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.

RinSim provides a modular simulation environment focused on modeling dynamic logistics scenarios with multiple autonomous agents. Users can define road networks via graph structures, configure fleets of vehicles including electric models with battery constraints, and simulate stochastic request arrivals for pickup and delivery tasks. The discrete-event architecture ensures precise timing and event management, while built-in routing algorithms and customizable agent behaviors allow extensive experimentation. RinSim supports output metrics such as travel time, energy consumption, and service level, and includes visualization modules for real-time and post-simulation analysis. Its extensible design enables integration of custom algorithms, scaling up to large fleets, and reproducible research workflows essential for academia and industry optimization of mobility strategies.

Saiki is an open-source agent orchestration framework that empowers developers to build complex AI-driven workflows by writing declarative YAML definitions. Each agent can perform tasks, call external services, or invoke other agents in a chained sequence. Saiki provides a built-in REST API server, execution tracing, detailed log output, and a web-based dashboard for real-time monitoring. It supports retries, fallbacks, and custom extensions, making it easy to iterate, debug, and scale robust automation pipelines.

Swarm Squad leverages autonomous AI agents that operate in concert to manage and execute complex workflows. Users define objectives and configure agent roles—such as research, drafting, analysis, and scheduling—through an intuitive interface. Each agent specializes in its function, exchanging data and feedback to refine outputs iteratively. The platform integrates with popular services like Google Drive, Slack, and CRM systems, enabling seamless data transfer and task handoffs. Real-time dashboards track agent performance, while automated alerts ensure timely interventions. Advanced customization features allow users to script custom agent behaviors and trigger conditional workflows, resulting in a unified, end-to-end solution for marketing campaigns, customer outreach, report generation, and other business-critical processes.

This repository delivers a customizable multi-agent framework simulating a taxi call center. It defines distinct AI agents: CustomerAgent to request rides, DispatchAgent to select drivers based on proximity, DriverAgent to confirm assignments and update statuses, and NotificationAgent for billing and messages. Agents interact through an orchestrator loop using OpenAI GPT calls and memory, enabling asynchronous dialogue, error handling, and logging. Developers can extend or adapt agent prompts, integrate real-time systems, and prototype AI-driven customer service and dispatch workflows with ease.

Tongui Agent provides a modular architecture for creating AI agents that can maintain conversation state, leverage external tools, and coordinate multiple sub-agents. Developers configure LLM backends, define custom actions, and attach memory modules to store context. The framework includes an SDK, CLI, and middleware hooks for observability, making it easy to integrate into web or Node.js applications. Supported LLMs include OpenAI, Azure OpenAI, and open-source models.

Java-Action-Shape is a dedicated action library designed to extend the LightJason multi-agent framework with advanced geometric capabilities. It provides agents with out-of-the-box actions to instantiate common shapes (circle, rectangle, polygon), apply transformations (translate, rotate, scale), and perform analytical computations (area, perimeter, centroid). Each action is thread-safe and integrates with LightJason’s asynchronous execution model, ensuring efficient parallel processing. Developers can define custom shapes by specifying vertices and edges, register them within the agent’s action registry, and include them in plan definitions. By centralizing shape-related logic, Java-Action-Shape reduces boilerplate code, enforces consistent APIs, and accelerates the creation of geometry-driven agent applications, from simulations to educational tools.

LLMLing Agent is a modular framework for building, configuring, and deploying AI agents powered by large language models. Users can instantiate multiple agent roles, connect external tools or APIs, manage conversational memory, and orchestrate complex workflows. The platform includes a browser-based playground that visualizes agent interactions, logs message history, and allows real-time adjustments. With a Python SDK, developers can script custom behaviors, integrate vector databases, and extend the system through plugins. LLMLing Agent streamlines creation of chatbots, data analysis bots, and automated assistants by providing reusable components and clear abstractions for multi-agent collaboration.

OpenAI Swarm Node orchestrates concurrent calls to multiple ChatGPT agents, gathers individual outputs, applies your chosen aggregation strategy—such as majority voting or custom weighting—and returns a unified consensus response. Its extensible architecture supports fine-grained control over model parameters, error handling, retry logic, and asynchronous execution, enabling developers to integrate swarm intelligence into any Node.js application for higher accuracy and consistency in AI-driven decision-making.

PommerLearn enables researchers and developers to train multi-agent RL bots in the Pommerman game environment. It includes ready-to-use implementations of popular algorithms (PPO, DQN), flexible configuration files for hyperparameters, automatic logging and visualization of training metrics, model checkpointing, and evaluation scripts. Its modular architecture makes it easy to extend with new algorithms, customize environments, and integrate with standard ML libraries such as PyTorch.

Team Coordination is a lightweight Python library designed to simplify the orchestration of multiple AI agents working together on complex tasks. By defining specialized agent roles—such as planners, executors, evaluators, or communicators—users can decompose a high-level objective into manageable sub-tasks, delegate them to individual agents, and facilitate structured communication between them. The framework handles asynchronous execution, protocol routing, and result aggregation, allowing teams of AI agents to collaborate efficiently. Its plugin system supports integration with popular LLMs, APIs, and custom logic, making it ideal for applications in automated customer service, research, game AI, and data processing pipelines. With clear abstractions and extensible components, Team Coordination accelerates the development of scalable multi-agent workflows.

