy AI agents that function as always-on digital teammates. These agents leverage billions of prior playbook executions to plan, reason, and execute complex security workflows with confidence. Cortex AgentiX provides flexibility through a comprehensive catalog of prebuilt agents as well as no-code tools for creating custom agents. The platform allows security leaders to define when agents operate autonomously and when human oversight is required. Strong access controls and permissions ensure agents follow the same governance rules as human analysts. Cortex AgentiX delivers complete transparency into agent behavior, eliminating black-box decision-making. Native support for natural language automation simplifies the creation of executable workflows. With over 1,000 prebuilt integrations, the platform connects easily to existing security tools. Cortex AgentiX helps organizations scale security operations while maintaining control, accountability, and compliance.

The Gemini Deep Research Agent provides an intelligent, automated research workflow that behaves like an “analyst-in-a-box,” capable of planning, searching, reading, and synthesizing information across the public web and proprietary data sources. Using Gemini 3 Pro at its core, it navigates multi-step reasoning tasks and produces long-form, citation-backed reports that traditional LLM calls cannot match. Developers use asynchronous background execution to support research cycles that run for several minutes, with automatic polling and reconnect logic for reliability. Streaming mode offers real-time transparency, surfacing thought summaries and partial findings throughout the process. The agent is steerable through formatting instructions, allowing teams to generate technical reports, competitive analyses, or structured documents with precision. It also supports follow-up questions tied to prior interactions, enabling iterative refinement and deeper exploration. Designed with safety controls, it protects against harmful web content, prompt injection risks, and unintended data exposure. This makes it ideal for organizations seeking to automate sophisticated research workflows while maintaining operational control and trustworthiness.

This Python script exemplifies an AI-driven task management system that leverages both OpenAI and Chroma to manage tasks effectively. The core concept of this system is that it generates tasks informed by prior outcomes and a set goal. Utilizing OpenAI's natural language processing (NLP), the script formulates new tasks aligned with its objectives while employing Chroma to archive and access task outcomes for added context. This implementation serves as a simplified version of the original Task-Driven Autonomous Agent. The script operates within an endless loop executing a series of defined steps, which include: 1. Retrieving the initial task from the list of tasks. 2. Dispatching the task to the execution agent, which utilizes OpenAI's API to accomplish the task within the contextual framework. 3. Enhancing the result obtained and saving it in Chroma for future reference. 4. Generating additional tasks and rearranging the task list according to the overarching objective and the results from the completed task, ensuring continuous adaptation and improvement in task management. This approach allows for a dynamic and responsive task management system that evolves with each completed task.

These automated AI processes are designed to operate in a linear sequence, culminating in a specific result, with each phase enhancing the preceding one to ensure uniform and reliable results each time. These workflows can be stored and executed repeatedly as required, enabling the automation of various tasks, such as managing customer service inquiries or compiling, summarizing, and distributing current news through a newsletter. Additionally, establish an internal knowledge repository to equip your AI agents with the necessary information to make well-informed decisions regarding your unique projects. This may include providing documentation to empower an AI-driven customer service representative, creating a readme file to assist an AI code reviewer, or supplying product specifications to guide an AI sales representative. By doing so, you enhance the overall effectiveness of AI in your organization.

Collaborating with Agents enhances customer success for businesses. You can create and tailor autonomous AI agents to provide round-the-clock assistance to both your staff and clientele. An Agentforce Agent functions as a proactive application, delivering specialized, always-available support for employees or customers, and is armed with the essential knowledge to perform tasks relevant to their designated roles. Agentforce Service Agents autonomously interact with customers through various channels at any time, utilizing natural language for communication. They swiftly and accurately resolve inquiries, as their responses are based on reliable data. You can set up your Service Agent in mere minutes with pre-existing templates or customize it rapidly using low-code solutions. Additionally, you can establish clear security protocols and determine when to escalate issues to human representatives seamlessly. Meanwhile, Agentforce SDRs automatically respond to inquiries about products, manage tasks, and schedule meetings for sales teams. They operate with precision, ensuring that their answers are firmly rooted in your business’s data, thereby enhancing operational efficiency. This innovative approach not only streamlines processes but also empowers employees to focus on more complex tasks that require human insight.

