# Agent

QwQ-Max-Preview is the latest in the Qwen series, built on Qwen2.5-Max. It demonstrates enhanced capabilities in mathematics, programming, and general tasks, while also showing strong performance in workflows involving Agents. As a preview release for the upcoming QwQ-Max, this version is undergoing continuous optimization. Key advantages include its robust deep reasoning, mathematical capabilities, programming assistance, and Agent task handling. Future plans include open-sourcing QwQ-Max and Qwen2.5-Max under the Apache 2.0 license to foster innovation across diverse applications.

/dev/agents is a platform dedicated to creating the next-generation AI agent operating system. It aims to empower computers to assist people in their daily software interactions similarly to human assistance, through innovative user interface models, reimagined privacy frameworks, and a developer platform. This platform challenges existing technological boundaries in order to create more powerful and consumer-friendly application experiences.

Vector Context is an AI-driven workflow platform that allows users to create powerful workflows without coding, simply by dragging and dropping. It offers a diverse array of workflow nodes, from generative AI models to multimodal processing, from vector databases to web crawling. The platform supports graphical design, enabling users to quickly generate endless possibilities of intelligent agents, enhance productivity, and achieve automation.

DB-GPT is an open-source AI-native data application development framework that, utilizing AWEL (Agentic Workflow Expression Language) and agent technologies, simplifies the integration of large models with data. Through its capabilities in multi-model management, Text2SQL optimization, RAG framework optimization, and multi-agent framework collaboration, DB-GPT empowers enterprises and developers to build customized applications with less code. In the era of Data 3.0, DB-GPT, based on models and databases, provides foundational data intelligence technologies for building enterprise-level reporting, analysis, and business insights.

Agently is an open-source framework for developing applications with large models, designed to help developers quickly build native AI agent applications based on large language models. It simplifies the interaction process with large language models through a series of tools and interfaces, allowing developers to focus more on implementing business logic. The Agently framework supports multiple models, is easy to install and configure, and has high flexibility and extensibility.

Baidu Smart Cloud Qianfan AppBuilder is a platform specifically designed for efficient development of AI-native applications. It provides a one-stop shop for AI-native application development resources and tools, including ready-to-use application frameworks, rich AI capability components, and cloud resource connections. Key features include: 1. Open-source application frameworks, such as the Knowledge Augmentation Application Framework (RAG) and the Agent framework. 2. Rich AI components, such as speech recognition, TTS, text-to-image generation, and vector engines. 3. Cloud resource connections, providing CPU/GPU computing, storage and other services. This platform lowers the threshold for AI application development and helps businesses efficiently develop AI-native applications.

CrewAI is an open-source library for developers designed to build and coordinate AI agent teams for tackling complex tasks. Built on the LangChain framework, it seamlessly integrates various AI tools to empower your agents to accomplish specific work goals. You can assemble different agents, tasks, and tools like building blocks to create AI agent systems tailored to your needs.

KwaiAgents is a series of open-source intelligent agent works from Kuaishou Technology's KwaiKEG. The open-source contents include: KAgentSys-Lite system: A streamlined version of the KAgentSys system in the paper; KAgentLMs series models: Large language models with agent functions such as planning, reflection, and tool usage; KAgentInstruct: Thousands of refined agent instruction data from the paper; KAgentBench: Over 3,000 human-assessed datasets for testing agents' planning, tool usage, reflection, summarization, and descriptive abilities, etc.

Agent M is a powerful main agent development framework driven by an LLM or ChatGPT, enabling you to create multiple agents based on LLMs. Agent M orchestrates between multiple agents performing various tasks, such as API calls based on natural language, connecting to your data, and helping to automate complex dialogues.

LangChain is a library that helps developers build applications by combining Large Language Models (LLMs) with other computational or knowledge sources. It provides end-to-end examples for various use cases, including question answering, chatbots, and agents. LangChain also offers a universal interface for LLMs, functionalities like chaining calls, data augmentation generation, memory management, and evaluation.

Suspicion-Agent is an implementation of playing imperfect information games using GPT-4 with an understanding of mental theories. It can train and evaluate agents, and provide sample outputs.

AutoAgents is an open-source, LLM-based experimental application for generating automated intelligent agents. Driven by LLMs, it can automatically generate multi-role intelligent agents based on your defined objectives. It can determine the required expert roles and specific execution plans based on the problem. Includes intelligent agent generator, execution plan generator, and result reflection module. Enables LLMs to autonomously allocate different roles based on problems, formulate solutions plans, and execute them, much like humans.