Best AI tools for< Train Agent >

MindPal is an AI platform that enables users to design, deploy, and manage custom AI agents and multi-agent workflows to automate tasks and boost productivity in the workplace. Users can create specialized AI agents for various tasks, provide instructions and context, train agents with different types of data, and connect multiple agents to collaborate on complex workflows. MindPal offers a user-friendly interface to streamline the process of building AI workflows and provides a range of AI models to choose from.

ResolveAI is a platform that allows users to create and train AI agents for customer service. These agents can be used to automate tasks such as answering FAQs, scheduling meetings, and collecting leads. ResolveAI's agents are trained using a variety of sources, including documents, website pages, and live data sources. Once trained, agents can be customized to fit the user's brand and integrated with a variety of platforms, including websites, social media, and messaging apps.

echowin is an AI Voice Agent Builder Platform that enables businesses to create AI agents for calls, chat, and Discord. It offers a comprehensive solution for automating customer support with features like Agentic AI logic and reasoning, support for over 30 languages, parallel call answering, and 24/7 availability. The platform allows users to build, train, test, and deploy AI agents quickly and efficiently, without compromising on capabilities or scalability. With a focus on simplicity and effectiveness, echowin empowers businesses to enhance customer interactions and streamline operations through cutting-edge AI technology.

IntegraBot is an advanced AI platform that allows users to develop AI chatbots without coding. Users can choose from different AI models, integrate tools and APIs, and train their agents with company data. The platform offers features like creating custom tools, importing data, and integrating with various applications. IntegraBot ensures data security, compliance with regulations, and provides best practices for AI usage.

Crisp is an all-in-one AI-powered business messaging platform that centralizes teams, conversations, data, and knowledge in one place. It offers features like centralizing inbound messages, automations, CRM integration, AI agent training, knowledge base creation, website chat widget, proactive campaigns, and more. Crisp aims to streamline customer support, marketing, and sales processes by leveraging artificial intelligence and automation.

Simple Phones is an AI-powered platform that offers customizable AI voice agents to handle inbound and outbound calls for businesses. The platform allows users to create and train AI agents to answer calls, book appointments, respond to FAQs, and more. With transparent call logging, affordable pricing plans, and extensive customization options, Simple Phones aims to provide a high-quality customer experience and streamline communication processes for businesses of all sizes.

Orimon AI is a conversational marketing tool that helps businesses engage with their customers through AI-powered chatbots. The platform offers tips and tricks to convert visitors into paying customers, train AI agents, and customize chatbot personalities. With a focus on conversational marketing, Orimon AI aims to enhance customer experience and boost sales for service, e-commerce, and SaaS businesses.

Knowmax is an omnichannel knowledge management platform that helps businesses improve customer experience (CX) by providing AI-powered knowledge management capabilities. It offers a range of features such as a Google-like search engine for accessing relevant knowledge across touchpoints, no-code cognitive decision trees for creating simple and mistake-proof customer service actions, visual how-to guides for minimizing repetitive explanations, and an omnichannel-ready knowledge base for creating self-help guides. Knowmax also integrates with CRM systems to deliver faster and personalized resolutions at scale. It is used by businesses in various industries, including telecom, banking, BPO, insurance, e-commerce, media & ISP, healthcare, travel, automobiles, and utilities.

Quidget is a no-code AI Agent platform designed to build, train, and deploy AI assistants for websites, apps, or as standalone chatbots. It offers features such as answering customer support questions, engaging leads for sales, automating bookings, orders, and inquiries, as well as assisting with HR, finance, and operations. Quidget AI Agents are trained virtual assistants that go beyond basic chatbots by understanding, learning, and intelligently assisting customers. The platform allows customization of AI behavior, deployment on multiple channels, and integration with various tools. Quidget supports 45+ languages and ensures data security with end-to-end encryption and GDPR compliance.

Kong.ai is an AI-powered platform offering Conversational Chatbots and AI Agents to automate and streamline various business operations such as customer support, sales, HR, and marketing workflows. The platform leverages state-of-the-art language models and machine learning to provide natural and intelligent conversations. Kong.ai provides specialized AI Agents for tasks like lead generation, social media management, recruitment, and more, helping businesses enhance efficiency and productivity.

AGENT is an AI chatbot powered by OpenAI and Anthropic, designed to handle customer inquiries, qualify leads, and enhance customer engagement. Developed by I Need Leads Ltd, AGENT combines advanced natural language processing, intelligent chatbots, and machine learning to empower businesses of all sizes in generating and managing leads efficiently. The platform adapts, learns, and grows with businesses, ensuring they stay ahead in the competitive digital landscape.