Agno is a streamlined framework designed for creating agents equipped with memory, knowledge, tools, and reasoning capabilities. It allows developers to construct a variety of agents, including reasoning agents, multimodal agents, teams of agents, and comprehensive agent workflows. Additionally, Agno features an attractive user interface that facilitates communication with agents and includes tools for performance monitoring and evaluation. Being model-agnostic, it ensures a consistent interface across more than 23 model providers, eliminating the risk of vendor lock-in. Agents can be instantiated in roughly 2μs on average, which is about 10,000 times quicker than LangGraph, while consuming an average of only 3.75KiB of memory—50 times less than LangGraph. The framework prioritizes reasoning, enabling agents to engage in "thinking" and "analysis" through reasoning models, ReasoningTools, or a tailored CoT+Tool-use method. Furthermore, Agno supports native multimodality, allowing agents to handle various inputs and outputs such as text, images, audio, and video. The framework's sophisticated multi-agent architecture encompasses three operational modes: route, collaborate, and coordinate, enhancing the flexibility and effectiveness of agent interactions. By integrating these features, Agno provides a robust platform for developing intelligent agents that can adapt to diverse tasks and scenarios.

Phi-4-mini-reasoning is a transformer-based language model with 3.8 billion parameters, specifically designed to excel in mathematical reasoning and methodical problem-solving within environments that have limited computational capacity or latency constraints. Its optimization stems from fine-tuning with synthetic data produced by the DeepSeek-R1 model, striking a balance between efficiency and sophisticated reasoning capabilities. With training that encompasses over one million varied math problems, ranging in complexity from middle school to Ph.D. level, Phi-4-mini-reasoning demonstrates superior performance to its base model in generating lengthy sentences across multiple assessments and outshines larger counterparts such as OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. Equipped with a 128K-token context window, it also facilitates function calling, which allows for seamless integration with various external tools and APIs. Moreover, Phi-4-mini-reasoning can be quantized through the Microsoft Olive or Apple MLX Framework, enabling its deployment on a variety of edge devices, including IoT gadgets, laptops, and smartphones. Its design not only enhances user accessibility but also expands the potential for innovative applications in mathematical fields.

Smolagents is a framework designed for AI agents that streamlines the development and implementation of intelligent agents with minimal coding effort. It allows for the use of code-first agents that run Python code snippets to accomplish tasks more efficiently than conventional JSON-based methods. By integrating with popular large language models, including those from Hugging Face and OpenAI, developers can create agents capable of managing workflows, invoking functions, and interacting with external systems seamlessly. The framework prioritizes user-friendliness, enabling users to define and execute agents in just a few lines of code. It also offers secure execution environments, such as sandboxed spaces, ensuring safe code execution. Moreover, Smolagents fosters collaboration by providing deep integration with the Hugging Face Hub, facilitating the sharing and importing of various tools. With support for a wide range of applications, from basic tasks to complex multi-agent workflows, it delivers both flexibility and significant performance enhancements. As a result, developers can harness the power of AI more effectively than ever before.

The Holo2 model family from H Company offers a blend of affordability and high performance in vision-language models specifically designed for computer-based agents that can navigate, localize user interface elements, and function across web, desktop, and mobile platforms. This new series, which is available in sizes of 4 billion, 8 billion, and 30 billion parameters, builds upon the foundations laid by the earlier Holo1 and Holo1.5 models, ensuring strong grounding in user interfaces while making substantial improvements to navigation abilities. Utilizing a mixture-of-experts (MoE) architecture, the Holo2 models activate only the necessary parameters to maximize operational efficiency. These models have been trained on carefully curated datasets focused on localization and agent functionality, allowing them to seamlessly replace their predecessors. They provide support for effortless inference in environments compatible with Qwen3-VL models and can be easily incorporated into agentic workflows such as Surfer 2. In benchmark evaluations, the Holo2-30B-A3B model demonstrated impressive results, achieving 66.1% accuracy on the ScreenSpot-Pro test and 76.1% on the OSWorld-G benchmark, thereby establishing itself as the leader in the UI localization sector. Additionally, the advancements in the Holo2 models make them a compelling choice for developers looking to enhance the efficiency and performance of their applications.

