GenAI Agents SDK is an open-source Python library designed to help developers create self-driven AI agents using large language models. It offers a core agent template with pluggable modules for memory storage, tool interfaces, planning strategies, and execution loops. You can configure agents to call external APIs, read/write files, run searches, or interact with databases. Its modular design ensures easy customization, rapid prototyping, and seamless integration of new capabilities, empowering the creation of dynamic, autonomous AI applications that can reason, plan, and act in real-world scenarios.

Genux AI offers advanced AI-powered agents designed to enhance customer experience by providing intelligent support, handling inquiries, scheduling appointments, and collecting lead data. These customizable agents integrate seamlessly with your website and social media profiles and can operate round-the-clock in multiple languages. With features like tailored responses, seamless integration with over 2,000 applications, and data security, Genux AI ensures your business is always online, effectively managing customer interactions and streamlining operations.

js-agent provides developers with a minimalistic yet powerful toolkit to create autonomous AI agents in JavaScript. It offers abstractions for conversation memory, function-calling tools, customizable planning strategies, and error handling. With js-agent, you can quickly wire up prompts, manage state, invoke external APIs, and orchestrate complex agent behaviors through a simple, modular API. It's designed to run in Node.js environments and integrates seamlessly with the OpenAI API to power intelligent, context-aware agents.

Maxun.dev is a no-code/low-code AI agent framework that allows developers and businesses to create intelligent agents tailored to specific tasks. Users can define agent workflows via a visual interface, integrate data sources and external APIs, and configure memory modules for contextual understanding. The platform supports multi-agent orchestration, real-time monitoring, and performance analytics to optimize agent behaviors. With built-in collaboration tools, version control, and one-click deployment options, Maxun.dev simplifies the entire lifecycle from prototype to production, accelerating AI-driven automation across customer support, document management, and business processes.

Micro-agent is a lightweight, unopinionated JavaScript library designed to simplify the creation of sophisticated AI agents using large language models. It exposes core abstractions such as agents, tools, planners, and memory stores, allowing developers to assemble custom conversational flows. Agents can invoke external APIs or internal utilities as tools, enabling dynamic data retrieval and action execution. The library supports both short-term conversational memory and long-term persistent memory to maintain context across sessions. Planners orchestrate chain-of-thought processes, breaking down complex tasks into tool calls or language model queries. With configurable prompt templates and execution strategies, micro-agent adapts seamlessly to frontend web applications, Node.js services, and edge environments, providing a flexible foundation for chatbots, virtual assistants, or autonomous decision-making systems.

The Multi-Agent AI framework provides an end-to-end solution for orchestrating multiple autonomous agents in cloud environments. It leverages Neon’s Postgres-compatible serverless database to store conversation history and agent state, Azure Functions to run agent logic at scale, and OpenAI APIs to power natural language understanding and generation. Built-in message queues and role-based behaviors allow agents to collaborate on tasks such as research, scheduling, customer support, and data analysis. Developers can customize agent policies, memory rules, and workflows to fit diverse business requirements.

Multi-Agent-AI-Dynamic-Interaction offers a flexible environment to design, configure, and run systems composed of multiple autonomous AI agents. Each agent can be assigned specific roles, objectives, and communication protocols. The framework manages message passing, conversation context, and sequential or parallel interactions. It supports integration with OpenAI GPT, other LLM APIs, and custom modules. Users define scenarios via YAML or Python scripts, specifying agent details, workflow steps, and stopping criteria. The system logs all interactions for debugging and analysis, allowing fine-grained control over agent behaviors for experiments in collaboration, negotiation, decision-making, and complex problem-solving.

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.

OmniMind0 is a comprehensive agent-based AI framework written in Python that allows creation and orchestration of multiple autonomous agents. Each agent can be configured to handle specific tasks—such as data retrieval, summarization, or decision-making—while sharing state through pluggable memory backends like Redis or JSON files. The built-in plugin architecture lets you extend functionality with external APIs or custom commands. It supports OpenAI, Azure, and Hugging Face models, and offers deployment via CLI, REST API server, or Docker for flexible integration into your workflows.

SCOUT-2 provides a modular architecture for building autonomous agents powered by large language models. It includes goal decomposition, task planning, an execution engine, and a feedback-driven reflection module. Developers define a top-level objective, and SCOUT-2 automatically generates a task tree, dispatches worker agents for execution, monitors progress, and refines tasks based on outcomes. It integrates with OpenAI APIs and can be extended with custom prompts and templates to support a wide range of workflows.