CoRover.ai is an AI-powered chatbot designed to help users book train tickets seamlessly through conversation. The chatbot, named AskDISHA, is integrated with the IRCTC platform, allowing users to inquire about train schedules, ticket availability, and make bookings effortlessly. CoRover.ai leverages artificial intelligence to provide personalized assistance and streamline the ticket booking process for users, enhancing their overall experience.

Kaiden AI is an AI-powered training platform that offers personalized, immersive simulations to enhance skills and performance across various industries and roles. It provides feedback-rich scenarios, voice-enabled interactions, and detailed performance insights. Users can create custom training scenarios, engage with AI personas, and receive real-time feedback to improve communication skills. Kaiden AI aims to revolutionize training solutions by combining AI technology with real-world practice.

SuperAGI is a leading research organization focused on Generalized Super Intelligence. They work on research in technical areas such as Neurosymbolic AI, Autonomous Agents & Multi-Agent Systems, New Model Architectures, System 2 Thinking, Recursive Self-Improving Systems, and other socio-economic super AGI-related topics such as Digital Workforce, Algorithmic Governance, UBI, etc.

Azara.AI is an AI tool that automates workflows, saving hours and enabling fast scaling. It allows users to handle tasks with the help of AI-generated workflows and actions. The tool offers no-code automations with secure, encrypted, and compliant features. Users can easily build automations from private company data, enhancing productivity and efficiency. Azara.AI simplifies the process of building, deploying, and managing AI agents without requiring technical expertise.

SupBot is an AI-powered support solution that enables businesses to handle customer support instantly. It offers the ability to train and deploy AI support bots with ease, allowing for seamless integration into any website. With a user-friendly interface, SupBot simplifies the process of setting up and customizing support bots to meet specific requirements. The platform is designed to enhance customer service efficiency and streamline communication processes through the use of AI technology.

BrainyBear is an AI tool that allows users to easily build and train AI chatbots and intelligent assistants. It offers a quick and accurate solution to customer queries by scanning websites or uploaded files. With BrainyBear, users can create, customize, and embed AI chatbots in just a few steps, without the need for complex training or setups. The tool leverages GPT-based AI technology to provide human-like interactions, multilingual support, and seamless integrations with popular messaging platforms.

Threado AI is an advanced AI application designed for customer service teams to provide instant, automated support to customers and internal teams. It offers AI agents trained on internal knowledge, personalized customer engagement at scale, and AI-powered solutions to accelerate sales processes. Threado AI integrates effortlessly into daily tools like Slack, Microsoft Teams, and Chrome, providing instant access to company information. The application is known for its efficiency, accuracy, and security in handling customer queries and improving customer relations.

Chunky is an AI chatbot builder that allows users to create human-like chatbots effortlessly. With Chunky, users can automate customer support, answer frequently asked questions, and save time by setting up their own AI-powered bot in just a few minutes. The platform offers a user-friendly experience with no coding required, integrated chatbot deployment on websites, and customization options for branding. Chunky utilizes the ChatGPT API and Embeddings provided by OpenAI to deliver smart chatbot interactions in close to 95 languages.

RoboResponseAI is a proactive AI chatbot customized for businesses, designed to initiate conversations, take feedback, and drive lead conversions. It offers features such as lead conversion guidance, recruitment assistance, quick chatbot training, interactive ad campaigns, and seamless integration with various platforms. The application aims to enhance customer engagement, streamline support processes, and improve lead generation for businesses of all types.

This repository contains code for building cooperative embodied agents modularly with large language models. The agents are trained to perform tasks in two different environments: ThreeDWorld Multi-Agent Transport (TDW-MAT) and Communicative Watch-And-Help (C-WAH). TDW-MAT is a multi-agent environment where agents must transport objects to a goal position using containers. C-WAH is an extension of the Watch-And-Help challenge, which enables agents to send messages to each other. The code in this repository can be used to train agents to perform tasks in both of these environments.

Godot RL Agents is an open-source package that facilitates the integration of Machine Learning algorithms with games created in the Godot Engine. It provides interfaces for popular RL frameworks, support for memory-based agents, 2D and 3D games, AI sensors, and is licensed under MIT. Users can train agents in the Godot editor, create custom environments, export trained agents in ONNX format, and utilize advanced features like different RL training frameworks.

