Memonto functions as a middleware library for AI agents, orchestrating the complete memory lifecycle. During each conversation turn, it records user and AI messages, distills salient details, and generates concise summaries. These summaries are converted into embeddings and stored in vector databases or file-based stores. When constructing new prompts, Memonto performs semantic searches to retrieve the most relevant historical memories, enabling agents to maintain context, recall user preferences, and provide personalized responses. It supports multiple storage backends (SQLite, FAISS, Redis) and offers configurable pipelines for embedding, summarization, and retrieval. Developers can seamlessly integrate Memonto into existing agent frameworks, boosting coherence and long-term engagement.

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 Coordination provides a lightweight API to define AI agents, register them with a central coordinator, and dispatch tasks for collaborative problem solving. It handles message routing, concurrency control, and result aggregation. Developers can plug in custom agent behaviors, extend communication channels, and monitor interactions through built-in logging and hooks. This framework simplifies the development of distributed AI workflows, where each agent specializes in a subtask and the coordinator ensures smooth collaboration.

Scalable MADDPG is a research-oriented framework for multi-agent reinforcement learning, offering a scalable implementation of the MADDPG algorithm. It features centralized critics during training and independent actors at runtime for stability and efficiency. The library includes Python scripts to define custom environments, configure network architectures, and adjust hyperparameters. Users can train multiple agents in parallel, monitor metrics, and visualize learning curves. It integrates with OpenAI Gym-like environments and supports GPU acceleration via TensorFlow. By providing modular components, Scalable MADDPG enables flexible experimentation on cooperative, competitive, or mixed multi-agent tasks, facilitating rapid prototyping and benchmarking.