Alternatives to Endeavor

MiMo-V2.6-Pro is an open-source, natively omnimodal AI model from Xiaomi MiMo designed for coding, agentic workflows, visual creation, research, and computer-based tasks. It is the highest-capability model in the MiMo-V2.6 series and was trained using a large-scale reinforcement learning process focused on verifiable, complex tasks. The model can handle software engineering, automation, tool use, visual reasoning, and long-running agent workflows across multiple environments. Its multimodal capabilities extend beyond traditional coding into 3D scene generation, Blender modeling, embodied simulation, frontend development, presentation design, video creation, and music composition. MiMo-V2.6-Pro can take text, images, video, and other visual inputs and coordinate multi-agent workflows to produce and iteratively refine complex outputs. Research applications demonstrated by Xiaomi include materials discovery, literature analysis, computational simulation, hypothesis generation, and formalizing mathematical proofs in Lean. Xiaomi has released the model alongside its technical report, reinforcement learning environments, and RL code so researchers can study and reproduce the training approach. MiMo-V2.6-Pro is also available in an UltraSpeed configuration that provides substantially faster output for latency-sensitive workflows. Users can access the model through MiMo Desktop, AI Studio, MiMo Code, the MiMo API Platform, OpenRouter, and Hugging Face.

Appsmith enables organizations to create custom internal applications quickly with minimal coding. The platform allows users to build applications by connecting data sources, APIs, and workflows through a user-friendly drag-and-drop interface. Appsmith's flexibility with JavaScript lets developers fully customize components, while the open-source architecture and enterprise security features ensure scalability and compliance. With self-hosting and cloud deployment options, businesses can choose the best setup for their needs, whether for simple dashboards or complex business applications. Appsmith offers a comprehensive solution for creating and deploying custom AI agents that can automate key business processes. Designed for sales, support, and people management teams, the platform allows companies to embed conversational agents into their systems. Appsmith's AI agents enhance operational efficiency by managing routine tasks, providing real-time insights, and boosting team productivity, all while leveraging secure data.

VibeAgent serves as a cloud-based visual platform designed for the creation and deployment of AI agents and automated workflows. It enables developers, entrepreneurs, and teams to design workflows using an intuitive drag-and-drop interface, where they can set up agent roles, configure model parameters, and establish connections between triggers, decision-making processes, tools, and sources of information. Workflows can be initiated from various events, such as scheduled tasks, webhooks, form submissions, or chat interactions, allowing for the orchestration of complex multi-step actions across integrated services. The platform is compatible with several models, including GPT-4, Claude, Gemini, and various open-source alternatives, and can incorporate document-based knowledge repositories. Users have the ability to deploy their agents and track their performance through comprehensive analytics dashboards. VibeAgent is particularly beneficial for teams looking to quickly prototype and run agent-based tools without the need to develop the underlying workflow infrastructure independently; features such as available connectors, model access, and execution limits differ according to the chosen plan. Additionally, the flexibility of VibeAgent allows for seamless integration of multiple services, enhancing productivity and efficiency within teams.

Kimi K2.7 Code is a Moonshot AI coding model built to help developers handle software engineering, code generation, debugging, and agent-based development workflows. It focuses on long-horizon coding tasks, where an AI assistant needs to understand goals, work across many files, and complete multi-step development work. The model builds on the Kimi K2.6 architecture and is described as improving agentic capabilities while reducing thinking-token usage by about 30% compared with K2.6. Kimi K2.7 Code offers a 256K context window, which helps developers work with larger repositories, longer prompts, and more detailed project instructions. It can be accessed through Kimi Code, Moonshot’s API platform, and third-party model providers such as Together AI. The model also supports OpenAI- and Anthropic-compatible APIs, making it easier for teams to test it as a replacement or addition to existing coding assistant workflows. Developers who want to self-host or experiment with the model can access it through Hugging Face, where deployment guidance references vLLM, SGLang, and KTransformers. Kimi K2.7 Code is especially relevant for teams interested in open-source coding agents, long-context software tasks, and tool-integrated development. While some third-party commentary notes that benchmark claims should be reviewed carefully, the model is positioned as a strong option for developers seeking flexible, agentic coding support.

The Agent Development Kit (ADK) is a powerful open-source platform designed to help developers create AI agents with ease. It integrates seamlessly with Google’s Gemini models and various AI tools, providing a modular framework for building both basic and complex agents. ADK supports flexible workflows, multi-agent systems, and dynamic routing, enabling users to create adaptive agents. The platform offers a rich set of pre-built tools, third-party library integrations, and deployment options, making it ideal for building scalable AI applications in any environment, from local setups to cloud-based systems.

