AgentSet allows you to build AI agents that can perform a wide range of tasks, from customer service to workflow automation. Users can define the parameters and functionalities of their agents to fit unique business needs, ensuring they have the perfect tool for their operations. Its intuitive interface is designed for users at all technical levels, making it easy to adapt AI to specific workflows and enhance overall efficiency.

Colosseum Agent Battles provides a modular Python SDK for constructing AI agent competitions in customizable arenas. Users can define environments with specific terrain, resources, and rulesets, then implement agent strategies via a standardized interface. The framework manages battle scheduling, referee logic, and real-time logging of agent actions and outcomes. It includes tools for running tournaments, tracking win/loss statistics, and visualizing agent performance through charts. Developers can integrate with popular machine learning libraries to train agents, export battle data for analysis, and extend referee modules to enforce custom rules. Ultimately, it streamlines the benchmarking of AI strategies in head-to-head contests. It also supports logging in JSON and CSV formats for downstream analytics.

TinyAuton provides a minimal, extensible architecture for building autonomous agents that plan, execute, and refine tasks using OpenAI’s GPT models. It offers built-in modules for defining objectives, managing conversation context, invoking custom tools, and logging agent decisions. Through iterative self-reflection loops, the agent can analyze outcomes, adjust plans, and retry failed steps. Developers can integrate external APIs or local scripts as tools, set up memory or state, and customize the agent’s reasoning pipeline. TinyAuton is optimized for rapid prototyping of AI-driven workflows, from data extraction to code generation, all within a few lines of Python.

Phidata is an innovative platform designed to build, deploy, and monitor AI agents enriched with memory, knowledge, and reasoning capabilities. This system allows users to create agile, responsive agents that can interact with external systems, utilize various data sources, and improve over time through learning. Phidata supports multiple large language models (LLMs), providing users flexibility in their selection. With built-in memory features, agents can maintain personalized conversations, making them ideal for a range of applications in various industries.

RainbowGPT provides a complete framework for designing, customizing, and deploying AI agents powered by OpenAI models. It includes a FastAPI backend, LangChain integration for tool and memory management, and a React-based UI for agent creation and testing. Users can upload documents for vector-based knowledge retrieval, define custom prompts and behaviors, and connect external APIs or functions. The platform logs interactions for analysis and supports multi-agent workflows, enabling complex automation and conversational pipelines.

Self-Determining AI Agents is a Python-based framework designed to simplify the creation of autonomous AI agents. It features a customizable planning loop where agents generate tasks, plan strategies, and execute actions using integrated tools. The framework includes persistent memory modules for context retention, a flexible task scheduling system, and hooks for custom tool integrations such as web APIs or database queries. Developers define agent goals via configuration files or code, and the library handles the iterative decision-making process. It supports logging, performance monitoring, and can be extended with new planning algorithms. Ideal for research, automating workflows, and prototyping intelligent multi-agent systems.

Team of AI Agents provides a modular architecture to build and deploy multi-agent systems. Each agent operates with distinct roles, utilizing a global memory store and local contexts for knowledge retention. The framework supports asynchronous messaging, tool usage via adapters, and dynamic task reassignment based on agent outcomes. Developers configure agents through YAML or Python scripts, enabling topic specialization, goal hierarchy, and priority handling. It includes built-in metrics for performance evaluation and debugging, facilitating rapid iteration. With extensible plugin architecture, users can integrate custom NLP models, databases, or external APIs. Team of AI Agents accelerates complex workflows by leveraging collective intelligence of specialized agents, making it ideal for research, automation, and simulation environments.

Thufir is a Python-based open-source agent framework designed to facilitate the creation of autonomous AI agents capable of complex task planning and execution. At its core, Thufir provides a planning engine that decomposes high-level objectives into actionable steps, a memory module for storing and retrieving contextual information across sessions, and a plug-and-play tool interface allowing agents to interact with external APIs, databases, or code execution environments. Developers can leverage Thufir’s modular components to customize agent behaviors, define custom tools, manage agent state, and orchestrate multi-agent workflows. By abstracting away low-level infrastructure concerns, Thufir accelerates the development and deployment of intelligent agents for use cases like virtual assistants, workflow automation, research, and digital workers.

AAGPT is an extensible, open-source AI agent framework designed for building autonomous agents. It enables you to define high-level objectives, manage conversational memory, plan multi-step tasks, and integrate external tools or APIs. Using a simple configuration file and Python SDK, you can customize agent behavior, define custom actions, and deploy agents that can interact with data sources, execute commands, and learn from past interactions to improve performance over time.

