23 مستودعات
Systems for sequencing and coordinating multiple specialized AI agents to complete complex multi-step tasks.
Distinct from End-to-End Pipelines: None of the candidates address AI agent coordination; they focus on encryption, type safety, audio pipelines, or software testing.
Explore 23 awesome GitHub repositories matching artificial intelligence & ml · Agent Workflow Orchestrations. Refine with filters or upvote what's useful.
This project is a Docker educational resource and a collection of practical examples designed for learning containerization technologies. It serves as a guide for understanding container fundamentals, including the creation and management of custom images and the use of registries. The repository provides specialized references for container security hardening, such as managing kernel privileges and implementing supply chain security. It also includes tutorials for multi-container orchestration and a DevOps guide focused on CI/CD automation and image optimization. The material covers a broad
Coordinates tasks across specialized AI agents using containerized orchestration and communication protocols.
Owl is a framework for agentic workflow automation and multi-agent orchestration. It functions as a system for coordinating autonomous large language model agents to decompose and execute complex tasks through shared communication and collaborative planning. The project distinguishes itself through a multi-modal toolset for processing images, audio, and video, alongside a synthetic data generator that produces domain-specific datasets using self-instruct and verifier loops. It further incorporates a retrieval-augmented generation pipeline framework that integrates long-term memory and real-ti
Coordinates specialized agents to automate complex tasks through collaborative learning and sequenced execution.
This project is a retrieval-augmented generation application designed to answer questions from uploaded PDF documents. It functions as a document question-answering engine and a streaming AI chat interface that provides responses backed by specific source citations. The system utilizes a state-machine workflow orchestrator to coordinate multi-step document ingestion and retrieval pipelines. This orchestration allows for step-by-step visualization and debugging of the process as documents are parsed and processed. The application manages the full lifecycle of document interaction, including P
Uses LangGraph to manage the complex state transitions involved in PDF ingestion and retrieval workflows.
This project is an AI agent workflow orchestrator and automated software lifecycle manager designed to sequence specialized AI personas for end-to-end software development. It serves as a prompt engineering library and a full-stack development toolkit that guides the process from initial discovery and specification through to deployment and code review. The system features a context management framework that utilizes progressive loading and routing tables to fetch reference files on-demand, reducing token consumption within the model context window. It employs a definition-based routing syste
Capability to sequence multiple specialized personas to execute complex end-to-end tasks like feature development or security hardening.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Runs multiple agents in sequential chains, iterative loops, or concurrent execution to create processing pipelines.
mcp-agent is a framework for building AI agents that integrate with Model Context Protocol servers to execute tools and access data. It functions as a multi-agent orchestrator and protocol-compliant server, enabling the creation of agents that can discover and invoke tools from connected external servers. The project distinguishes itself through a durable workflow engine that supports long-running tasks capable of pausing, resuming, and surviving restarts. It implements complex orchestration patterns, including iterative evaluator-optimizer loops, hierarchical workflow nesting, and specialist
Coordinates multiple specialized agents and functions using planners and intent classifiers to complete complex tasks.
The agent-framework is an LLM agent orchestration framework and multi-agent workflow engine designed for building autonomous AI agents. It provides a tool integration layer for binding external functions, APIs, and sandboxed code as executable tools for language models. The framework distinguishes itself through a graph-based system for designing sequential and parallel task flows, featuring state management and checkpointing for long-running processes. It implements comprehensive conversational state management and an observability suite that uses telemetry to trace execution flows and monit
Sequences specialized AI agents and functions via graph-based paths with type-safe routing.
OpenGPTs is a platform for building, deploying, and managing customizable AI assistants. It serves as an orchestrator that allows for the configuration of large language models with specific personas, cognitive architectures, and tool integrations. The system provides a complete lifecycle manager for AI agents, enabling the drafting of configurations, testing within sandboxes, and publishing assistants for public or internal distribution. It integrates a knowledge base interface using retrieval-augmented generation to attach documents to bots for context-aware responses. The platform covers
Implements a framework for managing AI assistants using LangGraph's state-machine based orchestration.
هذا المشروع هو تطبيق لوكيل محادثة ونظام تنسيق مصمم لبناء وتنفيذ سير عمل معقد لنماذج اللغة. يعمل كمساعد للتوثيق الفني يستخدم التوليد المعزز بالاسترجاع (RAG) لتجميع إجابات قائمة على الأدلة من قواعد المعرفة الرسمية. يستخدم النظام إدارة حالة قائمة على الرسوم البيانية للتعامل مع العمليات الوكيلة طويلة الأمد والدورات. ويدمج أدوات المراقبة لتتبع الطلبات وتقييم المخرجات، إلى جانب تكامل استدعاء الأدوات لإجراء عمليات البحث والتحقق من روابط المراجع الخارجية. تتضمن البنية حواجز حماية للاستعلام لتصفية الطلبات الخارجة عن الموضوع والتحقق من الروابط غير المتزامن لضمان بقاء التوثيق المذكور نشطاً. كما يدعم استضافة سير العمل القائم على الحالة للنماذج الأولية والتعاون في الأنماط الوكيلة.