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.

Oasis serves as a no-code platform that enables users to swiftly create coordinated teams of AI agents. By integrating these agents with your existing business tools, you can provide them with a shared context and memory, while also establishing policies and approval checkpoints to ensure that human oversight is maintained as agents manage complex multi-step tasks across various domains such as sales, support, and operations. Users have the option to either develop custom agents or utilize those from a pre-existing library, collaborate in multiplayer sessions with their teams, schedule automated tasks, and connect various integrations seamlessly. The platform supports frontier models like Claude, GPT, and Gemini, along with open-source alternatives, offering flexible pay-as-you-go pricing and a bring-your-own-API-key feature. Additionally, Oasis includes role-based access, SSO/SAML, audit logs, and data residency controls, making it suitable for teams of any size. With a free plan available and paid subscriptions starting at just $19 per month, it provides an accessible entry point for teams looking to leverage AI. Overall, Oasis empowers businesses to optimize workflows efficiently while maintaining necessary oversight and control.

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.

ZGI is an open-source AI platform designed for enterprises to create business-oriented agents that leverage their specific data, tools, workflows, models, and expertise. With its Agent Runtime feature, agents can efficiently load Skills, access real-time company insights, utilize various tools, and quickly deliver valuable results. The Model Gateway integrates multiple global and domestic model providers, such as OpenAI, Anthropic, Google, DeepSeek, and Qwen, enabling teams to select the most suitable model for each agent based on criteria like quality, availability, cost, or geographic location while maintaining centralized control over access, quotas, routing policies, and fallback options. The comprehensive product workspace encompasses the entire agent development lifecycle: Agent Studio merges Skills, knowledge, tools, and models; Workflows facilitate complex, multi-step processes and allow users to monitor each execution closely; the Database enables natural-language queries over real-time business information while adhering to access governance; Model Management oversees provider connections and enterprise-level routing; and Knowledge Assets transform company documents into searchable, context-aware knowledge repositories. By integrating these features, ZGI aims to streamline the deployment and management of AI agents within enterprises.

Strands Agents SDK is an open-source development framework that allows developers to build and manage AI agents with precision and control. It supports both Python and TypeScript, making it accessible to a wide range of developers and use cases. Instead of relying on rigid workflows or orchestration layers, the SDK lets developers define tools as functions and rely on the model’s reasoning capabilities to drive execution. The platform works across any AI model or cloud environment, offering flexibility for deployment and scaling. One of its standout features is the use of steering hooks, which act as middleware to guide, validate, and correct agent actions in real time. It also includes support for multi-agent systems, enabling complex workflows through agent collaboration. Built-in memory management ensures context is maintained across long interactions without manual intervention. Developers can monitor performance through observability tools that provide detailed traces and metrics. The SDK also includes an evaluation framework for testing agent accuracy and behavior before deployment. Overall, Strands Agents SDK empowers developers to create reliable, scalable, and intelligent AI agents with minimal complexity.

CAMEL-AI represents the inaugural framework for multi-agent systems based on large language models and fosters an open-source community focused on investigating the scaling dynamics of agents. This innovative platform allows users to design customizable agents through modular components that are specifically suited for particular tasks, thereby promoting the creation of multi-agent systems that tackle issues related to autonomous collaboration. Serving as a versatile foundation for a wide range of applications, the framework is ideal for tasks like automation, data generation, and simulations of various environments. By conducting extensive studies on agents, CAMEL-AI.org seeks to uncover critical insights into their behaviors, capabilities, and the potential risks they may pose. The community prioritizes thorough research and seeks to strike a balance between the urgency of findings and the patience required for in-depth exploration, while also welcoming contributions that enhance its infrastructure, refine documentation, and bring innovative research ideas to life. The platform is equipped with a suite of components, including models, tools, memory systems, and prompts, designed to empower agents, and it also facilitates integration with a wide array of external tools and services, thereby expanding its utility and effectiveness in real-world applications. As the community grows, it aims to inspire further advancements in the field of artificial intelligence and collaborative systems.

Effortlessly train intelligent AI agents using your own data, enabling them to understand and respond to users' intentions, activate internal tools, or initiate scripted conversations with forms. Streamline your processes by automating repetitive tasks through the capabilities of LLMs, such as replying to messages, completing forms, and managing data efficiently. Allow AI to handle your documents by creating and drafting new ones, extracting key information, conducting evaluations, or performing analyses, all supported by a variety of ready-to-use templates and examples. Import data through one of our 15 available integrations, process it with the most suitable models, or craft scripted dialogues to ensure you remain focused during critical moments. Develop consistent scripted conversations that maintain uniformity, while easily linking your models for a clear and intuitive visual layout. Build no-code chatbots, AI agents, workflows, or automation solutions with remarkable ease, and seamlessly incorporate GPT-4 along with numerous other powerful third-party and open-source models. This holistic approach not only enhances efficiency but also empowers you to tailor the solutions to meet your specific needs.

