AI tools for weshop
Related Tools:
WeShop AI
WeShop AI is an AI Creative Studio and Image Editing platform tailored for E-Commerce businesses. It offers advanced AI tools for creating high-quality product and model photos, with features like AI Model Shooting and AI Product Shooting. The platform aims to redefine excellence in E-Commerce imagery by providing users with instant image generation at fractional costs, unlimited AI-generated models, and rich backdrops for product photography. WeShop AI is trusted by over 300,000 users worldwide and caters to a wide range of users, from individual creators to global brands.
Yoast
Yoast is an AI-powered SEO tool designed to help website owners improve their search engine optimization. It offers a range of features such as AI-optimized SEO titles and meta descriptions, content optimization suggestions, automatic redirects, and internal linking recommendations. Yoast provides users with the latest SEO best practices and 24/7 support, making it a valuable tool for businesses, webshops, and bloggers looking to enhance their online visibility and compete in search results.
awesome-ai-painting
This repository, named 'awesome-ai-painting', is a comprehensive collection of resources related to AI painting. It is curated by a user named 秋风, who is an AI painting enthusiast with a background in the AIGC industry. The repository aims to help more people learn AI painting and also documents the user's goal of creating 100 AI products, with current progress at 4/100. The repository includes information on various AI painting products, tutorials, tools, and models, providing a valuable resource for individuals interested in AI painting and related technologies.
Cool-GenAI-Fashion-Papers
Cool-GenAI-Fashion-Papers is a curated list of resources related to GenAI-Fashion, including papers, workshops, companies, and products. It covers a wide range of topics such as fashion design synthesis, outfit recommendation, fashion knowledge extraction, trend analysis, and more. The repository provides valuable insights and resources for researchers, industry professionals, and enthusiasts interested in the intersection of AI and fashion.
AgentSquare
AgentSquare is an official implementation for the paper 'AgentSquare: Automatic LLM Agent Search in Modular Design Space'. It provides code, prompts, and results for automatic LLM agent search. The tool allows users to set up OpenAI API key, install dependencies, and run various tasks such as ALFworld, Webshop, M3Tooleval, and Sciworld. Users can also contribute new modules to the modular design challenge by standardizing LLM agents with recommended I/O interfaces. The tool aims to offer a platform for fully exploiting successful agent designs and consolidating efforts of the LLM agent research community.
OpenManus-RL
OpenManus-RL is an open-source initiative focused on enhancing reasoning and decision-making capabilities of large language models (LLMs) through advanced reinforcement learning (RL)-based agent tuning. The project explores novel algorithmic structures, diverse reasoning paradigms, sophisticated reward strategies, and extensive benchmark environments. It aims to push the boundaries of agent reasoning and tool integration by integrating insights from leading RL tuning frameworks and continuously updating progress in a dynamic, live-streaming fashion.
AgentBench
AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.
AgentGym
AgentGym is a framework designed to help the AI community evaluate and develop generally-capable Large Language Model-based agents. It features diverse interactive environments and tasks with real-time feedback and concurrency. The platform supports 14 environments across various domains like web navigating, text games, house-holding tasks, digital games, and more. AgentGym includes a trajectory set (AgentTraj) and a benchmark suite (AgentEval) to facilitate agent exploration and evaluation. The framework allows for agent self-evolution beyond existing data, showcasing comparable results to state-of-the-art models.
agent-q
Agentq is a tool that utilizes various agentic architectures to complete tasks on the web reliably. It includes a planner-navigator multi-agent architecture, a solo planner-actor agent, an actor-critic multi-agent architecture, and an actor-critic architecture with reinforcement learning and DPO finetuning. The repository also contains an open-source implementation of the research paper 'Agent Q'. Users can set up the tool by installing dependencies, starting Chrome in dev mode, and setting up necessary environment variables. The tool can be run to perform various tasks related to autonomous AI agents.