YGO Agent is a project focused on using deep learning to master the Yu-Gi-Oh! trading card game. It utilizes reinforcement learning and large language models to develop advanced AI agents that aim to surpass human expert play. The project provides a platform for researchers and players to explore AI in complex, strategic game environments.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework enables LLMs to reason and interact with the environment, optimizing entire trajectories for long-horizon reasoning while maintaining computational efficiency.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework consists of MDP formulation, RICO algorithm with rollout and update stages, and reward normalization strategies to stabilize training. RAGEN aims to optimize reasoning and action strategies for LLM agents operating in complex environments.

FinRL-DeepSeek is a project focusing on LLM-infused risk-sensitive reinforcement learning for trading agents. It provides a framework for training and evaluating trading agents in different market conditions using deep reinforcement learning techniques. The project integrates sentiment analysis and risk assessment to enhance trading strategies in both bull and bear markets. Users can preprocess financial news data, add LLM signals, and train agent-ready datasets for PPO and CPPO algorithms. The project offers specific training and evaluation environments for different agent configurations, along with detailed instructions for installation and usage.

This repository contains code for grounding large language models' knowledge in BabyAI-Text using the GLAM method. It includes the BabyAI-Text environment, code for experiments, and training agents. The repository is structured with folders for the environment, experiments, agents, configurations, SLURM scripts, and training scripts. Installation steps involve creating a conda environment, installing PyTorch, required packages, BabyAI-Text, and Lamorel. The launch process involves using Lamorel with configs and training scripts. Users can train a language model and evaluate performance on test episodes using provided scripts and config entries.

Habitat-Lab is a modular high-level library for end-to-end development in embodied AI. It is designed to train agents to perform a wide variety of embodied AI tasks in indoor environments, as well as develop agents that can interact with humans in performing these tasks.

MineStudio is a simple and efficient Minecraft development kit for AI research. It contains tools and APIs for developing Minecraft AI agents, including a customizable simulator, trajectory data structure, policy models, offline and online training pipelines, inference framework, and benchmarking automation. The repository is under development and welcomes contributions and suggestions.

Craftium is an open-source platform based on the Minetest voxel game engine and the Gymnasium and PettingZoo APIs, designed for creating fast, rich, and diverse single and multi-agent environments. It allows for connecting to Craftium's Python process, executing actions as keyboard and mouse controls, extending the Lua API for creating RL environments and tasks, and supporting client/server synchronization for slow agents. Craftium is fully extensible, extensively documented, modern RL API compatible, fully open source, and eliminates the need for Java. It offers a variety of environments for research and development in reinforcement learning.

AdaSociety is a multi-agent environment designed for simulating social structures and decision-making processes. It offers built-in resources, events, and player interactions. Users can customize the environment through JSON configuration or custom Python code. The environment supports training agents using RLlib and LLM frameworks. It provides a platform for studying multi-agent systems and social dynamics.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

Agents 2.0 is a framework for training language agents using symbolic learning, inspired by connectionist learning for neural nets. It implements main components of connectionist learning like back-propagation and gradient-based weight update in the context of agent training using language-based loss, gradients, and weights. The framework supports optimizing multi-agent systems and allows multiple agents to take actions in one node.

Foyle is a project focused on building agents to assist software developers in deploying and operating software. It aims to improve agent performance by collecting human feedback on agent suggestions and human examples of reasoning traces. Foyle utilizes a literate environment using vscode notebooks to interact with infrastructure, capturing prompts, AI-provided answers, and user corrections. The goal is to continuously retrain AI to enhance performance. Additionally, Foyle emphasizes the importance of reasoning traces for training agents to work with internal systems, providing a self-documenting process for operations and troubleshooting.

Proactive Agent is a project aimed at constructing a fully active agent that can anticipate user's requirements and offer assistance without explicit requests. It includes a data collection and generation pipeline, automatic evaluator, and training agent. The project provides datasets, evaluation scripts, and prompts to finetune LLM for proactive agent. Features include environment sensing, assistance annotation, dynamic data generation, and construction pipeline with a high F1 score on the test set. The project is intended for coding, writing, and daily life scenarios, distributed under Apache License 2.0.

AI4U is a tool that provides a framework for modeling virtual reality and game environments. It offers an alternative approach to modeling Non-Player Characters (NPCs) in Godot Game Engine. AI4U defines an agent living in an environment and interacting with it through sensors and actuators. Sensors provide data to the agent's brain, while actuators send actions from the agent to the environment. The brain processes the sensor data and makes decisions (selects an action by time). AI4U can also be used in other situations, such as modeling environments for artificial intelligence experiments.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with discrete action spaces. The algorithm aims to improve sample efficiency by using two uncorrelated Q-Networks to prevent overestimation of Q-values. By updating parameters periodically, the model reduces computation time and enhances training performance. The tool is based on the Double DQN method proposed by Hasselt in 2010.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

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