Sim serves as an open-source platform that facilitates the creation, testing, and deployment of AI agent workflows through an intuitive visual interface that allows users to drag and drop components, thus streamlining complex automation tasks. By offering a canvas-like workspace akin to traditional design software, it enables individuals to link various modular elements that represent AI agents, APIs, and other integrations, allowing for the development of intricate multi-step workflows without the necessity of extensive coding knowledge. Users can connect to a diverse range of over 100 models and applications, which allows them to incorporate various tools such as email services, databases, customer relationship management systems, and calendars into cohesive, automated workflows capable of executing genuine business operations. In addition, Sim.ai empowers users to create and define agents, track their collaborative processes step by step, and observe outputs in real-time, which simplifies the tasks of debugging, refining, and personalizing workflows. The platform also accommodates a variety of triggers, including APIs, chat interfaces, scheduled tasks, and webhooks, which supports versatile deployment in multiple settings, ensuring that users can adapt their automated solutions to meet varying needs. This flexibility and ease of use make Sim an appealing choice for individuals and businesses aiming to harness the power of AI without the steep learning curve typically associated with automation technologies.

The Microsoft Agent Framework is an open-source software development kit and runtime that assists developers in creating, orchestrating, and deploying AI agents alongside multi-agent workflows, utilizing programming languages like .NET and Python. By merging the straightforward agent abstractions found in AutoGen with the sophisticated capabilities of Semantic Kernel, it offers features such as session-based state management, type safety, middleware, telemetry, and extensive model and embedding support, thus providing a cohesive platform suitable for both experimentation and production settings. Additionally, it features graph-based workflows that empower developers with precise control over the interactions among multiple agents, enabling them to execute tasks and coordinate intricate processes efficiently, which facilitates structured orchestration in various scenarios, including sequential, concurrent, or branching workflows. Furthermore, the framework accommodates long-running operations and human-in-the-loop workflows by implementing robust state management, enabling agents to retain context, tackle complex multi-step problems, and function continuously over extended periods. This combination of features not only streamlines development but also enhances the overall performance and reliability of AI-driven applications.

Latitude is a comprehensive platform for prompt engineering, helping product teams design, test, and optimize AI prompts for large language models (LLMs). It provides a suite of tools for importing, refining, and evaluating prompts using real-time data and synthetic datasets. The platform integrates with production environments to allow seamless deployment of new prompts, with advanced features like automatic prompt refinement and dataset management. Latitude’s ability to handle evaluations and provide observability makes it a key tool for organizations seeking to improve AI performance and operational efficiency.

Airweave is a versatile open-source platform that converts application data into knowledge suitable for AI agents, facilitating semantic searches across multiple applications, databases, and document repositories. By providing no-code solutions, instant synchronization of data, and scalable deployment options, it greatly simplifies the creation of intelligent agents. Users can effortlessly link their data sources through OAuth2, API keys, or database credentials and begin data synchronization with minimal setup, granting agents a unified search endpoint to retrieve essential information. With support for more than 100 connectors, including popular services like Google Drive, Slack, Notion, Jira, GitHub, and Salesforce, agents can tap into a diverse array of data sources. The platform manages the complete data pipeline, covering everything from authentication and extraction to embedding and serving, and automates various tasks such as data ingestion, enrichment, mapping, and synchronization to vector stores and graph databases. Additionally, this comprehensive approach allows users to focus on building innovative solutions rather than getting bogged down by technical details.

Dify serves as an open-source platform aimed at enhancing the efficiency of developing and managing generative AI applications. It includes a wide array of tools, such as a user-friendly orchestration studio for designing visual workflows, a Prompt IDE for testing and refining prompts, and advanced LLMOps features for the oversight and enhancement of large language models. With support for integration with multiple LLMs, including OpenAI's GPT series and open-source solutions like Llama, Dify offers developers the versatility to choose models that align with their specific requirements. Furthermore, its Backend-as-a-Service (BaaS) capabilities allow for the effortless integration of AI features into existing enterprise infrastructures, promoting the development of AI-driven chatbots, tools for document summarization, and virtual assistants. This combination of tools and features positions Dify as a robust solution for enterprises looking to leverage generative AI technologies effectively.

