Esquilax is a lightweight TypeScript framework designed for building and orchestrating complex AI agent workflows. It provides developers with a clear API to declaratively define agents, assign memory modules, and integrate custom plugin actions such as API calls or database queries. With built-in support for context handling and multi-agent coordination, Esquilax streamlines the creation of chatbots, digital assistants, and automated processes. Its event-driven architecture allows tasks to be chained or triggered dynamically, while logging and debugging tools offer full visibility into agent interactions. By abstracting away boilerplate code, Esquilax helps teams rapidly prototype scalable AI-driven applications.

Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.

Agent-Oriented Architecture (AOA) is a robust framework that equips developers with tools to build and maintain intelligent multi-agent systems. Agents encapsulate state, behaviors, and interaction patterns, communicating via an asynchronous message bus. AOA includes modules for agent registration, discovery, and matchmaking, enabling dynamic service composition. Behavior modeling supports finite-state machines, goal-driven planning, and event-driven triggers. The framework handles agent lifecycle events like creation, suspension, migration, and termination. Built-in monitoring and logging facilitate performance tuning and debugging. AOA’s pluggable transport layer supports TCP, HTTP, and custom protocols, making it adaptable for on-premise, cloud, or edge deployments. Integration with popular libraries ensures seamless data processing and AI model integration.