Awesome GitHub RepositoriesMulti-Agent Orchestration Frameworks

ECC 是一个 LLM 智能体编排框架和跨平台 AI 工具套件，旨在协调多模型工作流。它提供了一个用于管理专业智能体角色、可复用技能和结构化规划的系统，以在不同的 AI 驱动代码编辑器中执行复杂的软件开发任务。 该项目作为模型上下文协议（Model Context Protocol）管理器脱颖而出，提供了一个配置层来集成外部服务器并审计工具执行。它进一步实现了一个智能体安全沙箱，限制敏感文件访问并扫描密钥泄露，以保护自主工作流。 该框架涵盖了广泛的能力领域，包括带有测试驱动开发护栏的 AI 编码工作流自动化、通过智能路由实现模型成本优化以及状态隔离的内存管理。它还包括用于强制执行特定语言编码标准和管理跨各种集成开发环境的智能体行为的工具。 该系统通过命令行界面进行管理，该界面处理工具安装、配置修复和工具预设的部署。

MetaGPT is an agentic workflow engine and multi-agent orchestration framework designed to automate complex software engineering and data analysis tasks. It functions as an automated software factory that transforms high-level natural language requirements into functional web applications, technical documentation, and production-ready code. By utilizing a runtime environment that manages the lifecycle of specialized agents, the platform bridges the gap between user intent and finished software components. The system distinguishes itself through role-based agent orchestration and dynamic task d

This framework provides a development environment for building collaborative systems where autonomous agents interact to solve complex tasks through conversational workflows. It functions as a conversational workflow engine and event-driven runtime, coordinating multi-step processes by translating high-level goals into structured dialogue sequences between specialized agents. The system distinguishes itself through its message-passing orchestration, which manages state transitions and task delegation between independent participants. It supports dynamic conversation state management to provid

CrewAI is a multi-agent orchestration framework and autonomous agent workflow engine. It provides a system for coordinating autonomous AI agents with specific roles and goals to solve complex tasks through collaborative intelligence. The framework distinguishes itself through a collaborative AI agent system that enables multiple language model instances to share intelligence and execute multi-step objectives via role-playing. It incorporates human-in-the-loop mechanisms, allowing for manual review checkpoints to validate decisions and refine outcomes within autonomous execution paths. The pl

This project serves as an educational resource and technical guide for building production-ready intelligent systems. It provides a collection of hands-on tutorials, blueprints, and documentation focused on the development of applications powered by large language models, autonomous agentic workflows, and retrieval-augmented generation. The repository distinguishes itself by offering structured implementations for multi-agent orchestration and standardized communication protocols. It enables developers to integrate external tools and data sources into their systems, ensuring interoperability

This project is a Python framework for building autonomous, event-driven agent systems. It provides a unified runtime for orchestrating multi-agent workflows, managing persistent conversation state, and executing code within secure, isolated sandbox environments. The framework is designed to handle complex task delegation, allowing agents to invoke other agents as tools while maintaining context across multi-turn interactions. The framework distinguishes itself through its deep integration with the Model Context Protocol, enabling agents to connect to external data sources and remote services

Agentscope is a comprehensive toolkit for developing and orchestrating autonomous multi-agent systems. It provides a unified framework for building agents that can reason, execute tools, and manage memory, enabling the creation of complex, collaborative workflows where multiple specialized agents interact to solve multi-step objectives. The platform distinguishes itself through a robust orchestration engine that supports both sequential and concurrent agent pipelines. It utilizes a centralized event bus for real-time telemetry, allowing developers to track agent reasoning, tool usage, and sys

gbrain is an agent framework and retrieval-augmented generation system that combines a durable task queue, a git-synced vector store, and a knowledge graph engine. It provides a foundation for building AI agents that interact with structured knowledge bases using the Model Context Protocol. The system synchronizes markdown files from a git repository into a database for high-performance semantic retrieval and creates typed edges between data pages by extracting entity references and wikilinks. It uses a database-backed queue to execute persistent background jobs and tool loops, ensuring relia

Generative Agents is a computational platform for simulating autonomous agents that exhibit human-like social behaviors and decision-making processes. The system functions as a multi-agent simulator where individual participants operate within a virtual environment, driven by large language models to process observations and generate natural language actions. The framework distinguishes itself through a hierarchical memory system that allows agents to store, retrieve, and synthesize past experiences into higher-level insights. This architecture supports the development of complex social dynam

Qwen-code is an AI-powered development framework designed for orchestrating intelligent coding agents within terminal and IDE environments. It provides a comprehensive infrastructure for automating software maintenance, code generation, and complex refactoring tasks by managing multi-agent workflows and persistent session states. The system is built to handle both interactive development and automated background processes, ensuring that agents can execute shell commands and file operations safely within isolated, sandboxed environments. What distinguishes this project is its focus on granular

