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.

PrisimAI provides a browser-based environment where users can rapidly prototype and deploy intelligent agents. Through a visual flow builder, you can assemble LLM-powered components, integrate external APIs, manage long-term memory, and orchestrate multi-step tasks. Built-in debugging and monitoring simplify testing and iteration, while a plugin marketplace allows extension with custom tools. PrisimAI supports collaboration across teams, version control for agent designs, and one-click deployment for webhooks, chat widgets, or standalone services.

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.

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.

AIPE centralizes AI agent orchestration with pluggable modules for memory, planning, tool use, and multi-agent collaboration. Developers can define agent personas, incorporate context via vector stores, and integrate external APIs or databases. The framework offers a built-in web dashboard and CLI for testing prompts, monitoring agent state, and chaining tasks. AIPE supports multiple memory backends like Redis, SQLite, and in-memory stores. Its multi-agent setups allow assigning specialized roles—data extractor, analyst, summarizer—to tackle complex queries collaboratively. By abstracting prompt engineering, API wrappers, and error handling, AIPE speeds up deployment of AI-driven assistants for document QA, customer support and automated workflows.

Isaree Platform is designed to streamline AI agent development and deployment. At its core, it provides a unified architecture for creating autonomous agents capable of conversation, decision-making, and collaboration. Developers can define multiple agents with custom roles, leverage vector-based memory retrieval, and integrate external data sources via pluggable modules. The platform includes a Python SDK and RESTful API for seamless interaction, supports real-time response streaming, and offers built-in logging and metrics. Its flexible configuration allows scaling across environments with Docker or cloud services. Whether building chatbots with persistent context, automating multi-step workflows, or orchestrating research assistants, Isaree Platform delivers extensibility and reliability for enterprise-grade AI solutions.

autogen_multiagent provides a structured way to instantiate, configure, and coordinate multiple AI agents in Python. It offers dynamic agent creation, inter-agent messaging channels, task planning, execution loops, and monitoring utilities. By integrating seamlessly with the OpenAI API, it allows you to assign specialized roles—such as planner, executor, summarizer—to each agent and orchestrate their interactions. This framework is ideal for scenarios requiring modular, scalable AI workflows, such as automated document analysis, customer support orchestration, and multi-step code generation.

Swarms.rs is the core Rust runtime for executing swarm-based AI agent programs. It features a modular plugin system to integrate custom logic or AI models, a message-passing layer for peer-to-peer communication, and an asynchronous executor for scheduling agent behaviors. Together, these components allow developers to design, deploy, and scale complex decentralized agent networks for simulation, automation, and multi-agent collaboration tasks.

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.

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.

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.

SwarmZero offers a scalable, open-source environment for defining, managing, and executing swarms of AI agents. Developers can declare agent roles, customize prompts, and chain workflows via a unified Orchestrator API. The framework integrates with major LLM providers, supports plugin extensions, and logs session data for debugging and performance analysis. Whether coordinating research bots, content creators, or data analyzers, SwarmZero streamlines multi-agent collaboration and ensures transparent, reproducible results.

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.

Multi-Agent is a developer toolkit that helps you build and orchestrate multiple AI agents running in parallel. Each agent maintains its own memory store, prompt configuration, and message queue. You can define custom behaviors, set up inter-agent communication channels, and delegate tasks automatically based on agent roles. It leverages OpenAI's Chat API for language understanding and generation, while providing modular components for workflow orchestration, logging, and error handling. This enables creation of specialized agents—such as research assistants, data processors, or customer support bots—that work together on multifaceted tasks.

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.

RoboCup Rescue Agent Simulation is an open-source framework that models urban disaster environments where multiple AI-driven agents collaborate to locate and rescue victims. It offers interfaces for navigation, mapping, communication, and sensor integration. Users can script custom agent strategies, run batch experiments, and visualize agent performance metrics. The platform supports scenario configuration, logging, and result analysis to accelerate research in multi-agent systems and disaster response algorithms.