Flowise is an open-source agentic development platform designed to help teams build AI agents and LLM-powered applications using a visual workflow interface. The platform allows users to design intelligent workflows through modular components that can be combined to create chatbots, automation systems, and autonomous AI agents. Developers can build both single-agent chat assistants and multi-agent systems that collaborate to complete complex tasks. Flowise integrates with more than 100 large language models, embedding models, and vector databases, providing flexibility in selecting AI technologies. The platform also supports retrieval-augmented generation (RAG), enabling applications to retrieve knowledge from documents and data sources. Built-in features such as human-in-the-loop workflows allow users to review and validate agent actions before execution. Observability tools provide detailed execution traces and compatibility with monitoring systems like Prometheus and OpenTelemetry. Developers can integrate Flowise with existing applications using APIs, SDKs, or embedded chat widgets. The platform supports both cloud and on-premises deployment environments for enterprise scalability. By providing visual tools and flexible integrations, Flowise accelerates the development and deployment of advanced AI-driven applications.

AG2 is an open-source AgentOS that enables the rapid development of production-ready AI agents and multi-agent systems in a matter of minutes rather than months. Previously known as AutoGen, it offers a Python framework for constructing, managing, and scaling AI agents that can effectively collaborate through a shared context while utilizing tools, executing workflows, and accommodating both autonomous and human-in-the-loop processes. This platform is specifically tailored for developers focused on creating systems rather than just prompts, featuring user-friendly syntax, integrated conversation patterns, and a versatile infrastructure for multi-agent automation. In AG2, agents can enhance their functionalities through various tools, enabling them to connect with external systems, retrieve real-time information, run code, conduct web searches, process documents, and tackle intricate tasks that exceed a model's inherent knowledge. The framework is compatible with a wide range of large language model (LLM) providers and local models, such as OpenAI-compatible endpoints, Anthropic Claude, Gemini via Vertex AI, DeepSeek, and LM Studio, making it a flexible choice for developers. By streamlining the development process, AG2 significantly accelerates the innovation of AI solutions across various applications.

PdfCraft is a versatile and free open-source application designed for managing and viewing PDF documents across macOS, Windows, and Linux platforms, along with a self-hostable web version. Users can engage with a wide array of features such as viewing PDFs, searching and selecting text, organizing pages, rotating, deleting, inserting, merging, and splitting files, as well as adding comments, forms, bookmarks, layers, and attachments, all while offering encryption and editing of document properties. The application is optimized for both local and offline use, and it comes equipped with a command-line tool along with an optional Model Context Protocol server, enabling automation and scripting capabilities for users. It caters to both individual users and organizations that require tools for inspecting, organizing, securing, or automating their PDF documents without dependence on cloud-based solutions. PdfCraft is released under either the MIT or Apache-2.0 licenses, ensuring flexibility and freedom for its users. This makes it an attractive choice for those who prioritize control over their document handling processes.

An open-source programming framework designed for agent-based AI is available in the form of AutoGen. This framework presents a multi-agent conversational system that serves as a user-friendly abstraction layer, enabling the efficient creation of workflows involving large language models. AutoGen encompasses a diverse array of functional systems that cater to numerous applications across different fields and levels of complexity. Furthermore, it enhances the performance of inference APIs for large language models, offering opportunities to optimize efficiency and minimize expenses. By leveraging this framework, developers can streamline their projects while exploring innovative solutions in AI.

DemoGPT is an open-source platform designed to facilitate the development of LLM (Large Language Model) agents by providing a comprehensive toolkit. It includes a variety of tools, frameworks, prompts, and models that enable swift agent creation. The platform can automatically generate LangChain code, which is useful for building interactive applications using Streamlit. DemoGPT converts user commands into operational applications through a series of steps: planning, task formulation, and code creation. This platform promotes an efficient method for constructing AI-driven agents, creating an accessible environment for establishing advanced, production-ready solutions utilizing GPT-3.5-turbo. Furthermore, upcoming updates will enhance its capabilities by incorporating API usage and enabling interactions with external APIs, which will broaden the scope of what developers can achieve. As a result, DemoGPT empowers users to innovate and streamline the development process in the realm of AI applications.

Activepieces is an intuitive, open-source automation platform that enables teams to build powerful AI-driven workflows without any coding. With 280+ pre-built automation pieces (MCPs), users can easily integrate various applications, streamline repetitive tasks, and automate business processes. The platform offers no-code tools for creating chat interfaces, automating approvals, and generating AI-powered agents. Whether for small businesses or large corporations, Activepieces supports decentralized innovation and seamless collaboration, empowering teams to automate daily operations, improve productivity, and unlock the full potential of AI in their workflows.

