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Agent Network Protocol (ANP) is an open-source communication protocol designed to facilitate secure, decentralized interactions among AI agents. It aims to become the 'HTTP of the Agentic Web Era' by providing a standardized framework that enables agents to connect, communicate, and collaborate efficiently. ANP's architecture comprises three layers: the Identity Layer for decentralized authentication using W3C DID standards, the Meta-Protocol Layer for dynamic protocol negotiation, and the Application Layer for semantic-based capability descriptions. This structure ensures interoperability, scalability, and secure communication across diverse AI systems.

Agent Network Protocol

agent-network-protocol

An open-source protocol enabling secure, decentralized communication among AI agents.

Google's Agent Development Kit (ADK) is an open-source framework designed to simplify the creation, deployment, and management of AI agents. Optimized for Google's Gemini models yet model-agnostic, ADK supports various agent types, including LLM-powered agents, workflow controllers, and custom agents. It offers features like multi-agent orchestration, tool integration, session management, and built-in evaluation tools. ADK facilitates flexible deployment options, including local environments, Google Cloud's Vertex AI Agent Engine, and Cloud Run, making it suitable for a wide range of AI applications.

Google Agent Development Kit

google-agent-development-kit

A modular framework for building, deploying, and managing AI agents across diverse workflows.

Google's Agent2Agent (A2A) protocol is an open standard designed to facilitate seamless communication and collaboration between AI agents developed by different organizations using various frameworks. A2A provides a standardized method for agents to discover each other's capabilities, negotiate interaction modalities, and securely collaborate on tasks without exposing internal states or proprietary tools. Built on existing technologies like HTTP, JSON-RPC 2.0, and Server-Sent Events (SSE), A2A supports both synchronous and asynchronous interactions, enabling agents to handle complex, long-running tasks efficiently. The protocol emphasizes security, interoperability, and scalability, making it suitable for enterprise-grade applications.

Google A2A

google-a2a

An open protocol enabling AI agents to communicate and collaborate across diverse systems securely.

BondAI is an open-source tool designed for developing sophisticated AI agent systems. It handles complex implementation aspects such as memory and context management, error handling, and semantic search capabilities. Supporting advanced AI architectures like ReAct Agents and Conversational Agents, BondAI enables both individual interactions and multi-agent collaborations. The framework integrates seamlessly with services like OpenAI models, Microsoft Azure, Google Search, and LangChain, providing a versatile environment for AI development.

BondAI

bondai

Open-source framework for developing AI agent systems with advanced memory management and integrations.

Seraphnet AI is a decentralized base layer designed for developing ideologically-transparent generative AI applications. It offers a multi-layered infrastructure that prioritizes unbiased outputs and data privacy through fully homomorphic encryption (FHE). The platform features components like the Swarm Manager, an intent-sensitive orchestrator for dynamic agent management; Multi-Agent Pods, modular agents utilizing advanced algorithms for collaborative task resolution; and the Forge SDK, enabling model deployment and training in a containerized environment. Seraphnet integrates both on-chain and off-chain data, ensuring accurate and transparent AI operations.

Seraphnet AI

seraphnet-ai

A decentralized platform for ideologically-transparent generative AI applications with a focus on privacy and unbiased outputs.

AgentVerse is an open-source framework designed to facilitate the deployment of multiple Large Language Model (LLM)-based agents across various applications. It provides two primary frameworks: task-solving, which assembles multiple agents into an automatic multi-agent system to collaboratively accomplish tasks, and simulation, which allows users to set up custom environments to observe behaviors among multiple agents. AgentVerse aims to enhance multi-agent collaboration and explore emergent behaviors in agents.

AgentVerse

agentverse

An open-source framework for deploying multiple LLM-based agents in task-solving and simulation applications.

Vessium is a platform that enables businesses to automate their operations by converting natural language descriptions into multi-agent workflows. Users can describe their business processes, and Vessium generates corresponding workflows that can be fine-tuned through conversation. The platform features a visual flow builder, human-in-the-loop approval gates, and seamless integration with various tools, allowing for customizable and efficient automation.

Vessium

vessium

Transforms business operations into agentic workflows using natural language prompts.

Langroid is an open-source Python framework designed to streamline the development of applications powered by Large Language Models (LLMs). It introduces a multi-agent programming paradigm, treating agents as first-class citizens to manage multiple LLM conversations, each responsible for different aspects of a task. This approach facilitates efficient collaboration through message exchange, enabling developers to build complex, intelligent applications with ease. Langroid supports integration with various LLMs, vector databases, and function-calling tools, offering a modular and extensible architecture for AI application development.

Langroid

langroid

An open-source Python framework that simplifies LLM application development using a multi-agent programming paradigm.

ChatArena is an open-source Python framework designed to facilitate the development of multi-agent language game environments for large language models (LLMs). It provides a flexible structure for defining multiple players, environments, and their interactions, enabling researchers and developers to explore and enhance AI communication and collaboration skills. With user-friendly interfaces, including both web UI and command-line options, ChatArena supports the creation of diverse language game scenarios, such as conversational simulations, educational settings, and social deduction games.

ChatArena

chatarena

An open-source framework for creating multi-agent language game environments to enhance AI communication and collaboration capabilities.

LangGraph is an open-source library designed to facilitate the development of stateful, multi-actor applications utilizing large language models (LLMs). It enables the creation of complex agent and multi-agent workflows, supporting features such as cycles, controllability, and persistence. LangGraph allows developers to define flows involving cycles, essential for most agentic architectures, and provides fine-grained control over both the flow and state of applications. Additionally, it includes built-in persistence, enabling advanced human-in-the-loop and memory features, making it suitable for creating reliable, fault-tolerant agent-based systems.

LangGraph

langgraph

An open-source AI framework for building stateful, multi-actor applications with large language models (LLMs).

Multi-GPT is an experimental open-source project that aims to create a fully autonomous system by enabling multiple specialized GPT-4 agents, referred to as 'expertGPTs,' to collaborate on complex tasks. Each agent possesses its own short-term and long-term memory and can communicate with others to share information and coordinate actions. This multi-agent approach leverages the strengths of individual agents to achieve a collective intelligence capable of performing intricate operations without human intervention.

Multi-GPT

multi-gpt

An experimental open-source system where multiple specialized GPT-4 agents collaborate to autonomously accomplish complex tasks.

The Multi-Agent Orchestrator is an open-source framework developed by AWS Labs, designed to manage multiple AI agents and handle complex conversations efficiently. It intelligently routes user queries to the most suitable agent based on context and content, maintaining coherent dialogues across multiple domains. With dual language support in Python and TypeScript, it offers pre-built components for quick deployment and allows easy integration of custom agents and conversation message storage solutions. Key features include intelligent intent classification, flexible agent responses supporting both streaming and non-streaming interactions, and seamless coordination of multiple AI agents within a single system.

Multi-Agent Orchestrator

multi-agent-orchestrator

A flexible and powerful open-source framework for managing multiple AI agents and handling complex conversations.

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