# Intelligent Agent

Button is an AI intelligent agent development platform that integrates rich capabilities such as plugins, long-term and short-term memory, and workflows, aiming to help users quickly build and deploy intelligent agents with commercial value. Its openness and flexibility allow users from various industries to find suitable solutions, catering to the different needs of individuals and businesses.

OpenManus is an open-source intelligent agent project aimed at achieving functionalities similar to Manus through open-source means, but without requiring an invitation code. Developed collaboratively by multiple developers, it leverages a powerful language model and a flexible plugin system to quickly implement various complex tasks. The main advantages of OpenManus are that it is open-source, free, and easily extensible, making it suitable for developers and researchers for secondary development and research. The project background stems from a need to improve existing intelligent agent tools, with the goal of creating a completely open and easy-to-use intelligent agent platform.

Agent Network Protocol (ANP) aims to define how intelligent agents connect and communicate. It ensures data security and privacy protection through decentralized identity authentication and end-to-end encrypted communication. Its dynamic protocol negotiation function can automatically organize agent networks for efficient collaboration. ANP aims to break down data silos, allowing AI to access complete contextual information, thereby promoting the arrival of the intelligent agent era. This technology has advantages such as openness, security, and efficiency, and is suitable for various scenarios requiring intelligent agent collaboration.

Doublezero is an AI assistance tool for knowledge workers, designed to automate tedious, repetitive tasks via an intelligent agent. It integrates with various common applications, allowing users to complete tasks autonomously with simple instructions, providing feedback or approvals when needed. The product emphasizes security, transparency, and real-time control, allowing users to monitor agent behavior and make adjustments as required. Currently in beta, it aims to provide efficient automation solutions without requiring coding skills.

Tbox is a large language model-based product designed for Alipay's lifestyle ecosystem, aimed at enabling businesses to quickly build professional-grade intelligent agents and drive business growth. Integrating advanced technologies such as Ant Bailing LLM, Ant Tianjian, and Lingjing Digital Human, Tbox enables enhanced experiences and intelligent decision-making. Applicable to various industries such as public services, government affairs, travel, tourism, and healthcare, Tbox enhances user experience and operational efficiency through intelligent services. Pricing and specific positioning vary according to enterprise needs, providing customized solutions for businesses.

Qwen2.5-VL is the latest flagship visual language model released by the Qwen team, representing a significant advancement in the field of visual language models. It can not only recognize common objects but also analyze complex content in images, such as text, charts, and icons, and supports understanding of long videos and event localization. The model performs exceptionally well in various benchmark tests, particularly excelling in document understanding and visual agent tasks, showcasing strong visual comprehension and reasoning abilities. Its main advantages include efficient multimodal understanding, powerful long video processing capabilities, and flexible tool invocation features, making it suitable for a variety of application scenarios.

Dria-Agent-α is a large language model (LLM) tool interaction framework introduced by Hugging Face. By using Python code to invoke tools, it fully utilizes the reasoning capabilities of LLMs, enabling the model to solve complex problems in a manner closer to human natural language compared to traditional JSON formats. This framework enhances LLM performance in agent scenarios by leveraging Python's popularity and pseudo-code-like syntax. The development of Dria-Agent-α utilized a synthetic data generation tool called Dria, which produces realistic scenarios through a multi-stage pipeline to train the model for complex problem-solving. Currently, two models, Dria-Agent-α-3B and Dria-Agent-α-7B, are available on Hugging Face.

The Genspark Autopilot Agent is an intelligent agent capable of autonomously processing tasks by simulating user operations, allowing it to continue functioning even after the user closes the page. Its core advantage lies in its automation capabilities, which can significantly boost efficiency and minimize repetitive labor, enabling users to focus on more creative and strategic tasks. Developed to meet the demands for automation and efficiency in modern work environments, the Genspark Autopilot Agent is likely to offer different tiers of service to accommodate various user needs.

VectorVein is an AI workflow-driven intelligent agent platform that allows users to create powerful workflows without any coding through a drag-and-drop interface, rapidly generating limitless possibilities for intelligent agents. The platform focuses on enhancing productivity with rich features and strong scalability, making it suitable for a variety of scenarios requiring automation of repetitive workflows.

OmAgent is a sophisticated multimodal intelligent agent system dedicated to leveraging multimodal large language models and other multimodal algorithms to accomplish compelling tasks. It encompasses a lightweight intelligent agent framework, omagent_core, meticulously designed to address multimodal challenges. OmAgent comprises three core components: Video2RAG, DnCLoop, and Rewinder Tool, respectively responsible for long video understanding, complex problem decomposition, and information retrieval.

Nerve is an LLM tool that enables the creation of stateful agents. Users can define and execute complex tasks without writing code. Through dynamically updated system prompts and state preservation across multiple inference steps, agents can plan and execute tasks progressively. Nerve supports any model accessible through ollama, groq, or the OpenAI API, offering high flexibility and efficiency while prioritizing memory safety.

Tencent Yuanqi is an intelligent agent open platform that allows developers to quickly and easily create high-quality intelligent agents through plugins, knowledge bases, workflows, etc. Its main advantages include flexibility, ease of use, and efficiency, aiming to provide developers with a one-stop solution for intelligent agent development and publishing.

Sierra is a conversational AI platform designed to elevate customer experience. It allows you to create customizable intelligent chatbots that provide personalized support and solutions tailored to your business. Sierra prioritizes security, compliance, and trustworthiness.

UFO is a UI-focused dual agent framework for interacting with the Windows operating system. It executes user requests by understanding natural language and seamlessly navigating and operating within one or multiple applications. The framework consists of two agents: AppAgent and ActAgent. AppAgent is responsible for selecting applications based on user requests. ActAgent is responsible for iteratively executing operations within the selected application until the task is successfully completed. Both agents utilize the multi-modal capabilities of GPT-Vision to understand the application UI and fulfill user requests.

LLaMA Pro is a model designed for large-scale natural language processing. By leveraging the extended Transformer modules, the model can effectively utilize new data sources to enhance its knowledge without forgetting previous information. LLaMA Pro demonstrates outstanding performance across general tasks, programming, and mathematics. It is a general-purpose model initialized based on LLaMA2-7B. Both LLaMA Pro and its instruction-tuned counterpart (LLaMA Pro-Instruct) achieve state-of-the-art performance on various benchmark tests, showcasing its immense potential for reasoning and handling diverse tasks within intelligent agents. This model provides valuable insights into integrating natural and programming languages, laying a solid foundation for the development of advanced language agents capable of operating effectively in various environments.

LARP is a language intelligent agent framework designed for role-playing in open world games. It includes cognitive architecture, environmental interaction, and character shaping modules, which allow for the creation of unique characters with distinct backgrounds and personalities, enhancing user interaction with intelligent agents. LARP facilitates flexible problem-solving through refined interaction and coherent long-term memory, helping language models adapt to the complexities of open worlds.

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