The Multi Agent Robotic System project offers a modular Python-based platform for developing, simulating, and deploying cooperative robotic teams. At its core, it implements decentralized control strategies, enabling robots to share state information and collaboratively allocate tasks without a central coordinator. The system includes built-in modules for path planning, collision avoidance, environment mapping, and dynamic task scheduling. Developers can integrate new algorithms by extending provided interfaces, adjust communication protocols via configuration files, and visualize robot interactions in simulated environments. Compatible with ROS, it supports seamless transitions from simulation to real-world hardware deployments. This framework accelerates research by providing reusable components for swarm behavior, collaborative exploration, and warehouse automation experiments.

Rigging is a developer-focused framework that streamlines the creation and orchestration of AI agents. It provides tool and function registration, context and memory management, workflow chaining, callback events, and logging. Developers can integrate multiple LLM providers, define custom plugins, and assemble multi-step pipelines. Rigging’s type-safe TypeScript SDK ensures modularity and reusability, accelerating AI agent development for chatbots, data processing, and content generation tasks.

Microsoft AutoGen is designed to facilitate the end-to-end development of autonomous AI agents by providing modular components for memory management, task planning, tool integration, and communication. Developers can define custom tools with structured schemas and connect to major LLM providers such as OpenAI and Azure OpenAI. The framework supports both single-agent and multi-agent orchestration, enabling collaborative workflows where agents coordinate to complete complex tasks. Its plug-and-play architecture allows easy extension with new memory stores, planning strategies, and communication protocols. By abstracting the low-level integration details, AutoGen accelerates prototyping and deployment of AI-driven applications across domains like customer support, data analysis, and process automation.

NagaAgent is an open-source Python library designed to simplify the creation, orchestration, and scaling of AI agents. It provides a plug-and-play tool integration system, persistent conversational memory objects, and an asynchronous multi-agent controller. Developers can register custom tools as functions, manage agent state, and choreograph interactions between multiple agents. The framework includes logging, error-handling hooks, and configuration presets for rapid prototyping. NagaAgent is ideal for building complex workflows—customer support bots, data processing pipelines, or research assistants—without infrastructure overhead.

Playbooks AI is a developer framework for building AI agents through a declarative playbook DSL. It enables integration with various LLMs, custom tools, and memory stores. With a CLI and web UI, users can define agent behavior, orchestrate multi-step workflows, and monitor execution. Features include tool routing, stateful memory, version control, analytics, and multi-agent collaboration, making it easy to prototype and deploy production-ready AI assistants.

Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.

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.