Strands Agents provides a full-stack environment for creating intelligent assistants. Users can define conversation flows, manage knowledge bases, configure memory settings, and integrate with webhooks or external APIs. The platform offers analytics to measure performance, team collaboration tools for version control, and seamless deployment across web chat, mobile, or embedded widgets. No coding skills are required—customize behaviors via a visual editor and scale agents to handle high volumes of queries.

Strands Agents offers a modular architecture for creating intelligent agents that combine natural language reasoning, long-term memory, and external API/tool calls. It enables developers to configure planner, executor, and memory components, plug in any LLM (e.g., OpenAI, Hugging Face), define custom action schemas, and manage state across tasks. With built-in logging, error handling, and extensible tool registry, it accelerates prototyping and deployment of agents that can research, analyze data, control devices, or serve as digital assistants. By abstracting common agent patterns, it reduces boilerplate and promotes best practices for reliable, maintainable AI-driven automation.

Super-Agent-Party allows developers to define a Party object where individual AI agents perform distinct roles such as planning, researching, drafting, and reviewing. Each agent can be configured with custom prompts, tools, and model parameters. The framework manages message routing and shared context, enabling agents to collaborate in real time on subtasks. It supports plugin integration for third-party services, flexible agent orchestration strategies, and error handling routines. With an intuitive API, users can dynamically add or remove agents, chain workflows, and visualize agent interactions. Built on Node.js and compatible with major cloud providers, Super-Agent-Party streamlines the development of scalable, maintainable AI multi-agent systems for automation, content generation, data analysis, and more.

Swarm Simulator provides a customizable environment for real-time multi-agent experiments. Users can adjust key behavior parameters—alignment, cohesion, separation—and observe emergent dynamics on a visual canvas. It supports interactive UI sliders, dynamic agent count adjustment, and data export for analysis. Ideal for educational demonstrations, research prototyping, or hobbyist exploration of swarm intelligence principles.

xBrain provides a modular architecture for creating, configuring, and orchestrating autonomous agents within Python applications. Users define agents with specific capabilities—such as data retrieval, analysis, or generation—and assemble them into workflows where each agent communicates and delegates tasks. The framework includes a scheduler for managing asynchronous execution, a plugin system to integrate external APIs, and a built-in logging mechanism for real-time monitoring and debugging. xBrain’s flexible interface supports custom memory implementations and agent templates, allowing developers to tailor behavior to various domains. From chatbots and data pipelines to research experiments, xBrain accelerates the development of complex multi-agent systems with minimal boilerplate code.

AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.

AgentSpeak is an open-source Java-based implementation of the AgentSpeak(L) programming language, designed to facilitate the creation and management of BDI (Belief-Desire-Intention) autonomous agents. It features a runtime environment that parses AgentSpeak(L) code, maintains agents’ belief bases, triggers events, and selects and executes plans based on current beliefs and goals. The interpreter supports concurrent agent execution, dynamic plan updates, and customizable semantics. With a modular architecture, programmers can extend core components such as plan selection and belief revision. AgentSpeak enables developers in academia and industry to prototype, simulate, and deploy intelligent agents in simulations, IoT systems, and multi-agent scenarios.

CogAgent is a research-oriented, open-source Python library designed to streamline the development of AI agents. It provides core modules for memory management, planning and reasoning, tool and API integration, and chain-of-thought execution. With its highly modular architecture, users can define custom tools, memory stores, and agent policies to create conversational chatbots, autonomous task planners, and workflow automation scripts. CogAgent supports integration with popular LLMs such as OpenAI GPT and Meta LLaMA, allowing researchers and developers to experiment, extend, and scale their intelligent agents for a variety of real-world applications.

Arakoo.ai is an AI Agent platform designed to help businesses automate repetitive tasks and enhance customer interactions through intelligent virtual assistants. Users can select from a library of pre-built agent templates—such as support bots, sales assistants, and scheduling bots—or create custom agents using a visual workflow builder. The platform integrates with CRM systems, messaging apps, and ticketing tools, allowing agents to fetch data, answer queries, and escalate complex issues seamlessly. Arakoo.ai also offers analytics dashboards to track agent performance, conversation metrics, and user satisfaction. Advanced NLP capabilities ensure agents understand context and intent, while iterative training features enable continuous improvement based on real-world interactions.

Practical AI Agents provides developers with a comprehensive framework and ready-to-use examples to construct autonomous agents powered by large language models. It demonstrates how to integrate API tools (e.g., web browsers, databases, custom functions), implement RAG-style memory, manage conversation context, and perform dynamic planning. You can adapt examples for chatbots, data analysis assistants, task automation scripts, or research tools. The repository includes notebooks, Dockerfiles, and configuration files to streamline setup and deployment across environments.