Open-source solutions for Kubernetes enable efficient workflow management, cluster administration, and effective GitOps practices. These Kubernetes-native workflow engines allow for the implementation of both Directed Acyclic Graph (DAG) and step-based workflows, promoting a declarative approach to continuous delivery alongside a comprehensive user interface. They simplify advanced deployment strategies, such as Canary and Blue-Green, to streamline the process. Argo Workflows stands out as an open-source, container-native engine specifically designed for orchestrating parallel jobs within Kubernetes environments, implemented as a Custom Resource Definition (CRD). Users can design complex, multi-step workflows by arranging tasks sequentially or representing their dependencies through a graphical model. This capability enables the execution of demanding computational tasks, such as machine learning or data processing, significantly faster when utilizing Argo Workflows on Kubernetes. Moreover, CI/CD pipelines can be executed natively on Kubernetes, eliminating the need for complicated configurations typically associated with traditional software development tools. Built specifically for container environments, these tools avoid the burdens and constraints that come with legacy virtual machine and server-based systems, paving the way for more efficient operational practices. This makes Argo Workflows an essential component for modern cloud-native development strategies.

The Teradata Enterprise AgentStack is a comprehensive platform designed for the development, deployment, and management of enterprise-level autonomous AI agents that seamlessly connect to reliable data and analytics, aiding businesses in transitioning from experimentation phases to fully operational agentic AI with robust enterprise control. This platform consolidates diverse functionalities to facilitate the entire agent lifecycle; AgentBuilder streamlines the process of creating intelligent agents through both no-code and pro-code tools that are compatible with Teradata Vantage and various open-source frameworks. Furthermore, the Enterprise MCP provides secure, context-rich access to well-governed enterprise data along with tailored prompts that enhance agent intelligence. Meanwhile, AgentEngine ensures scalable agent execution while maintaining consistent memory and reliability across various hybrid environments. Additionally, AgentOps plays a crucial role in centralizing the monitoring, governance, compliance, auditability, and policy enforcement, ensuring that the agents operate within established parameters, which ultimately leads to increased efficiency and adherence to organizational standards. Collectively, these features empower organizations to harness the full potential of autonomous AI in a controlled and efficient manner.

Seclai serves as a comprehensive cloud solution tailored for developers and teams engaged in creating AI applications utilizing their proprietary content. It features an intuitive visual workflow builder that integrates knowledge bases capable of syncing from various sources such as websites, feeds, files, cloud storage, email, and custom APIs. Users have the ability to design intricate multi-step agents and assistants equipped with retrieval capabilities, memory functions, and triggers that can be event-driven or scheduled, all of which can be deployed through a web interface, REST API, SDKs, or an MCP server. The platform also offers access to a variety of models from different providers, along with functionalities for tracking run traces, monitoring usage and costs, evaluating outputs, scanning for prompt injections, and enforcing policy-based governance. Typical applications include customer support and internal documentation assistants, content monitoring, data extraction, as well as automated tasks scheduled at specific intervals. Additionally, teams have the flexibility to experiment with workflows, manage their model selections, and link agent outputs to external systems via webhooks and compatible MCP servers, making it a versatile choice for diverse AI-driven projects. By streamlining these processes, Seclai significantly enhances the efficiency and effectiveness of AI application 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.

Prime Intellect serves as a comprehensive superintelligence framework, offering a cohesive platform for computation, training, inference, and experimentation for groups aiming to develop, implement, and enhance their models over time. Instead of relying on advancements from frontier models, the stack emphasizes ownership of intelligence, providing users with a singular loop for reinforcement learning environments, extensive training, evaluations, inference, and computing needs. Within the Lab, teams can enable self-improving agents by transforming tasks into reinforcement learning settings and utilizing the Prime CLI for creation, development, evaluation, and deployment. The Environment Hub presents access to an extensive collection of over 2,500 open-source RL environments, while hosted evaluations allow teams to assess model performance across various open-source frameworks without the burden of managing infrastructure. Additionally, Hosted Training facilitates large-scale models tailored for agentic workflows, ensuring managed training processes with complete visibility and control, along with direct assistance from the dedicated applied research team, allowing for a more robust and user-friendly experience in model development. This integrated approach not only streamlines the development process but also fosters innovation and collaboration among teams.

