AI Agent Frameworks Benchmarks Types Examples and Marketplace Review A Comprehensive List

Introduction

In this blog, we will introduce popular AI Agent Frameworks, Benchmarks (keep updated and beyond) Types and provide you some examples with Project Name, Project Website and its application and industries. The resources are collected from AI and ML websites and communities (github, huggingface, paper arxiv,etc) and the comprehensive will keep updating. You can also visit AI Agent Search to find the best resources AI Agents from various industries and applications. For AI Agent Frameworks, we will cover some popular AI agent frameworks, including LangChain, AutoGen, Crew AI etc. And for various types of AI agents, since it's very broad concepts, we will mainly cover the AI agents classified by Autonomous Ability (Auto AI Agents or Rule based) and by industries perspective. For AI Agent Benchmarks, this blog is usefully for AI and ML practitioners and beginners who want to understand what are AI Agents Benchmarks or Environments, the key capability why there are important and how the applications of these AI Agent benchmarks. We will cover different categories of AI Agent Environments, including Game-Based Environments, Text Chat-Based Environments, Physics and Robotics Simulations, Multi-Agent Platforms. Additionally, we can cover AI-Agents in various domains, such as the benchmarks and environments of AI Agents in Healthcare, AI Agents in Finance, AI Agents in Law, AI Agents in Education, etc. To find best AI Agent and Apps Search Engine and Navigation, please visit AI Agent Search.

To find best AI Agent and Apps Search Engine Marketplace and Navigation, please visit AI Agent Search

Table of Contents

• 1. AI Agent Frameworks
• LangChain
• AutoGen
• Magentic One
• 2. AI Agent Benchmarks By Application
• Game-Based Environments
• Physics Robotics and Embodied AI
• Text-Based Environments
• Social and Multi-Agent
• Autonomous Driving Vehicles Environment
• Tool Use Agents
• 3. AI Agent Types By Industries
• AI Agents in Healthcare
• AI Agents in Finance
• AI Agents in Law

Key Concepts of AI Agents

What are AI Agents Benchmarks

AI Agent Benchmarks refers to the common frameworks or environments for AI Agents to interact with, which can help evaluate and compare the performance of various AI models, algorithms, AI systems, etc. The AI Agent benchmarks cover very broad categories of environments, including Web-based GUI, Games, Physical World Simulators, Computer Laptops, Cellphones, etc and not limited to the ones mentioned above. For exmaple, with the rapid development of Large Language Models (LLM), a lot of Chatbot based agent benchmarks and frameworks are proposed to compare various models, including GPT-3.5, GPT-4o, GPT-4V, Claude Sonnet, Gemini, etc.

What are Tasks in AI agent

Tasks of AI Agents are scenarios from an environment which the AI agents try to solve. For exmaple, in the OpenAI Gym environment, the task may refers to a Atari, Go or Chess game. In the more recent, computer use environments, such as ANDROIDWORLD, AndroidLab, the tasks may refer to click, move, type on the UI of android cellphones, etc.

What are Tools in AI agent

Tools in AI Agent refers to functions that develops provide to LLM to decide which one to use to accomplish a task. A typical workflow is like. You want to get realtime weather data for New York City. And you prepare a python function "get_weather(city:str)" so that LLM can choose. When user asked a question "What's the weather like in New York?", the LLM will take the prompt and tools as input, and output a function call results as tools=get_weather and parameters {"city":"New York"}. When you get the parameters and executionable function, you can execetute the functions on your side and complete the tasks.

2. List of AI Agent Resources

AI Agent Frameworks

• LangChain

  LangChain is one of the most popular AI Agents frameworks, which helps developers to developing AI applications powered by large language models (LLMs). It simplifies the stage of the LLM application lifecycle from Development, Productionization and Deployment. For production deployment, it also provides APIs and Assistants with LangGraph Cloud.

  examples website website

• AutoGen

  AutoGen is developped by Microsoft, which is a unified multi-agent conversation framework using foundation LLM models. It features capable, customizable and conversable agents which integrate LLMs, tools, and humans via automated agent chat. Additionally, AutoGen helps facilitating cooperation among multiple agents to solve tasks. AutoGen aims to provide an easy-to-use and flexible framework for accelerating development and research on agentic AI, like PyTorch for Deep Learning. It offers features such as agents that can converse with other agents, LLM and tool use support, autonomous and human-in-the-loop workflows, and multi-agent conversation patterns.