This project is a comprehensive framework for building and managing autonomous agent systems. It provides a unified architecture for orchestrating multi-agent societies, where specialized agents collaborate through roleplay to decompose and solve complex tasks. The system integrates language models with external environments, enabling agents to perform real-world actions through a standardized tool-calling abstraction layer. The framework distinguishes itself through its focus on iterative reasoning and data reliability. It employs automated feedback loops to refine agent outputs and self-eva

This project functions as a Model Context Protocol server and a multi-agent orchestration framework designed to bridge large language models with external data sources and specialized engineering tools. It provides a structured environment for automating software development workflows, enabling models to interact directly with codebases and remote services to perform complex tasks. The system distinguishes itself through a multi-agent orchestration layer that coordinates autonomous assistants to manage shared objectives and multi-step workflows. By utilizing structured task decomposition and

Hive is an artificial intelligence workflow automation engine and development platform designed for building and deploying autonomous agents. It provides a framework for orchestrating complex, multi-step business processes by coordinating tasks across multiple specialized agents using directed graph structures. The platform distinguishes itself through a focus on production-grade reliability and state management. It maintains persistent execution context and conversation history on disk, enabling crash recovery and continuity for long-running automated sessions. Furthermore, it incorporates a

This project is a collection of architectural templates and design patterns for building autonomous AI agents. It provides a framework for transitioning from simple prompt-response loops to goal-oriented systems that utilize structural patterns to increase autonomy and improve the reliability of complex task completion. The framework focuses on reasoning orchestration, specifically through the implementation of reflection and self-correction cycles. It enables the coordination of specialized agents via task delegation and state sharing to solve complex problems. The architectural surface cov

This project is a comprehensive AI infrastructure that combines an LLM agent orchestration framework, an autonomous research system, and a local AI environment. It centers on the creation of a personal knowledge graph and a programmatic prompt engineering library to provide long-term memory and optimized reasoning for artificial intelligence tasks. The system is distinguished by its ability to compose multi-agent teams using specialized personas and deterministic skills to execute complex workflows. It features an autonomous research pipeline capable of deep investigations and adversarial ana

XianyuAutoAgent is a multi-agent orchestration framework and large language model chatbot designed to automate customer service and sales interactions on a marketplace platform. It functions as a system of specialized AI entities, including context-aware conversational agents and automated price negotiation bots, to manage peer-to-peer conversational commerce. The framework distinguishes itself through an intent-based routing mechanism that dispatches queries to specialized agents and utilizes a multi-expert collaborative decision process to determine response strategies. It features dedicate

Agent Squad 是一个 LLM 多智能体编排框架，旨在协调专业智能体以解决复杂任务。它作为一个管理智能体团队和主管的系统，利用主管主导的编排模型将大问题分解为可管理的步骤。 该框架的独特之处在于结合了基于意图的查询路由和人机协作自动化。它采用分层路由系统将请求定向到最合适的智能体或模型，同时集成异步消息队列，将复杂案例路由给人工操作员进行干预。 该系统涵盖了全面的对话状态管理能力，包括用于在多轮对话中保持连贯性的多层记忆。它还提供了一个工具集成层，将自然语言转换为结构化格式，以便将智能体连接到外部 API、数据库和知识库。 该架构支持实时响应流和混合通信模式，以处理即时消息和异步交互。

Agent Squad is a multi-agent system orchestrator and language model agent orchestration framework. It serves as an AI workflow automation engine and tool integration layer designed to coordinate teams of specialized agents to solve complex tasks through routing, parallel execution, and state management. The project is distinguished by its ability to dynamically compose purpose-specific agents on-demand and route requests based on intent, language, or domain expertise. It supports advanced coordination patterns, including parallel subtask distribution, sequential task pipelines, and the abilit

Swarms 是一个多代理编排框架和自主代理工具包，旨在协调大语言模型代理。它作为一个用于管理代理关系的工作流引擎，提供了构建具有集成内存、工具调用能力和推理循环的自主代理的基础设施。 该框架的特色在于其多代理共识系统，利用投票、对抗性辩论和裁判代理来合成高质量的响应。它支持多种协作模式，包括导演-工作者层次结构、专家合成以及基于自然语言描述的自动化群体架构生成。 该系统涵盖了广泛的运营功能，包括通过领域特定语言进行基于图和顺序的工作流编排、针对不同模型提供商的统一接口，以及与 Model Context Protocol 的集成以实现动态工具发现。它还包括对检索增强生成、状态持久内存以及将代理功能公开为 Web 服务的能力的支持。 该项目提供用于代理管理的命令行界面，并支持通过 YAML 和模块化 markdown 技能文件进行配置。

Open Multi-Agent is a TypeScript framework for multi-agent orchestration that decomposes natural language goals into a runtime-generated directed acyclic graph of tasks. It functions as a task orchestrator and workflow state manager, coordinating multiple AI models to execute parallel and sequential operations. The framework is distinguished by a proposer-judge consensus protocol used to validate agent outputs through a quorum of agreement. It employs provider-agnostic model routing to assign specific models to tasks based on roles or execution phases and utilizes state-based workflow checkpo