OpenText Aviator Studio serves as a comprehensive platform that caters to both no-code and pro-code users, facilitating the creation, management, integration, deployment, and continuous improvement of enterprise AI agents. Tailored for scalability, robust security, and smart automation, it functions as an AI control hub that enables the development of reliable agents across various OpenText offerings and enterprise settings. Teams benefit from the ability to swiftly design, evaluate, and enhance agents, as well as create intricate workflows, implement logical chains of reasoning, and deploy solutions across multiple platforms. The platform's multi-agent choreography feature empowers organizations to synchronize agents and systems within complex workflows seamlessly. Additionally, tools like a prompt library, reusable micro-capabilities, and standardized interfaces contribute to streamlined development practices and promote code reuse. Aviator Studio also prioritizes enterprise-level governance, offering features such as permissions management, policy enforcement, identity control, audit logging, data isolation, and guardrails to mitigate risks like prompt injection and safeguard sensitive data. Ultimately, this platform provides organizations with the tools needed to harness AI effectively while maintaining a strong focus on security and compliance.

OWL (Optimized Workforce Learning) represents a cutting-edge system tailored for collaborative efforts among multiple agents in the automation of real-world tasks. Developed on the CAMEL-AI platform, OWL seeks to transform the way AI agents interact, leading to enhanced efficiency, natural communication, and greater resilience in task automation across diverse sectors. It stands out for its exceptional performance, achieving the top position among open-source frameworks on the GAIA benchmark with an impressive score of 58.18. Key features of OWL include real-time sharing of information, flexible task management, and seamless integration with a variety of tools and platforms, which collectively empower collaborative AI agents to tackle intricate tasks effectively. This innovative framework not only optimizes workflows but also paves the way for future advancements in AI-driven automation solutions.

OpenAI Presence is an enterprise AI agent deployment product designed to help companies put voice and chat agents into real production workflows. The platform is built for high-value use cases where agents need to answer questions, resolve issues, access company systems, follow policies, take approved actions, and escalate to people when required. Presence starts each deployment around a defined job, such as resolving billing problems, supporting insurance claims, handling customer support, assisting outbound sales, or managing internal IT service requests. Companies control what knowledge the agent receives, which systems it can access, what actions it can take, and when human approval or escalation is required. Presence includes policies, standard operating procedures, guardrails, simulations, evaluation tools, approved actions, and escalation rules to help verify accuracy and performance. Before launch, teams can test agents against common requests, edge cases, higher-risk scenarios, and company-specific policies. After launch, production sessions and escalations show where the agent is working well and where it needs improvement. Codex can investigate those signals and propose updates that teams test, compare against production behavior, and approve before rollout. By combining OpenAI models, enterprise deployment support, workflow-specific controls, evaluations, guardrails, and continuous improvement, OpenAI Presence helps organizations build AI agents they can trust in customer and internal operations.

The NVIDIA Agent Toolkit is an extensive framework and solution stack that facilitates the creation, deployment, and scaling of autonomous AI agents capable of reasoning, planning, and executing intricate tasks within enterprise environments. In contrast to traditional generative AI that reacts to isolated prompts, agentic AI employs advanced reasoning and iterative planning methods to independently tackle multi-step challenges, empowering systems to analyze information, devise strategies, and carry out workflows without the need for constant human oversight. This toolkit encompasses various elements of the NVIDIA AI ecosystem, featuring pretrained models, microservices, and development frameworks, which enable organizations to develop context-aware AI agents that leverage their own data for optimal performance. These agents can effectively process substantial amounts of both structured and unstructured data sourced from enterprise systems, allowing them to understand context and synchronize actions across diverse applications for automating processes in areas such as customer support, software development, analytics, and operational workflows. Additionally, by enhancing collaboration among various business functions, the NVIDIA Agent Toolkit can significantly improve efficiency and decision-making across organizations.

Arsenal serves as an innovative web-based AI agent and workflow platform tailored for founders, operators, creators, agencies, and businesses alike. With its intuitive no-code visual builder, users can easily establish agents by inputting instructions, selecting tools, and integrating memory and knowledge sources, followed by testing and deploying them through various messaging channels, embedded applications, and shareable agent pages. The platform empowers users to design intricate multi-step workflows that incorporate triggers, schedules, approval processes, and clearly defined outputs, facilitating the routing of tasks among various AI model providers. Additionally, Business Spaces within Arsenal streamline the organization of agents, knowledge bases, workflows, and reporting, catering to the needs of teams, client projects, and operations across multiple locations. Furthermore, Arsenal enables users to publish and monetize their agents via marketplace listings, subscription models, credit systems, and payments facilitated by Stripe. Use cases for this versatile platform range from lead intake and customer follow-up to comprehensive customer support, reporting, onboarding, document generation, content workflows, and the creation of internal copilots that leverage business-specific information, showcasing its adaptability across different business functions. Ultimately, Arsenal represents a significant advancement in automating and enhancing operational efficiency for diverse enterprises.