Uses the LangGraph framework to coordinate complex stateful AI agent workflows.
This project is a conversational assistant and retrieval-augmented generation system designed to provide technical answers from official documentation and support knowledge bases. It implements a retrieval architecture that routes queries through specialized tools and utilizes a model abstraction layer to switch between different chat and embedding providers without modifying core integration code. The system employs a graph-based state machine for durable agent execution, enabling state persistence and human-in-the-loop interactions. It features an agentic middleware framework that allows fo
Uses LangGraph to orchestrate agent workflows with state persistence and human-in-the-loop interactions.
FinRobot is an AI-powered financial analysis framework that coordinates multiple specialized agents to automate equity research, financial analysis, and investment risk assessment. At its core, it functions as a multi-agent orchestration system where a director and task manager allocate financial tasks to the most suitable large language models based on performance metrics and task requirements. The framework distinguishes itself through its ability to execute complex multi-step financial workflows by routing tasks through perception, reasoning, and action modules. It generates professional e
Coordinates specialized AI agents—market forecasters, document analysts, and trading strategists—to execute complex financial tasks.
PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo
Orchestrates multiple agents with handoffs, planning, parallel execution, and conditional routing for complex goals.
Ruoyi AI is a multi-agent orchestration platform that coordinates specialized AI agents through a supervisor-based delegation pattern, allowing complex requests to be broken into subtasks that are assigned, executed, and merged under centralized control. It provides a unified abstraction layer that connects multiple AI model providers behind a single interface, so switching between providers requires no application code changes. The platform also includes a retrieval-augmented generation engine that indexes internal documents into vector stores and retrieves relevant context at query time to g
Defines and executes sequences of AI agent tasks that collaborate to complete processes, routing subtasks and merging results.
Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk
Coordinates multiple independent agents in code to create complex Plan-and-Execute workflows.
Yuxi-Know is an LLM agent orchestration platform that coordinates multiple AI agents through graph-based workflows to decompose and execute complex reasoning tasks. It functions as a multi-tenant AI workspace with an agentic chat interface, combining retrieval-augmented generation with knowledge graph management for enterprise document processing and retrieval. The platform distinguishes itself through graph-based agent orchestration, where directed acyclic graphs define execution dependencies between reasoning steps, enabling parallel or sequential task decomposition. It provides multi-tenan
Coordinates multiple LLM agents through graph-based workflows for complex reasoning and task execution.
Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran
Sequences and coordinates multiple specialized agents via a central agency and entry-point agent.
cc-wf-studio is a suite of tools for visually designing, refining, and exporting AI agent workflows. It provides a visual automation orchestrator and an LLM agent workflow designer that allow users to create multi-agent sequences and tool integrations using a drag-and-drop canvas. The project features a converter that transforms these visual agent designs into markdown-formatted commands and skills for use with artificial intelligence coding assistants. It also includes an AI-driven workflow editor that enables the modification of agent logic through natural language conversations. The platf
Sequences and coordinates multiple specialized AI agents to complete complex multi-step tasks.
LazyLLM is a multi-agent framework and orchestration engine designed for building complex AI applications. It provides a system for chaining large language models into sequential or parallel pipelines, utilizing a tool registry to convert standard functions into discoverable tools that models can invoke via reasoning. The project features an application deployment kit that enables hosting model workflows as web services with integrated chat interfaces and API gateways. It includes an infrastructure abstraction layer that allows users to switch between bare-metal servers, clusters, and public
Provides a system for sequencing and coordinating multiple specialized AI agents to complete complex multi-step tasks.
DeepAnalyze is an autonomous data science agent and research pipeline designed to transform raw datasets into comprehensive analysis reports. It operates by generating and executing Python code to perform data preparation, modeling, and visualization. The system utilizes a secure, containerized execution environment to run generated scripts in isolation from the host system. It includes a benchmarking tool to evaluate the accuracy and performance of large language models against standardized data science tasks and a standardized API gateway for managing model completions and file uploads. Th
Coordinates sequences of specialized AI agents to automate a complete research and data analysis workflow.
This project is a containerized local AI infrastructure stack designed to deploy large language models and vector databases on private hardware. It functions as an orchestration platform that combines AI runners, knowledge graphs, and a visual workflow builder for creating agentic chatflows and automating tasks via tool integration. The platform distinguishes itself through a low-code approach to agent orchestration, utilizing a visual interface to design complex sequences and connect agents to external tools and search engines. It includes a dedicated local observability stack to track promp
Sequences and coordinates multiple specialized AI agents to complete complex multi-step tasks.