AgentsFlow abstracts each AI agent as a node in a directed graph, enabling developers to visually and programmatically design complex pipelines. Each node can represent an LLM call, data preprocessing task, or decision logic, and can be connected to trigger subsequent actions based on outputs or conditions. The framework supports branching, loops, and parallel execution, with built-in error handling, retries, and timeout controls. AgentsFlow integrates with major LLM providers, custom models, and external APIs. Its monitoring dashboard offers real-time logs, metrics, and flow visualization, simplifying debugging and optimization. With a plugin system and REST API, AgentsFlow can be extended and integrated into CI/CD pipelines, cloud services, or custom applications, making it ideal for scalable, production-grade AI workflows.

Regal AI Agent Platform is designed to revolutionize customer interactions by providing AI Agents that handle calls more efficiently and professionally. It integrates with your existing data systems, making it easy to customize and monitor the interactions. The platform supports automated inbound and outbound calls, enhancing customer support, sales, and operational efficiency. It helps in increasing revenue, improving lead response speed, reducing service costs, and achieving high answer rates, making it a crucial tool for modern businesses.

AgentFarm provides a comprehensive framework to coordinate diverse AI agents in a unified system. Users can script specialized agent behaviors in Python, assign roles (manager, worker, analyzer), and establish task queues for parallel processing. It integrates seamlessly with major LLM services (OpenAI, Azure OpenAI), enabling dynamic prompt routing and model selection. The built-in dashboard tracks agent status, logs interactions, and visualizes workflow performance. With modular plug-ins for custom APIs, developers can extend functionality, automate error handling, and monitor resource utilization. Ideal for deploying multi-stage pipelines, AgentFarm enhances reliability, scalability, and maintainability in AI-driven automation.