Empathic Agents JS offers a robust framework for creating emotionally aware conversational agents in JavaScript. Developers can define custom emotional states, update them based on user inputs, and store context in both short- and long-term memory modules. Agents leverage OpenAI GPT-3.5 or compatible LLMs via provided integrations, enabling dynamic, contextually relevant, and empathy-driven dialogues. The library supports configuration of response styles, emotion-driven branching logic, and memory management hooks for personalization. Its modular design allows extension with custom actions, making it suitable for customer support, educational tutoring, companion bots, and other empathy-sensitive applications. Empathic Agents JS runs in both browser and Node.js environments, simplifying deployment across web and server platforms.

GenerativeAgentsCN is an open-source Chinese adaptation of the Stanford Generative Agents framework designed to simulate lifelike digital personas. By combining large language models with a long-term memory module, reflection routines, and planner logic, it orchestrates agents that perceive context, recall past interactions, and autonomously decide on next actions. The toolkit provides ready-to-run Jupyter notebooks, modular Python components, and comprehensive Chinese documentation to walk users through setting up environments, defining agent characteristics, and customizing memory parameters. Use it to explore AI-driven NPC behavior, prototype customer service bots, or conduct academic research on agent cognition. With flexible APIs, developers can extend memory algorithms, integrate custom LLMs, and visualize agent interactions in real time.

CamelAGI is an open-source framework designed to simplify the creation of autonomous AI agents. It features a plugin architecture for custom tools, long-term memory integration for context persistence, and support for multiple large language models such as GPT-4 and Llama 2. Through explicit planning and execution modules, agents can decompose tasks, call external APIs, and adapt over time. CamelAGI’s extensibility and community-driven approach make it suitable for research prototypes, production systems, and educational projects alike.

Llongterm is designed to enhance AI applications and agents by providing a robust long-term memory solution. It allows AI to remember and recall important interactions and details over long periods, thus improving the overall efficiency and accuracy of the AI. With its compatibility with various AI chatbots and agents, and features like human-readable memory, knowledge mapping, and structured timelines, Llongterm represents a significant advancement in AI memory technology.

Memorai is an AI-driven tool designed to help users remember what they read. By selecting passages from any text, Memorai generates relevant questions, which can be seamlessly added to Anki flashcards. This spaced repetition feature ensures that information is retained long-term, making it perfect for students and lifelong learners. The tool focuses on minimizing effort while maximizing memory retention, making studying more engaging and effective.

Neocortex is a web-based AI platform that acts as a personal knowledge hub and task manager. It stores and retrieves information using long-term memory, creates intelligent agents to handle research, summarization, and planning tasks, and integrates with documents, calendars, and APIs. Users can interact via chat to query past insights, generate reports, and delegate workflows to custom agents. Neocortex continually refines context, offers proactive reminders, and supports collaboration across teams.

Avalon-LLM is a Python-based multi-agent AI framework that allows users to orchestrate multiple LLM-driven agents in a coordinated environment. Each agent can be configured with specific tools—including web search, file operations, and custom APIs—to perform specialized tasks. The framework supports memory modules for storing conversation context and long-term knowledge, chain-of-thought reasoning to improve decision making, and built-in evaluation pipelines to benchmark agent performance. Avalon-LLM provides a modular plugin system, enabling developers to easily add or replace components such as model providers, toolkits, and memory stores. With simple configuration files and command-line interfaces, users can deploy, monitor, and extend autonomous AI workflows tailored to research, development, and production use cases.

ChatGuru is an innovative AI chatbot that integrates the power of ChatGPT API along with models like GPT-4 and Google Gemini. It provides real-time assistance by answering questions, providing information, and even assisting in research and mindfulness practices. This chatbot is designed to be your virtual assistant, offering ease of access and smart responses to improve productivity and user experience.

Devon provides a comprehensive suite of tools for defining, orchestrating, and running autonomous agents within Python applications. Users can outline agent goals, specify callable tasks, and chain actions based on conditional logic. Through seamless integration with language models like GPT and local vector stores, agents ingest and interpret user inputs, retrieve contextual knowledge, and generate plans. The framework supports long-term memory via pluggable storage backends, enabling agents to recall past interactions. Built-in monitoring and logging components allow real-time tracking of agent performance, while a CLI and SDK facilitate rapid development and deployment. Suitable for automating customer support, data analysis pipelines, and routine business operations, Devon accelerates the creation of scalable digital workers.

Doraemon-Agent is an open-source Python platform and framework designed for developers to build sophisticated AI agents. It allows you to integrate custom plugins and external tools, maintain long-term memory across sessions, and execute chain-of-thought planning with multiple steps. Developers can configure agent roles, manage context, log interactions, and extend functionality through a plugin architecture. It simplifies the creation of autonomous assistants for tasks like data analysis, research support, or customer service automation.

Superpowered AI offers a comprehensive API for Retrieval Augmented Generation (RAG), enabling developers to build applications that can efficiently query and chat with extensive knowledge bases. By uploading files and querying knowledge bases, users can enhance their LLM applications with external knowledge and long-term memory. The platform supports creating knowledge bases, integrating with various tools, and offers both a Python SDK and REST API for ease of use. It's designed for dynamic information retrieval, making it easier to generate insightful conversations and summaries.

Simular AI Agent S2 is a comprehensive solution to craft autonomous agents capable of handling complex multistep tasks. Users can ingest domain data for knowledge, set up long-term memory stores to maintain context, and integrate external tools (APIs, web browsers, databases) to fetch real-time information. The platform leverages fine-tuned GPT-4 models for robust decision-making and supports conversational and non-conversational interfaces. Agents can be deployed via API endpoints or embedded in applications, offering monitoring dashboards for performance insights and logs. Simular's built-in security ensures data privacy and compliance, making Agent S2 suitable for customer service, market research, and workflow automation across industries.

Agent Script provides a declarative scripting layer over large language models, enabling you to write YAML or JSON scripts that define agent workflows, tool calls, and memory usage. You can plug in OpenAI, local LLMs, or other providers, connect external APIs as tools, and configure long-term memory backends. The framework handles context management, asynchronous execution, and detailed logging out of the box. With minimal code, you can prototype chatbots, RPA workflows, data extraction agents, or custom control loops, making it easy to build, test, and deploy AI-powered automations.

Agents-Deep-Research is designed to streamline the development and testing of autonomous AI agents by offering a modular, extensible codebase. It features a task planning engine that decomposes user-defined goals into sub-tasks, a long-term memory module that stores and retrieves context, and a tool integration layer that allows agents to interact with external APIs and simulated environments. The framework also provides evaluation scripts and benchmarking tools to measure agent performance across diverse scenarios. Built on Python and adaptable to various LLM backends, it enables researchers and developers to rapidly prototype novel agent architectures, conduct reproducible experiments, and compare different planning strategies under controlled conditions.