AI-Agents provides a flexible, modular platform for creating autonomous AI-driven agents. Developers can define agent objectives, chain tasks, and incorporate memory modules to store and retrieve contextual information across sessions. The framework supports integration with leading LLMs via API keys, enabling agents to generate, evaluate, and revise outputs. Customizable tool and plugin support allows agents to interact with external services like web scraping, database queries, and reporting tools. Through clear abstractions for planning, execution, and feedback loops, AI-Agents accelerates prototyping and deployment of intelligent automation workflows.

Agents with ADK is an open-source Python framework designed to streamline the creation of intelligent agents powered by large language models. It includes modular agent templates, built-in memory management, tool execution interfaces, and multi-agent coordination capabilities. Developers can quickly plug in custom functions or external APIs, configure planning and reasoning chains, and monitor agent interactions. The framework supports integration with popular LLM providers and provides logging, retry logic, and extensibility for production deployments.

Agent ChatRoom provides a flexible environment to build and run multi-agent conversational systems. Users can create agents with unique personas and prompts, route messages between agents, and view conversation histories in a sleek UI. It integrates with OpenAI APIs, supports custom configuration of agent behaviors, and can be deployed on any static hosting service. Developers benefit from a modular architecture, easy prompt tuning, and a responsive interface for testing AI collaboration scenarios.

AgentLayer is a comprehensive platform that allows users to create bespoke AI agents tailored specifically to their operational needs. It leverages advanced artificial intelligence capabilities to automate workflows, improve customer interactions, and streamline decision-making processes. Users can customize the agents' functionality, integrate with existing tools, and deploy them seamlessly across multiple channels. This enables businesses to optimize their efficiency and enhance user experience through intelligent solutions.

AgentLLM is a web-based AI agent framework that lets users create, configure, and run autonomous agents through a graphical interface or JSON definitions. Agents can plan multi-step workflows by reasoning over tasks, invoke code via Python tools or external APIs, maintain conversation and memory, and adapt based on results. The platform supports OpenAI, Azure, or self-hosted models, offering built-in tool integrations for web search, file handling, mathematical computation, and custom plugins. Designed for experimentation and rapid prototyping, AgentLLM streamlines building intelligent agents capable of automating complex business processes, data analysis, customer support, and personalized recommendations.

Agents Base harnesses artificial intelligence to develop customizable agents that streamline business processes. Users can design agents that respond to customer queries, handle transactions, and manage workflows efficiently. This technology is built for flexibility and scalability, making it suitable for both small enterprises and large corporations looking to enhance their service delivery and operational efficiency.

AGENTS.inc specializes in creating AI agents that can be customized according to user needs. These agents assist with workflow automation, scheduling, and data management, saving time and increasing efficiency. Users can define the tasks their agents should perform, ensuring that the AI seamlessly integrates into their daily operations. The platform allows for real-time updates and easy adjustments to the agent's functions, making it ideal for both personal and professional use.

AI Agent Code Generator provides a command-line interface to scaffold Python projects for AI agents. Users select from multiple LangChain-based templates, configure their OpenAI API keys, and specify custom tools or functions. The tool then generates boilerplate code, project structure, and sample scripts to deploy conversational, information-retrieval, or task-automation agents. Developers can extend the generated code with additional plugins, modify prompts, and integrate new toolkits for specialized agent behavior, accelerating prototype and production development.

AI Agents with LangGraph leverages a graph representation to define relationships and communication between autonomous AI agents. Each node represents an agent or tool, enabling task decomposition, prompt customization, and dynamic action routing. The framework integrates seamlessly with popular LLMs and supports custom tool functions, memory stores, and logging for debugging. Developers can prototype complex workflows, automate multi-step processes, and experiment with collaborative agent interactions in just a few lines of Python code.

ANAC-agents is a Python-based framework that centralizes multiple negotiation agent implementations for the Automated Negotiating Agents Competition (ANAC). Each agent within the repository embodies distinct strategies for utility modeling, proposal generation, concession tactics, and acceptance criteria, facilitating comparative studies and rapid prototyping. Users can define negotiation domains with custom issues and preference profiles, then simulate bilateral negotiations or tournament-style competitions across agents. The toolkit includes configuration scripts, evaluation metrics, and logging utilities to analyze negotiation dynamics. Researchers and developers can extend existing agents, test novel algorithms, or integrate external learning modules, accelerating innovation in automated bargaining and strategic decision-making under incomplete information.

Autogen Studio Research is a GitHub-hosted research prototype for building, visualizing, and iterating on multi-agent AI applications. It offers a web-based UI that lets you drag and drop agent components, define communication channels, and configure execution pipelines. Under the hood, it uses a Python SDK to connect to various LLM backends (OpenAI, Azure, local models) and provides real-time logging, metrics, and debugging tools. The platform is designed for rapid prototyping of collaborative agent systems, decision-making workflows, and automated task orchestration.