This repository implements emergent communication in multi-agent reinforcement learning using PyTorch. Users can configure sender and receiver neural networks to play referential games or cooperative navigation, encouraging agents to develop a discrete or continuous communication channel. It offers scripts for training, evaluation, and visualization of learned protocols, along with utilities for environment creation, message encoding, and decoding. Researchers can extend it with custom tasks, modify network architectures, and analyze protocol efficiency, fostering rapid experimentation in emergent agent communication.

MACL is a modular Python framework designed to simplify the creation and orchestration of multiple AI agents. It lets you define individual agents with custom skills, set up communication channels, and schedule tasks across an agent network. Agents can exchange messages, negotiate responsibilities, and adapt dynamically based on shared data. With built-in support for popular LLMs and a plugin system for extensibility, MACL enables scalable and maintainable AI workflows across domains like customer service automation, data analysis pipelines, and simulation environments.

VillagerAgent provides a comprehensive toolkit for constructing AI agents that leverage large language models. At its core, developers define modular tool interfaces such as web search, data retrieval, or custom APIs. The framework manages agent memory by storing conversation context, facts, and session state for seamless multi-turn interactions. A flexible prompt templating system ensures consistent messaging and behavior control. Advanced features include orchestrating multiple agents to collaborate on tasks and scheduling background operations. Built in Python, VillagerAgent supports easy installation through pip and integrates with popular LLM providers. Whether building customer support bots, research assistants, or workflow automation tools, VillagerAgent streamlines the design, testing, and deployment of intelligent agents.

Agent-Baba provides a comprehensive toolkit for creating and managing autonomous AI agents tailored to specific tasks. It offers a plugin architecture for extending capabilities, a memory system to retain conversational context, and workflow automation for sequential task execution. Developers can integrate tools like web scrapers, databases, and custom APIs into agents. The framework simplifies configuration through declarative YAML or JSON schemas, supports multi-agent collaboration, and provides monitoring dashboards to track agent performance and logs, enabling iterative improvement and seamless deployment across environments.

Agent-FLAN is designed to simplify the creation of sophisticated AI agent-driven applications by segmenting tasks into planning and execution roles. Users define agent behaviors and workflows via configuration files, specifying input formats, tool interfaces, and communication protocols. The planning agent generates high-level task plans, while execution agents carry out specific actions, such as calling APIs, processing data, or generating content with large language models. Agent-FLAN’s modular architecture supports plug-and-play tool adapters, custom prompt templates, and real-time monitoring dashboards. It seamlessly integrates with popular LLM providers like OpenAI, Anthropic, and Hugging Face, enabling developers to quickly prototype, test, and deploy multi-agent workflows for scenarios such as automated research assistants, dynamic content generation pipelines, and enterprise process automation.

AgentForge provides a structured environment for defining, combining, and orchestrating individual AI skills into cohesive autonomous agents. It supports conversation memory for context retention, plugin integration for external services, multi-agent communication, task scheduling, and error handling. Developers can configure custom skill handlers, leverage built-in modules for natural language understanding, and integrate with popular LLMs like OpenAI’s GPT series. AgentForge’s modular design accelerates development cycles, facilitates testing, and simplifies deployment of chatbots, virtual assistants, data analysis agents, and domain-specific automation bots.

Agentic-Systems is designed to streamline the development of sophisticated autonomous AI applications by offering a modular architecture composed of agent, tool, and memory components. Developers can define custom tools that encapsulate external APIs or internal functions, while memory modules retain contextual information across agent iterations. The built-in orchestration engine schedules tasks, resolves dependencies, and manages multi-agent interactions for collaborative workflows. By decoupling agent logic from execution details, the framework enables rapid experimentation, easy scaling, and fine-grained control over agent behavior. Whether prototyping research assistants, automating data pipelines, or deploying decision-support agents, Agentic-Systems provides the necessary abstractions and templates to accelerate end-to-end AI solution development.

Agentic Shared Memory provides a robust solution for managing contextual data in AI-driven multi-agent environments. Leveraging vector embeddings and efficient data structures, it stores agent observations, decisions, and state transitions, enabling seamless context retrieval and update. Agents can query the shared memory to access past interactions or global knowledge, fostering coherent behavior and collaborative problem-solving. The library supports plug-and-play integration with popular AI frameworks like LangChain or custom agent orchestrators, offering customizable retention strategies, context windowing, and search functions. By abstracting memory management, developers can focus on agent logic while ensuring scalable, consistent memory handling across distributed or centralized deployments. This improves overall system performance, reduces redundant computations, and enhances agent intelligence over time.

The AWS Bedrock Multi-Agent Blueprint provides a modular framework to implement a multi-agent architecture on AWS Bedrock. It includes sample code for defining agent roles—planner, researcher, executor, and evaluator—that collaborate through shared message queues. Each agent can invoke different Bedrock models with custom prompts and pass intermediate outputs to subsequent agents. Built-in CloudWatch logging, error handling patterns, and support for synchronous or asynchronous execution demonstrate how to manage model selection, batch tasks, and end-to-end orchestration. Developers clone the repo, configure AWS IAM roles and Bedrock endpoints, then deploy via CloudFormation or CDK. The open-source design encourages extending roles, scaling agents across tasks, and integrating with S3, Lambda, and Step Functions.