  website website

• Magentic One

  Magentic-One is a high-performing generalist agentic system designed to solve complicated tasks. It employs a multi-agent architecture where a lead agent, the Orchestrator, directs four other agents to solve tasks. The Orchestrator plans, tracks progress, and re-plans to recover from errors, while directing specialized agents to perform tasks like operating a web browser, navigating local files, or writing and executing Python code.

  website paper

AI Agent Benchmarks and Environment By Application

Game-Based Environments

• OpenAI Gym

  OpenAI Gym is A standard toolkit for developing and comparing reinforcement learning (RL) algorithms. It offers a variety of tasks like Atari games and control problems.

  paper github

• Unity ML-Agents

  Unity Machine Learning Agents Toolkit enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning, leveraging Unity's game engine for complex simulations.

  website github

• Minecraft Project Malmo

  Minecraft is a computer game ideal for artificial intelligence research, it is addictively appealing to the millions of fans who enter its virtual world every day. It offers its users endless possibilities, ranging from simple tasks, like walking around looking for treasure, to complex ones, like building a structure with a group of teammates.

  Micsofot Malmo platform is a sophisticated AI experimentation platform built on top of Minecraft, and designed to support fundamental research in artificial intelligence, which consists of a mod for the Java version, and code that helps artificial intelligence agents sense and act within the Minecraft environment. The two components can run on Windows, Linux, or Mac OS, and researchers can program their agents in any programming language they’re comfortable with.

  website github

• DeepMind Lab

  DeepMind Lab is A 3D environment tailored for training and evaluating reinforcement learning agents, which provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning.

  github paper

Physics Robotics and Embodied AI

• Mujoco

  Offers fast and accurate physics simulation, commonly used for robotic control tasks. MuJoCo means Multi-Joint dynamics with Contact, which is a general purpose physics engine that aims to facilitate research and development in robotics, biomechanics, graphics and animation, machine learning, and other areas which demand fast and accurate simulation of articulated structures interacting with their environment.

  github Website paper

• PyBullet

  Bullet is the official C++ source code repository of the Bullet Physics SDK, which contains real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc. And PyBullet is a Python module for physics simulations, suitable for robotics, machine learning, and more.

  github Website

• Webots

  Webots provides a complete development environment to model, program and simulate robots, vehicles and mechanical systems.

  github Website

• Gazebo

  Gazebo is a simulator with a complete toolbox of development libraries and cloud services to make simulation.

  github Website

• Habitat

  Habitat: A Platform for Embodied AI Research is the original paper published on ICCV. Habitat enables training embodied agents (virtual robots) in highly efficient photorealistic 3D simulation. Specifically, Habitat consists of: (i) Habitat-Sim: a flexible, high-performance 3D simulator with configurable agents, sensors, and generic 3D dataset handling. Habitat-Sim is fast – when rendering a scene from Matterport3D, it achieves several thousand frames per second (fps) running single-threaded, and can reach over 10,000 fps multi-process on a single GPU. (ii) Habitat-API: a modular high-level library for end-to-end development of embodied AI algorithms – defining tasks (e.g. navigation, instruction following, question answering), configuring, training, and benchmarking embodied agents.

  paper github website

Text-Based Environments

• TextWorld

  A framework for learning agents in text-based games and environments. TextWorld is A text-based game generator developed by Microsoft. It provides an open-source, extensible engine that both generates and simulates text games, which are useful to train reinforcement learning (RL) agents to learn skills such as language understanding and grounding, combined with sequential decision making.

  github website

• Jericho

  Jericho is a lightweight python-based interface connecting learning agents with interactive fiction games, which focuses on interactive fiction games, enabling research in natural language understanding.

  github

Social AI Agents and Multi-Agent Environment

Social AI agents are AI agents that can perceive and learn from the behavior of other agents.