Second Me represents a groundbreaking advancement in open-source AI identity systems, offering entirely private and highly personalized AI agents that authentically embody who you are. Unlike conventional models, it not only acquires your preferences but also grasps your distinct cognitive processes, allowing it to represent you in various scenarios, collaborate with other Second Mes, and generate new opportunities within the burgeoning agent economy. With its innovative Hierarchical Memory Modeling (HMM), which consists of a three-tiered framework, your AI counterpart can swiftly identify patterns and adapt to your evolving needs. The system's Personalized Alignment Architecture (Me-alignment) converts your fragmented data into a cohesive, deeply personalized insight, achieving a remarkable 37% improvement over top retrieval-augmented generation models in terms of user comprehension. Moreover, Second Me operates with a commitment to complete privacy, functioning locally to ensure that you maintain total control over your personal information, sharing it solely when you choose to do so. This unique approach not only enhances user experience but also sets a new standard for trust and agency in the realm of artificial intelligence.

Mistral AI Studio serves as a comprehensive platform for organizations and development teams to create, tailor, deploy, and oversee sophisticated AI agents, models, and workflows, guiding them from initial concepts to full-scale production. This platform includes a variety of reusable components such as agents, tools, connectors, guardrails, datasets, workflows, and evaluation mechanisms, all enhanced by observability and telemetry features that allow users to monitor agent performance, identify root causes, and ensure transparency in AI operations. With capabilities like Agent Runtime for facilitating the repetition and sharing of multi-step AI behaviors, AI Registry for organizing and managing model assets, and Data & Tool Connections that ensure smooth integration with existing enterprise systems, Mistral AI Studio accommodates a wide range of tasks, from refining open-source models to integrating them seamlessly into infrastructure and deploying robust AI solutions at an enterprise level. Furthermore, the platform's modular design promotes flexibility, enabling teams to adapt and scale their AI initiatives as needed.

DronaHQ is a no-code, low-code, and AI-powered platform for building internal business apps, voice workflows, and agentic automation. The platform gives teams multiple ways to build, including prompt-based vibe coding, low-code tools, app builders, and integrations with enterprise systems. DronaHQ Apps is focused on creating production-grade internal tools that include security, permissions, reliability, and enterprise controls from the start. Teams can use it to build dashboards, operational apps, approval tools, admin panels, data apps, and other internal systems that support day-to-day work. DronaHQ Agents extends the platform into agentic workflows by helping organizations create AI agents that connect to company tools and data. These agents can execute operational tasks, automate work across systems, and support teams that need faster, more intelligent process automation. The platform is designed for engineering teams, operations teams, IT teams, and business users that want to build software faster without sacrificing governance. DronaHQ is also positioned for enterprise and regulated environments where security, integration, and reliability matter. By combining app building, AI agents, workflow automation, and enterprise connectivity, DronaHQ helps teams move from idea to working operational software more quickly.

Omnara is an open-source API for building and running AI agents in your product or internal workflows. It handles agent execution and state, so developers can focus on what each agent needs to do instead of building the infrastructure around it. You can use Omnara to add agents to a customer dashboard, create internal Slack agents, or run workflows such as code review. Launch agents, send instructions, and follow their work through a REST API, TypeScript SDK, or CLI. Agents preserve their state and can resume after interruptions. Choose the models, tools, and environments that fit your project. Omnara supports providers such as OpenAI, Anthropic, and OpenRouter, as well as compatible endpoints for models you host yourself. Agents can use managed sandboxes or connect to your own machines. MCP integrations and reusable skills let them work with external tools and data. For teams, Omnara provides project access controls, encrypted secrets, activity history, and approval steps for sensitive tool actions. You can start with Omnara Cloud or self-host the platform in your own environment.

PDFsam Enhanced, our commercial solution, is a powerful and professional PDF editor that can modify, convert and review PDF files. It can also sign, fill out forms, and secure them. Your PDF files remain private on your computer. You don't need to upload them any third-party services. PDFsam Basic is an open-source, free solution for casual users. You can split, merge, combine, extract pages, rotate and rotate PDF files. PDFsam Enhanced is a commercial solution for professionals. You can edit, sign, convert, fill out forms, visually combine pages, reorder them, and much more. Your PDF files can be merged, split, extracted pages, rotated, and mixed. Available for Windows, Mac, and Linux. Open-source and free since 2006. Professional and customizable solution to convert, insert, review and sign PDF files. View and create PDFs in free from over 300 file formats. You can modify the PDF content without having to export it or copy it to another format.