MGX (MetaGPT X) serves as a versatile AI platform that replicates the functions of a complete software development team, allowing users to turn their ideas into reality, whether they involve websites, blogs, online stores, analytics tools, games, or other creative projects. Users can engage with various AI personas, including roles like team leader, product manager, architect, engineer, and data analyst, which facilitates round-the-clock project execution without requiring any coding skills. By applying established software development procedures, MGX guarantees an organized and effective project creation process. The platform provides an effortless environment where users can envision, converse, and produce, leading to the realization of their creative concepts. Additionally, MGX ensures specialized expertise is available for each stage of development, minimizes context confusion between various phases, reduces operational costs by allowing agents to concentrate solely on their designated tasks, and permits the independent swapping or upgrading of specific agents. Ultimately, the system fosters a more intuitive development experience that closely resembles the collaborative dynamics of human teams. This innovative approach not only enhances productivity but also empowers users to fully realize their potential in the digital landscape.

Claude Sonnet 4.5 represents Anthropic's latest advancement in AI, crafted to thrive in extended coding environments, complex workflows, and heavy computational tasks while prioritizing safety and alignment. It sets new benchmarks with its top-tier performance on the SWE-bench Verified benchmark for software engineering and excels in the OSWorld benchmark for computer usage, demonstrating an impressive capacity to maintain concentration for over 30 hours on intricate, multi-step assignments. Enhancements in tool management, memory capabilities, and context interpretation empower the model to engage in more advanced reasoning, leading to a better grasp of various fields, including finance, law, and STEM, as well as a deeper understanding of coding intricacies. The system incorporates features for context editing and memory management, facilitating prolonged dialogues or multi-agent collaborations, while it also permits code execution and the generation of files within Claude applications. Deployed at AI Safety Level 3 (ASL-3), Sonnet 4.5 is equipped with classifiers that guard against inputs or outputs related to hazardous domains and includes defenses against prompt injection, ensuring a more secure interaction. This model signifies a significant leap forward in the intelligent automation of complex tasks, aiming to reshape how users engage with AI technologies.

Common Lisp stands out as a contemporary, multi-faceted, high-performance, compiled language that adheres to ANSI standards, making it one of the leading successors, alongside Scheme, in the extensive lineage of Lisp programming languages. Renowned for its remarkable adaptability, it offers robust support for object-oriented programming and facilitates rapid prototyping. The language is equipped with an exceptionally powerful macro system, enabling developers to customize it to fit specific applications, along with a versatile runtime environment that permits on-the-fly modifications and debugging of active applications, which is particularly advantageous for server-side development and mission-critical software that requires long operational lifespans. Additionally, Common Lisp's multi-paradigm nature empowers developers to select the programming approach best suited to their particular application requirements. This flexibility not only enhances productivity but also fosters innovation in software design.

Questera serves as a dynamic customer engagement platform designed for growth and lifecycle teams to both formulate and implement strategies with unprecedented speed. It empowers users to either design custom AI agents tailored to their needs or take advantage of readily available pre-configured agents for seamless personalization, automatic segmentation, journey automation, and hands-free campaign management, all of which provide boundless scalability. The platform simplifies the creation of AI agents into a straightforward four-step process: first, users define the agent by inputting essential details like its name and image; next, they specify core instructions that outline the agent's goals, rules, and operating parameters; third, they select from over 75 integrations to enable abilities such as email automation, segmentation, and retargeting; and lastly, they can test and launch the agent while receiving real-time feedback on its performance. For those looking for a quicker start, Questera offers a range of pre-configured agents, including Sara, the intelligent ads retargeting agent, which specializes in generating personalized advertisements aimed at re-engaging past visitors and executing effective retargeting campaigns. This flexibility allows businesses to adapt their engagement strategies swiftly and efficiently, ensuring they can meet the evolving demands of their customers.

Openwork is a local, open-source AI agent designed for Mac that operates directly on your computer, allowing you to manage your files, create and modify documents, streamline workflows, and organize various types of content without transmitting any of your data to external servers, thereby ensuring you maintain complete control over its capabilities and access. Functioning as a collaborative AI assistant, it goes beyond mere conversational responses by executing tangible tasks such as sorting, renaming, and relocating files according to predefined criteria or content analysis, summarizing folder contents, crafting follow-up documents from meeting notes, setting up calendar entries, and organizing project frameworks with minimal user input. Additionally, Openwork empowers users to integrate their own AI by utilizing preferred API keys from providers like OpenAI or Anthropic, which eliminates subscription fees and vendor lock-in, allowing you to pay solely for the API usage you select; thus, the software serves as a customizable tool rather than a conventional service, making it a versatile solution for individual needs. Now, Openwork stands out by offering a seamless and personal approach to AI assistance in your daily tasks.