• PettingZoo

  PettingZoo is a python interface capable of general multi-agent reinforcement learning (MARL) problems. PettingZoo includes a wide variety of reference environments, helpful utilities, and tools for creating your own custom environments.

  website

• SOTOPIA

  SOTOPIA is the interactive Evaluation for Social Intelligence in Language Agents

  website github

Autonomous Driving Vehicles

• CARLA

  CARLA is an open-source simulator for autonomous driving research, which can support support development, training, and validation of autonomous driving systems. It also provides open digital assets (urban layouts, buildings, vehicles) that were created for this purpose and can be used freely. The simulation platform supports flexible specification of sensor suites and environmental conditions.

  website github

• AirSim

  AirSim is developped by Microsoft Research as a simulation platform for AI research and experimentation. For example, drone delivery is no longer a sci-fi storyline—it’s a business reality, which means there are new needs to be met. We’ve learned a lot in the process, and we want to thank this community for your engagement along the way.

  github

Tool Use Autonomous Agents Environment

• AndroidWorld

  AndroidWorld is a Dynamic Benchmarking Environment for Autonomous Agents. Autonomous agents that execute human tasks by controlling computers can enhance human productivity and application accessibility. AndroidWorld is a fully functional Android environment that provides reward signals for 116 programmatic tasks across 20 real-world Android apps. Unlike existing interactive environments, which provide a static test set, AndroidWorld dynamically constructs tasks that are parameterized and expressed in natural language in unlimited ways, thus enabling testing on a much larger and more realistic suite of tasks.

  paper github

• AndroidLab

  AndroidLab is a systematic Android agent framework, which includes an operation environment with different modalities, action space, and a reproducible benchmark. It supports both large language models (LLMs) and multimodal models (LMMs) in the same action space. AndroidLab benchmark includes predefined Android virtual devices and 138 tasks across nine apps built on these devices. By using the AndroidLab environment, we develop an Android Instruction dataset and train six open-source LLMs and LMMs, lifting the average success rates from 4.59% to 21.50% for LLMs and from 1.93% to 13.28% for LMMs. AndroidLab is open-sourced and publicly available at this https URL.

  paper github

AI Agent Benchmarks By Industry

AI Agents in Healthcare

• AgentClinic

  AgentClinic is a multimodal agent benchmark to evaluate AI in simulated clinical environments. Keywords: clinical scenarios, Benchmark, multimodal

  paper github

• LARGE LANGUAGE MODELS AS AGENTS IN THE CLINIC

  High-fidelity simulations may also be used to evaluate interactions between users and LLMs within a clinical workflow, or to model the dynamic interactions of multiple LLMs. Developing these robust, real-world clinical evaluations will be crucial towards deploying LLM agents into healthcare.

  paper

• AI Hospital

  AI Hospital is a LLM-powered multi-agent framework that simulates real-world dynamic medical interactions. AI Hospital consists of multiple non-player characters (NPCs), including Patient, Examiner, and Chief Physician, as well as the player character, represented by the Doctor. I

  paper github

AI Agents in Finance Benchmarks

• AgentClinic

  AgentClinic is a multimodal agent benchmark to evaluate AI in simulated clinical environments. Keywords: clinical scenarios, Benchmark, multimodal

  paper github

• LARGE LANGUAGE MODELS AS AGENTS IN THE CLINIC

  High-fidelity simulations may also be used to evaluate interactions between users and LLMs within a clinical workflow, or to model the dynamic interactions of multiple LLMs. Developing these robust, real-world clinical evaluations will be crucial towards deploying LLM agents into healthcare.

  paper

• AI Hospital

  AI Hospital is a LLM-powered multi-agent framework that simulates real-world dynamic medical interactions. AI Hospital consists of multiple non-player characters (NPCs), including Patient, Examiner, and Chief Physician, as well as the player character, represented by the Doctor. I

  paper github

AI Agents in Finance Benchmarks

• FinBen

  FinBen is open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks, covering seven critical aspects: information extraction (IE), textual analysis, question answering (QA), text generation, risk management, forecasting, and decision-making. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading.

  paper github

AI Agents in Law Benchmarks

AI Agents in Education Benchmarks