Huginn is a versatile, open-source automation framework that lets users create agents to monitor, gather, and act on data from various sources such as websites, APIs, social media, and email. Each agent can be configured to trigger on events, transform data, and pass it to other agents or external services. With built-in scheduling, logging, and a rich library of agent types—like RSSAgent, EmailAgent, WebhookAgent, and DataOutputAgent—Huginn supports complex workflows and conditional logic. It runs on Linux, macOS, Windows, or Docker, and can be extended with custom Ruby code or Docker containers for specialized tasks and integrations.

Plan Agent with Meta-Agent provides a layered AI agent architecture: the Plan Agent generates structured strategies to achieve high-level goals, while the Meta-Agent oversees execution, adjusts plans in real-time, and delegates subtasks to specialized sub-agents. It features plug-and-play tool connectors (e.g., web APIs, databases), persistent memory for context retention, and configurable logging for performance analysis. Users can extend the framework with custom modules to suit diverse automation scenarios, from data processing to content generation and decision support.

The Agent Supervisor Example repository demonstrates how to orchestrate several autonomous AI agents in a coordinated workflow. Built in Python, it defines a Supervisor class to dispatch tasks, monitor agent status, handle failures, and aggregate responses. You can extend base agent classes, plug in different model APIs, and configure scheduling policies. It logs activities for auditing, supports parallel execution, and offers a modular design for easy customization and integration into larger AI systems.

AgentUniverse provides a unified Python SDK to design, orchestrate, and run autonomous AI agents. Developers can define agent behaviors, integrate external tools or APIs, maintain conversational memory, and sequence multi-step tasks. Supporting LangChain, custom tool plugins, and configurable runtime environments, it accelerates agent development and deployment. Built-in monitoring and logging enable real-time insights, while its modular architecture allows easy extension with new capabilities or AI models.

AUITestAgent harnesses the power of GPT-based AI to streamline mobile UI testing. By feeding it application screenshots and textual test scenarios, it automatically generates Appium scripts ready for execution on emulators or real devices. The agent supports both Android and iOS testing environments, offering customizable prompts for specific workflows. It also provides test result reporting and integrates effortlessly into existing CI/CD systems, ensuring faster, more reliable regression and functional testing with minimal manual effort.

BotGauge is a Generative AI-powered, low-code platform that revolutionizes test automation. It allows users to write test case scenarios in English and automates them with AI support. Aimed at enhancing testing efficiency, reducing costs, and speeding up time-to-market, BotGauge simplifies the process of end-to-end automation for web-based applications. With features like API, database, functional, visual, and UI testing, BotGauge is designed to comprehensively cover all testing needs.

Botticelli Bots is an open-source .NET Core framework that facilitates the development of universal bots. This platform allows integration with various databases, message brokers, speech engines, and numerous AI engines such as GPT-j and ChatGPT. Designed for ease of use, it supports cross-platform compatibility between Windows and Linux and provides tools to manage high-load systems, scheduling, and automation.

AgentSmithy is designed to streamline the development lifecycle of AI agents by offering modular components for memory management, task planning, and execution orchestration. The framework leverages Google Cloud Storage or Firestore for persistent memory, Cloud Functions for event-driven triggers, and Pub/Sub for scalable messaging. Handlers define agent behaviors, while planners manage multi-step task execution. Observability modules track performance metrics and logs. Developers can integrate bespoke plugins to enhance capabilities such as custom data sources, specialized LLMs, or domain-specific tools. AgentSmithy’s cloud-native architecture ensures high availability and elasticity, allowing deployment across development, testing, and production environments seamlessly. With built-in security and role-based access controls, teams can maintain governance while rapidly iterating on intelligent agent solutions.

AutoAct is designed to streamline the development of intelligent agents by combining LLM-driven reasoning with structured planning and modular tool integration. It offers a Planner component to generate action sequences, a ToolKit for defining and invoking external APIs, and a Memory module to maintain context. With logging, error handling, and configurable policies, AutoAct supports robust end-to-end automation for tasks such as data analysis, content generation, and interactive assistants. Developers can customize workflows, extend tools, and deploy agents on-premise or in the cloud.