AgentFly
AgentFly is an extensible framework for building LLM agents with reinforcement learning. It supports multi-turn training by adapting traditional RL methods with token-level masking. It features a decorator-based interface for defining tools and reward functions, enabling seamless extension and ease of use. To support high-throughput training, it implemented asynchronous execution of tool calls and reward computations, and designed a centralized resource management system for scalable environment coordination. A suite of prebuilt tools and environments are provided.
Awesome-Code-LLM
Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)
DecryptPrompt
This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.
LLM-Agent-Survey
Autonomous agents are designed to achieve specific objectives through self-guided instructions. With the emergence and growth of large language models (LLMs), there is a growing trend in utilizing LLMs as fundamental controllers for these autonomous agents. This repository conducts a comprehensive survey study on the construction, application, and evaluation of LLM-based autonomous agents. It explores essential components of AI agents, application domains in natural sciences, social sciences, and engineering, and evaluation strategies. The survey aims to be a resource for researchers and practitioners in this rapidly evolving field.
LLM4IR-Survey
LLM4IR-Survey is a collection of papers related to large language models for information retrieval, organized according to the survey paper 'Large Language Models for Information Retrieval: A Survey'. It covers various aspects such as query rewriting, retrievers, rerankers, readers, search agents, and more, providing insights into the integration of large language models with information retrieval systems.
awesome-tool-llm
This repository focuses on exploring tools that enhance the performance of language models for various tasks. It provides a structured list of literature relevant to tool-augmented language models, covering topics such as tool basics, tool use paradigm, scenarios, advanced methods, and evaluation. The repository includes papers, preprints, and books that discuss the use of tools in conjunction with language models for tasks like reasoning, question answering, mathematical calculations, accessing knowledge, interacting with the world, and handling non-textual modalities.
LLM-Tool-Survey
This repository contains a collection of papers related to tool learning with large language models (LLMs). The papers are organized according to the survey paper 'Tool Learning with Large Language Models: A Survey'. The survey focuses on the benefits and implementation of tool learning with LLMs, covering aspects such as task planning, tool selection, tool calling, response generation, benchmarks, evaluation, challenges, and future directions in the field. It aims to provide a comprehensive understanding of tool learning with LLMs and inspire further exploration in this emerging area.
Awesome-Neuro-Symbolic-Learning-with-LLM
The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.
Awesome-AgenticLLM-RL-Papers
This repository serves as the official source for the survey paper 'The Landscape of Agentic Reinforcement Learning for LLMs: A Survey'. It provides an extensive overview of various algorithms, methods, and frameworks related to Agentic RL, including detailed information on different families of algorithms, their key mechanisms, objectives, and links to relevant papers and resources. The repository covers a wide range of tasks such as Search & Research Agent, Code Agent, Mathematical Agent, GUI Agent, RL in Vision Agents, RL in Embodied Agents, and RL in Multi-Agent Systems. Additionally, it includes information on environments, frameworks, and methods suitable for different tasks related to Agentic RL and LLMs.
AgentsMeetRL
AgentsMeetRL is an awesome list that summarizes open-source repositories for training LLM Agents using reinforcement learning. The criteria for identifying an agent project are multi-turn interactions or tool use. The project is based on code analysis from open-source repositories using GitHub Copilot Agent. The focus is on reinforcement learning frameworks, RL algorithms, rewards, and environments that projects depend on, for everyone's reference on technical choices.
LLM-Agent-Evaluation-Survey
LLM-Agent-Evaluation-Survey is a tool designed to gather feedback and evaluate the performance of AI agents. It provides a user-friendly interface for users to rate and provide comments on the interactions with AI agents. The tool aims to collect valuable insights to improve the AI agents' capabilities and enhance user experience. With LLM-Agent-Evaluation-Survey, users can easily assess the effectiveness and efficiency of AI agents in various scenarios, leading to better decision-making and optimization of AI systems.