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.

Exo is a developer-centric framework enabling the creation of AI-driven agents capable of communicating with users, invoking external APIs, and preserving conversational context. At its core, Exo uses TypeScript definitions to describe tools, memory layers, and dialogue management. Users can register custom actions for tasks like data retrieval, scheduling, or API orchestration. The framework automatically handles prompt templates, message routing, and error handling. Exo’s memory module can store and recall user-specific information across sessions. Developers deploy agents in Node.js or serverless environments with minimal configuration. Exo also supports middleware for logging, authentication, and metrics. Its modular design ensures components can be reused across multiple agents, accelerating development and reducing redundancy.

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.

SwiftAgent offers a robust toolkit for constructing intelligent agents by integrating OpenAI's models directly in Swift. Developers can declare custom actions and external tools, which agents invoke based on user queries. The framework maintains conversational memory, enabling agents to reference past interactions. It supports prompt templating and dynamic context injection, facilitating multi-turn dialogues and decision logic. SwiftAgent's async API works seamlessly with Swift concurrency, making it ideal for iOS, macOS, or server-side environments. By abstracting model calls, memory storage, and pipeline orchestration, SwiftAgent empowers teams to prototype and deploy conversational assistants, chatbots, or automation agents quickly within Swift projects.

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.

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.

AIKitchen provides a developer-friendly Python toolkit enabling you to compose AI agents as modular building blocks. At its core, it offers pipeline definitions with stages for input preprocessing, LLM invocation, tool execution, and memory retrieval. Integrations with popular LLM providers allow flexibility, while built-in memory stores track conversational context. Developers can embed custom tasks, leverage retrieval-augmented generation for knowledge access, and gather standardized metrics to monitor performance. The framework also includes workflow orchestration capabilities, supporting sequential and conditional flows across multiple agents. With its plugin architecture, AIKitchen streamlines end-to-end agent development—from prototyping research ideas to deploying scalable digital workers in production environments.

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.