Swarms World provides a unified interface for designing multi-agent systems, allowing users to define roles, communication protocols, and workflows visually or via code. Agents can collaborate, delegate subtasks, and aggregate results in real time. The platform supports on-premises, cloud, and edge deployments, with built-in logging, performance metrics, and automatic scaling. A decentralized marketplace lets users discover, share, and monetize agent modules. With support for popular LLMs, APIs, and custom models, Swarms World accelerates the development of robust, enterprise-grade AI automation at scale.

CrewAI Studio is a web-based platform that allows developers to design, visualize, and monitor multi-agent AI workflows. Users can configure each agent’s prompts, chain logic, memory settings, and external API integrations via a graphical canvas. The studio connects to popular vector databases, LLM providers, and plugin endpoints. It supports real-time debugging, conversation history tracking, and one-click deployment to custom environments, streamlining the creation of powerful digital assistants.

DSpy Agents is an open-source Python toolkit that simplifies creation of autonomous AI agents. It provides a modular architecture to assemble agents with customizable tools for web scraping, document analysis, database queries, and language model integrations (OpenAI, Hugging Face). Developers can orchestrate complex workflows using pre-built agent templates or define custom tool sets to automate tasks like research summarization, customer support, and data pipelines. With built-in memory management, logging, retrieval-augmented generation, multi-agent collaboration, and easy deployment via containerization or serverless environments, DSpy Agents accelerates development of agent-driven applications without boilerplate code.

Emergent Communication in Agents is an open-source PyTorch framework designed for researchers exploring how multi-agent systems develop their own communication protocols. The library offers flexible implementations of cooperative reinforcement learning tasks, including referential games, combination games, and object identification challenges. Users define speaker and listener agent architectures, specify message channel properties like vocabulary size and sequence length, and select training strategies such as policy gradients or supervised learning. The framework includes end-to-end scripts for running experiments, analyzing communication efficiency, and visualizing emergent languages. Its modular design allows easy extension with new game environments or custom loss functions. Researchers can reproduce published studies, benchmark new algorithms, and probe compositionality and semantics of emergent agent languages.

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.

GenWorlds is an AI development framework designed to facilitate the creation of multi-agent systems. Utilizing an event-based communication framework via websocket, it allows developers to set up interactive environments where autonomous agents can asynchronously interact with each other and their surroundings. These agents collaborate, plan actions, and execute complex tasks collectively, making GenWorlds a robust platform for creating scalable and flexible AI ecosystems.

GPT-agents is a developer-focused toolkit that streamlines the creation and orchestration of autonomous AI agents using GPT. It offers built-in Agent classes, a modular tool integration system, and persistent memory management to support ongoing context. The framework handles conversational planning loops and multi-agent collaboration, allowing you to assign objectives, schedule sub-tasks, and chain agents on complex workflows. It supports customizable tools, model selection, and error handling to deliver robust, scalable automation for various domains.

LiteSwarm is a comprehensive AI agent orchestration framework designed to facilitate collaboration among multiple specialized agents. Users define individual agents with distinct roles—such as data fetching, analysis, summarization, or external API calls—and link them within a visual workflow. LiteSwarm handles inter-agent communication, persistent memory storage, error recovery, and logging. It supports API integration, custom code extensions, and real-time monitoring, so teams can prototype, test, and deploy complex multi-agent solutions without extensive engineering overhead.

Swarms.ai provides a visual interface to define and connect multiple AI agents into intelligent workflows. Each agent can be configured with specific roles, data sources, and custom API integrations. Agents collaborate by passing messages, triggering actions, and sharing context to handle complex tasks end to end. The platform offers role-based access control, versioning, and real-time analytics to monitor swarm performance. No coding is required: users drag and drop components, set triggers, and link outputs to design automated processes for support, sales, operations, and more.

Meta-Agent-with-More-Agents is an extensible open-source framework that implements a meta agent architecture allowing multiple specialized sub-agents to collaborate on complex tasks. It leverages LangChain for agent orchestration and OpenAI APIs for natural language processing. Developers can define custom agents for tasks like data extraction, sentiment analysis, decision-making, or content generation. The meta agent coordinates task decomposition, dispatches objectives to appropriate agents, gathers their outputs, and iteratively refines results via feedback loops. Its modular design supports parallel processing, logging, and error handling. Ideal for automating multi-step workflows, research pipelines, and dynamic decision support systems, it simplifies building robust distributed AI systems by abstracting inter-agent communication and lifecycle management.

Multi-Agent-LLM is designed to streamline the orchestration of multiple AI agents powered by large language models. Users can define individual agents with unique personas, memory storage, and integrated external tools or APIs. A central AgentManager handles communication loops, allowing agents to exchange messages in a shared environment and collaboratively advance towards complex objectives. The framework supports swapping LLM providers (e.g., OpenAI, Hugging Face), flexible prompt templates, conversation histories, and step-by-step tool contexts. Developers benefit from built-in utilities for logging, error handling, and dynamic agent spawning, enabling scalable automation of multi-step workflows, research tasks, and decision-making pipelines.