AgentServe provides a unified interface for creating and deploying AI agents. Users define agent behaviors in configuration files or code, integrate external tools or knowledge sources, and expose agents over REST endpoints. The framework handles model routing, parallel requests, health checks, logging, and metrics out of the box. AgentServe’s modular design allows plugging in new models, custom tools, or scheduling policies, making it ideal for building chatbots, automated workflows, and multi-agent systems in a scalable, maintainable way.

Agentic Kernel offers a decoupled architecture for constructing AI agents by composing reusable components. Developers can define planning pipelines to break down goals, configure short-term and long-term memory stores using embeddings or file-based backends, and register external tools or APIs for action execution. The framework supports dynamic tool selection, agent reflection cycles, and built-in scheduling to manage agent workflows. Its pluggable design accommodates any LLM provider and custom components, enabling use cases such as conversational assistants, automated research agents, and data-processing bots. With transparent logging, state management, and easy integration, Agentic Kernel accelerates development while ensuring maintainability and scalability in AI-driven applications.

Automata is a developer-focused framework that enables creation of autonomous AI agents in JavaScript and TypeScript. It offers a modular architecture including planners for task decomposition, memory modules for context retention, and tool integrations for HTTP requests, database queries, and custom API calls. With support for asynchronous execution, plugin extensions, and structured outputs, Automata streamlines the development of agents that can perform multi-step reasoning, interact with external systems, and dynamically update their knowledge base.

Java-Action-Bool integrates seamlessly with the LightJason multi-agent framework, enabling developers to leverage ready-made boolean logic actions in their agent programs. Instead of writing custom boolean checks, you can call provided actions like ActionBoolAnd, ActionBoolOr, ActionBoolNot, and more. These actions evaluate truth values at runtime to guide agent behavior, reducing boilerplate code and simplifying plan definitions in cognitive and reactive agent systems.

OmniSteward is a modular AI agent orchestration platform built on Python that connects to OpenAI, local LLMs, and supports custom models. It provides memory modules to store context, toolkits for API calls, web search, code execution, and database queries. Users define agent templates with prompts, workflows, and triggers. The framework orchestrates multiple agents in parallel, manages conversation history, and automates tasks via pipelines. It also includes logging, monitoring dashboards, plugin architecture, and integration with third-party services. OmniSteward simplifies creating domain-specific assistants for research, operations, marketing, and more, offering flexibility, scalability, and open-source transparency for enterprises and developers.