Nova provides a comprehensive toolkit for creating autonomous AI agents in Python. It offers a planner that decomposes goals into actionable steps, a plugin system to integrate any external tools or APIs, and a memory module to store and recall conversation context. Developers can configure custom behaviors, track agent decisions through logs, and extend functionality with minimal code. Nova streamlines the entire agent lifecycle from design to deployment.

Stella Framework empowers developers to build robust AI agents that can maintain context, perform tool-assisted actions, and deliver dynamic conversational experiences. By abstracting the complexities of LLM integrations, Stella offers provider-agnostic adapters for OpenAI, Hugging Face, and self-hosted models. Agents can leverage customizable memory stores to recall user data and conversation history, and plugins enable interactions with external APIs, databases, or services. The built-in orchestration engine manages decision loops, while a concise DSL allows defining actions, tool calls, and response handling. Whether creating customer support bots, research assistants, or workflow automators, Stella provides a scalable foundation for deploying production-grade AI agents.

ADK-Golang is an open-source Agent Development Kit for the Go ecosystem. It provides a modular framework to register and manage tools (APIs, databases, external services), build dynamic prompt templates, and maintain conversation memory for multi-turn interactions. With built-in orchestration patterns and logging support, developers can easily configure, test, and deploy AI agents that perform tasks such as data retrieval, automated workflows, and contextual chat. ADK-Golang abstracts low-level API calls and streamlines end-to-end agent lifecycles—from initialization and planning to execution and response handling—entirely in Go.

Besser Agentic Framework offers a modular toolkit for defining, coordinating, and scaling AI agents. It allows you to configure agent behaviors, integrate external tools and APIs, manage agent memory and state, and monitor execution. Built on Python, it supports extensible plugin interfaces, multi-agent collaboration, and built-in logging. Developers can rapidly prototype and deploy agents for tasks like data extraction, automated research, and conversational assistants, all within a unified framework.

Enterprise AI Agents Spec defines a comprehensive specification for enterprise-grade AI agents, including manifest schemas for agent identity, description, triggers, memory management, and supported tools. The framework includes JSON-based tool definition formats, pipeline and workflow orchestration guidelines, and versioning standards to ensure consistent deployments. It supports extensibility through custom tool registration, security and governance best practices, and integration with various runtimes. By following its open standard, teams can build, share, and maintain AI agents across multiple environments, promoting collaboration, scalability, and uniform development processes within large organizations.