CASA is designed as a modular, plug-and-play autonomy framework built on the Robot Operating System (ROS) ecosystem. It features a decentralized architecture where each robot runs local planners and behavior tree nodes, publishing to a shared blackboard for world-state updates. Task allocation is handled via auction-based algorithms that assign missions based on robot capabilities and availability. The communication layer uses standard ROS messages over multirobot networks to synchronize agents. Developers can customize mission parameters, integrate sensor drivers, and extend behavior libraries. CASA supports scenario simulation, real-time monitoring, and logging tools. Its extensible design allows research teams to experiment with novel coordination algorithms and deploy seamlessly on diverse robotic platforms, from unmanned ground vehicles to aerial drones.

Agent Forge streamlines the entire lifecycle of AI agent development by offering CLI scaffold commands to generate boilerplate code, conversation templates, and configuration settings. Developers can define agent roles, attach LLM providers, and integrate external tools such as vector databases, REST APIs, and custom plugins using YAML or JSON descriptors. The framework enables local execution, interactive testing, and packaging agents as Docker images or serverless functions for easy deployment. Built-in logging, environment profiles, and VCS hooks simplify debugging, collaboration, and CI/CD pipelines. This flexible architecture supports creating chatbots, autonomous research assistants, customer support bots, and automated data processing workflows with minimal setup.

Agentic Seek leverages OpenAI’s GPT models and a custom toolkit to automate the entire web research lifecycle. Users define high-level objectives, and the system spawns specialized sub-agents to execute search queries, navigate websites, extract key information via scraping, and summarize findings. It supports iterative refinement, allowing agents to revisit and update results based on new insights. Developers can extend its capabilities by integrating custom action handlers and API connectors. Ideal for competitive intelligence, academic research, market analysis, and large-scale data gathering, Agentic Seek reduces manual browsing, accelerates decision-making, and ensures comprehensive coverage across multiple online sources. The platform includes a web-based interface for monitoring agent activity and reviewing intermediate outputs. With built-in logging, customizable prompts, and audit trails, teams can trace agent decisions for transparency, compliance, and quality assurance.

The Agent Supervisor Example repository demonstrates how to orchestrate several autonomous AI agents in a coordinated workflow. Built in Python, it defines a Supervisor class to dispatch tasks, monitor agent status, handle failures, and aggregate responses. You can extend base agent classes, plug in different model APIs, and configure scheduling policies. It logs activities for auditing, supports parallel execution, and offers a modular design for easy customization and integration into larger AI systems.

Agentic Website Dev brings automation to website development by coordinating specialized AI agents. One agent analyzes user prompts to draft site architecture, another generates responsive HTML and CSS templates, while a coding agent implements dynamic JavaScript features. Finally, a deployment agent packages and pushes the site to hosting platforms like Vercel or Netlify. This framework abstracts the entire workflow—planning, coding, testing, and deployment—enabling rapid prototyping and iteration. Developers define website requirements in plain English, and the agents collaborate to produce a fully functional, live website. This reduces manual coding, accelerates time-to-market, and democratizes web development for non-technical stakeholders.

AGNO AI Agents delivers a suite of customizable, pre-built AI agents that handle a variety of tasks: summarizing large documents, scraping and interpreting web content, answering domain-specific queries, reviewing source code, analyzing data sets, and powering chatbots with memory. Its modular design lets you plug in new tools or integrate external APIs. Agents are orchestrated via LangChain pipelines and exposed through REST endpoints. AGNO supports multi-agent workflows, logging, and easy deployment, enabling developers to accelerate AI-driven automation in their apps.

Dynamic Multi-Agent RAG Pathway provides a modular architecture where each agent handles specific tasks—such as document retrieval, vector search, context summarization, or generation—while a central orchestrator dynamically routes inputs and outputs between them. Developers can define custom agents, assemble pipelines via simple configuration files, and leverage built-in logging, monitoring, and plugin support. This framework accelerates development of complex RAG-based solutions, enabling adaptive task decomposition and parallel processing to improve throughput and accuracy.

The GA-based NQueen Solver uses a modular 2APL multi-agent architecture where each agent encodes a candidate N-Queen configuration. Agents evaluate their fitness by counting non-attacking queen pairs, then share high-fitness configurations with others. Genetic operators—selection, crossover, and mutation—are applied across the agent population to generate new candidate boards. Over successive iterations, agents collectively converge on valid N-Queen solutions. The framework is implemented in Java, supports parameter tuning for population size, crossover rate, mutation probability, and agent communication protocols, and outputs detailed logs and visualizations of the evolutionary process.

GPA-LM is a Python-based framework designed to simplify the creation and orchestration of AI agents powered by large language models. It features a planner that breaks down high-level instructions into sub-tasks, an executor that manages tool calls and interactions, and a memory module that retains context across sessions. The plugin architecture allows developers to add custom tools, APIs, and decision logic. With multi-agent support, GPA-LM can coordinate roles, distribute tasks, and aggregate results. It integrates seamlessly with popular LLMs like OpenAI GPT and supports deployment on various environments. The framework accelerates the development of autonomous agents for research, automation, and application prototyping.