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.

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.

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.

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.

GenerativeAgentsCN is an open-source Chinese adaptation of the Stanford Generative Agents framework designed to simulate lifelike digital personas. By combining large language models with a long-term memory module, reflection routines, and planner logic, it orchestrates agents that perceive context, recall past interactions, and autonomously decide on next actions. The toolkit provides ready-to-run Jupyter notebooks, modular Python components, and comprehensive Chinese documentation to walk users through setting up environments, defining agent characteristics, and customizing memory parameters. Use it to explore AI-driven NPC behavior, prototype customer service bots, or conduct academic research on agent cognition. With flexible APIs, developers can extend memory algorithms, integrate custom LLMs, and visualize agent interactions in real time.

MCP Ollama Agent leverages the Ollama local LLM runtime to provide a versatile agent framework for task automation. It integrates multiple tool interfaces, including web search via SERP API, file system operations, shell command execution, and Python environment management. By defining custom prompts and tool configurations, users can orchestrate complex workflows, automate repetitive tasks, and build specialized assistants tailored to various domains. The agent handles tool invocation and context management, maintaining conversation history and tool responses to generate coherent actions. Its CLI-based setup and modular architecture make it easy to extend with new tools and adapt to different use cases, from research and data analysis to development support.