Envole is an innovative no-code platform designed to create dynamic AI agents using plain English. Users can define the types of inputs (text, files, images, audio), specify activities (answering questions, summarizing, sentiment analysis), unlock actions (sending emails, saving to Notion, tool integration), and configure outputs. With Envole, users can empower their AI agents to connect with various tools, create custom actions, and deploy them via hosted environments or API integrations. Envole simplifies AI automation, making it accessible and versatile for transforming workflows.

Melissa provides a lightweight, extensible architecture for building AI-driven agents without requiring extensive boilerplate code. At its core, the framework leverages a plugin-based system where developers can register custom actions, data connectors, and memory modules. The memory subsystem enables context preservation across interactions, enhancing conversational continuity. Integration adapters allow agents to fetch and process information from APIs, databases, or local files. By combining a straightforward API, CLI tools, and standardized interfaces, Melissa streamlines tasks such as automating customer inquiries, generating dynamic reports, or orchestrating multi-step workflows. The framework is language-agnostic for integration, making it suitable for Python-centric projects and can be deployed on Linux, macOS, or Docker environments.

Viggle AI is a groundbreaking platform that transforms character images into animated videos with customizable actions. Leveraging advanced AI models, it allows users to create dynamic and engaging videos, making it easier than ever to animate characters for various purposes. Whether you want to create dancing videos, sports highlights, or other creative animations, Viggle AI provides an intuitive and fun tool for both personal and professional use.

AgentLab is a ServiceNow framework for creating AI agents—also called digital workers—using a visual, drag-and-drop editor. Users link large language models with ServiceNow tables, define intents and actions, and orchestrate workflows for tasks like incident resolution, change approvals, and knowledge retrieval. Agents can be tested in built-in sandboxes, versioned, and monitored in real time. With connectors for external APIs and chat interfaces, AgentLab enables deployment across portals, Microsoft Teams, and Slack. The platform offers governance controls, audit trails, and analytics dashboards to ensure compliance and performance at scale.

AtomicAgent provides a structured framework for defining, composing, and executing AI agent tasks. Core modules include a tool registry to register and invoke external services, a memory manager to persist conversational or task context, and an orchestration engine that drives LLM interactions step by step. Developers can define reusable tools, configure decision logic, and leverage asynchronous execution for long-running tasks. AtomicAgent’s modular design promotes maintainability, testability, and rapid iteration of complex AI-driven workflows, from chatbots to data processing pipelines.