LaunchLemonade empowers businesses to create, implement, and expand personalized AI agents effortlessly, eliminating the need for any coding, thereby enabling these agents to operate around the clock for various functions such as lead generation, marketing initiatives, customer assistance, and internal processes. The platform features an intuitive, no-code interface that allows users to craft AI assistants, link them to various data sources like Notion and Google Drive, integrate with automation platforms such as Zapier, and seamlessly embed these agents into websites or operational workflows. Additionally, users can take advantage of multi-model capabilities, orchestrate workflows involving multiple agents, upload knowledge bases, and achieve smooth integration with their existing technology ecosystems. The service brands itself as the "Shopify or WordPress of AI agent creation," catering specifically to agencies, small to medium-sized businesses, and service-oriented professionals eager to deploy AI-driven assistants without the necessity of hiring technical expertise. This innovative solution not only streamlines the development process but also democratizes access to advanced AI functionalities for a wider audience.

Deep Agents serves as an open-source library and agent framework designed for the creation of LLM-driven agents capable of executing multi-step tasks. This library features an agent loop equipped with functionalities such as planning, persistent memory, file-system access, and the ability to delegate tasks to subagents, while also enabling developers to connect various model providers and tools seamlessly. Aimed at software developers and teams focused on crafting tailored agent applications, it supports diverse workflows including research, programming, and other scenarios necessitating planning and action across multiple steps. Additionally, developers have the flexibility to enhance the framework's capabilities by incorporating custom tools, middleware, and backends, while also integrating with LangGraph and LangSmith for effective orchestration, tracing, and deployment processes. Ultimately, Deep Agents functions as a foundational software development framework, empowering users to create and operate agents within their specific applications and environments, rather than providing a pre-built agent for end-users. This versatility makes it an invaluable asset for those looking to innovate in the realm of agent-based application development.

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.

AgentKit offers an all-in-one collection of tools aimed at simplifying the creation, deployment, and enhancement of AI agents. Central to its offerings is Agent Builder, a visual platform that allows developers to easily create multi-agent workflows using drag-and-drop nodes, implement guardrails, preview executions, and manage different workflow versions. The Connector Registry plays a key role in unifying the oversight of data and tool integrations across various workspaces, ensuring effective governance and access management. Additionally, ChatKit facilitates the seamless integration of interactive chat interfaces, which can be tailored to fit specific branding and user experience requirements, into both web and app settings. To ensure high performance and dependability, AgentKit upgrades its evaluation framework with comprehensive datasets, trace grading, automated optimization of prompts, and compatibility with third-party models. Moreover, it offers reinforcement fine-tuning capabilities, further enhancing the potential of agents and their functionalities. This comprehensive suite makes it easier for developers to create sophisticated AI solutions efficiently.

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.

Hyperagent is an innovative platform that utilizes cloud-based AI agents to transcend conventional chat interfaces by facilitating autonomous, task-oriented agents that can effectively execute real work within an organization. Unlike typical chatbots, it offers robust cloud agents equipped with a complete computing environment, enabling them to browse the internet, analyze information, create content, develop applications, and engage with existing tools and services instantaneously. These agents are adept at managing intricate, multi-step workflows, such as sourcing job candidates, compiling comprehensive executive reports, or orchestrating marketing campaigns, while seamlessly integrating with platforms like Slack, Gmail, and CRM systems to collect and act on relevant data. A standout characteristic of Hyperagent is its capacity to learn and evolve over time by accumulating “skills and memories,” which empowers it to adjust to unique company processes, internal data configurations, and brand guidelines without the need for explicit retraining. By doing so, Hyperagent not only enhances productivity but also ensures that agents become increasingly aligned with the specific needs and preferences of the organization they serve. Ultimately, this transformative approach positions Hyperagent as a valuable asset in driving efficiency and innovation in the workplace.

OpenAI Frontier is an innovative platform designed for enterprises that facilitates the creation, deployment, management, and orchestration of numerous AI agents capable of executing practical tasks within established systems, workflows, and data environments. This unified framework enables organizations to seamlessly integrate AI agents, whether developed by OpenAI or external parties, with their internal tools such as CRM systems, data warehouses, and ticketing applications, ensuring that these agents operate with a shared context, permissions, memory, and oversight to effectively handle business-critical tasks. Frontier aims to transition AI agents from isolated experimental phases into fully operational production environments by offering features such as shared business context, governance controls, streamlined onboarding processes, observability, and secure access boundaries. In doing so, it empowers companies to centralize and expand their intelligent automation capabilities in a manner analogous to how human resources systems manage workforce operations, ultimately enhancing efficiency and productivity across the organization. By leveraging such a comprehensive approach, businesses can ensure that their AI agents are not only effective but also aligned with their strategic objectives.