Dial4JaCa is a Java library plugin for the JaCaMo multi-agent platform that intercepts inter-agent messages, encodes agent intentions, and routes them through LLM backends (OpenAI, local models). It manages dialogue context, updates belief bases, and integrates response generation directly into AgentSpeak(L) reasoning cycles. Developers can customize prompts, define dialogue artifacts, and handle asynchronous calls, enabling agents to interpret user utterances, coordinate tasks, and retrieve external information in natural language. Its modular design supports error handling, logging, and multi-LLM selection, ideal for research, education, and rapid prototyping of conversational MAS.

Multi-Agents provides a structured environment where different AI agents—such as planners, executors, and critics—coordinate to solve multi-step tasks. The planner agent breaks down high-level goals into sub-tasks, the executor agent interacts with external APIs or tools to carry out each step, and the critic agent reviews outcomes for accuracy and consistency. Memory modules allow agents to store context across interactions, while a messaging system ensures seamless communication. The framework is extensible, letting users add custom roles, integrate proprietary tools, or swap LLM backends for specialized use cases.

Julep AI Responses is an AI agent framework delivered as a Node.js SDK and cloud platform. Developers initialize an Agent object, define onMessage handlers for custom responses, manage session state for context-aware conversations, and integrate plugins or external APIs. The platform handles hosting and scaling, enabling rapid prototyping and deployment of chatbots, customer support agents, or internal assistants with minimal setup.

OpenAgent offers a comprehensive framework for developing autonomous AI agents that can understand tasks, plan multi-step actions, and interact with external services. By integrating with LLMs such as OpenAI and Anthropic, it enables natural language reasoning and decision-making. The platform features a pluggable tool system for executing HTTP requests, file operations, and custom Python functions. Memory management modules allow agents to store and retrieve contextual information across sessions. Developers can extend functionality via plugins, configure real-time streaming of responses, and utilize built-in logging and evaluation tools to monitor agent performance. OpenAgent simplifies orchestration of complex workflows, accelerates prototyping of intelligent assistants, and ensures modular architecture for scalable AI applications.

Promptsion is an AI-enhanced Chrome extension designed to streamline your web browsing research process. It provides instant answers by understanding the context of highlighted texts, eliminating the need for endless tab switching and copying-pasting. With features like smart folders and context-aware responses, it greatly enhances productivity and organization. Promptsion is available in multiple pricing tiers, including a free version, making it accessible for casual browsers and professional researchers alike.

ChatRobo leverages state-of-the-art AI technology to facilitate seamless conversations across numerous topics. It excels in understanding context and providing accurate responses, making it a valuable tool for both individuals and businesses. With a wide range of functionalities, ChatRobo can assist in customer service, answer FAQs, and provide information on diverse subjects, ensuring an engaging and efficient user experience.

Python AGNO Memory Agent provides a structured approach to agent memory by organizing memories via an AGNO framework. It leverages embedding models to convert textual memories into vector representations and stores them in configurable vector stores like ChromaDB, FAISS, or SQLite. Agents can add new memories, query relevant past events, update outdated entries, or delete irrelevant data. The library offers timeline tracking, namespaced memory stores for multi-agent scenarios, and customizable similarity thresholds. It integrates easily with popular LLM frameworks and can be extended with custom embedding models to suit diverse AI agent applications.

SuperBot is a comprehensive AI Agent framework enabling developers to deploy autonomous, context-aware assistants via Python and the command line. It integrates OpenAI’s chat models with a memory system, function-calling features, and plugin architecture. Agents can execute shell commands, run code, interact with files, perform web searches, and maintain conversation state. SuperBot supports multi-agent orchestration for complex workflows, all configurable through simple Python scripts and CLI commands. Its extensible design allows you to add custom tools, automate tasks, and integrate external APIs to build robust AI-driven applications.

AI Agent with MCP is a comprehensive framework designed to streamline the development of advanced AI agents capable of maintaining long-term context, performing multi-step reasoning, and adapting strategies based on memory. It leverages a modular design comprising Memory Manager, Conditional Planner, and Prompt Manager, allowing custom integrations and extension with various LLMs. The Memory Manager persistently stores past interactions, ensuring context retention. The Conditional Planner evaluates conditions at each step and dynamically selects the next action. The Prompt Manager formats inputs and chains tasks seamlessly. Built in Python, it integrates with OpenAI GPT models via API, supports retrieval-augmented generation, and facilitates conversational agents, task automation, or decision support systems. Extensive documentation and examples guide users through setup and customization.