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35 个仓库

Awesome GitHub RepositoriesTask Decompositions

Methods for breaking down complex objectives into executable steps.

Distinguishing note: Focuses on the logic of task breakdown rather than the execution itself.

Explore 35 awesome GitHub repositories matching artificial intelligence & ml · Task Decompositions. Refine with filters or upvote what's useful.

Awesome Task Decompositions GitHub Repositories

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  • significant-gravitas/auto-gptSignificant-Gravitas 的头像

    Significant-Gravitas/Auto-GPT

    184,987在 GitHub 上查看↗

    Auto-GPT is an autonomous agent framework that uses large language models to decompose complex goals and execute multi-step tasks without human intervention. It functions as a workflow automation tool that chains language model tasks and manages memory to achieve specific objectives. The project features a visual agent designer that allows users to define behaviors and goals by connecting functional blocks through a graphical interface. It employs a vector database memory system to recall information across different sessions and a sliding-window buffer for immediate short-term context. The

    Decomposes complex high-level objectives into smaller, executable sub-tasks through a self-prompting reasoning loop.

    Python
    在 GitHub 上查看↗184,987
  • microsoft/ai-agents-for-beginnersmicrosoft 的头像

    microsoft/ai-agents-for-beginners

    67,369在 GitHub 上查看↗

    This project is a structured educational resource and technical guide for designing and implementing autonomous systems using large language models. It provides a comprehensive curriculum and code samples focused on agentic design patterns, autonomous development, and the creation of systems capable of planning and executing multi-step tasks. The resource details the implementation of agentic retrieval-augmented generation, where models autonomously plan and refine data searches. It covers a wide array of orchestrators and design patterns, including metacognitive reflection for self-correctin

    Provides methods for breaking down complex objectives into smaller, executable steps.

    Jupyter Notebookagentic-aiagentic-frameworkagentic-rag
    在 GitHub 上查看↗67,369
  • ruvnet/rufloruvnet 的头像

    ruvnet/ruflo

    61,524在 GitHub 上查看↗

    Ruflo is an AI agent orchestration platform and workflow automation tool designed to decompose high-level goals into executable action plans. It functions as a manager for multi-agent swarms, organizing autonomous entities into collaborative topologies that utilize shared consensus to complete complex tasks. The framework distinguishes itself through a retrieval-augmented generation layer and knowledge graphs for reasoning over linked data. It incorporates a trajectory-based learning loop that analyzes previous execution paths to refine cognitive patterns and improve future reasoning accuracy

    Uses LLMs to recursively break high-level objectives into actionable sub-tasks that update as state changes.

    TypeScript
    在 GitHub 上查看↗61,524
  • block/gooseblock 的头像

    block/goose

    49,564在 GitHub 上查看↗

    Goose is an extensible agentic AI platform designed for autonomous task orchestration and developer-centric assistance. It provides a workflow engine that manages complex, multi-step objectives by delegating tasks to specialized subagents, all while maintaining stateful session continuity. The system is built to integrate directly into terminal and coding environments, allowing for automated file manipulation and context-aware interaction. The platform distinguishes itself through a secure, sandboxed runtime environment that enforces granular permission controls and policy-driven guardrails.

    Breaks down large, multi-step objectives into smaller, manageable tasks that the agent can execute sequentially or in parallel.

    Rustmcp
    在 GitHub 上查看↗49,564
  • anthropics/anthropic-cookbookanthropics 的头像

    anthropics/anthropic-cookbook

    45,984在 GitHub 上查看↗

    This repository is a collection of guides, notebooks, and recipes for implementing advanced prompting techniques and workflow patterns with large language models. It serves as a prompt engineering guide, an evaluation suite for scoring prompt quality, and a framework for orchestrating agents and integrating external tools. The project provides implementation patterns for building applications with Claude, specifically focusing on coordinating multiple models to split complex tasks between high-reasoning and high-efficiency agents. It includes technical demonstrations for multimodal data proce

    Implements patterns for using LLMs to recursively break high-level objectives into actionable sub-tasks for multi-agent coordination.

    Jupyter Notebook
    在 GitHub 上查看↗45,984
  • reworkd/agentgptreworkd 的头像

    reworkd/AgentGPT

    36,194在 GitHub 上查看↗

    AgentGPT is a browser-based platform for deploying autonomous AI agents. It serves as a web-based orchestrator and self-hosted framework that allows users to configure agents that decompose high-level goals into smaller, actionable tasks for iterative execution. The system manages the full lifecycle of autonomous agents, from defining behaviors and parameters to overseeing goal-oriented task automation. It enables the deployment of agents that use a recursive loop of planning and analysis to reach a desired outcome. The platform includes a command line interface for bootstrapping the project

    Decomposes complex objectives into smaller, actionable sub-tasks using iterative LLM prompting.

    TypeScriptagentagentgptagents
    在 GitHub 上查看↗36,194
  • virattt/dextervirattt 的头像

    virattt/dexter

    27,085在 GitHub 上查看↗

    Dexter is an autonomous research platform designed to decompose complex inquiries into structured, multi-step workflows. It functions as an agent orchestration system that utilizes iterative tool-calling loops and language models to gather data, perform analysis, and validate findings against internal criteria to ensure accuracy. The platform distinguishes itself through its specialized focus on financial research and messaging integration. It autonomously interprets real-time market data, including income statements and regulatory filings, to generate evidence-based insights. By connecting d

    Divides high-level inquiries into logical sequences of steps to ensure systematic data collection and thorough analysis.

    TypeScript
    在 GitHub 上查看↗27,085
  • promtengineer/localgptPromtEngineer 的头像

    PromtEngineer/localGPT

    22,215在 GitHub 上查看↗

    localGPT is a private AI knowledge base and retrieval-augmented generation application. It provides a local document indexer, a hybrid search engine, and an inference interface to enable chatting with private documents and managing a self-hosted information repository without sending data to external servers. The system distinguishes itself through a dual-pass verification pipeline that ensures generated answers are grounded in retrieved sources, accompanied by explicit source attribution. It employs a hybrid retrieval approach combining semantic vector search with keyword matching and rerank

    Breaks complex natural language requests into smaller sub-queries for parallel execution and final synthesis.

    Python
    在 GitHub 上查看↗22,215
  • lissy93/personal-security-checklistLissy93 的头像

    Lissy93/personal-security-checklist

    21,691在 GitHub 上查看↗

    This project provides a comprehensive, modular framework for auditing and hardening personal digital and physical security. It functions as a structured, platform-agnostic knowledge base that breaks down complex security standards into granular, actionable tasks. By utilizing a static documentation architecture, the project ensures that its guidance remains accessible and transparent, allowing users to track their security posture incrementally through a persistent, manual progress-tracking system. The project distinguishes itself by bridging the gap between digital cybersecurity and physical

    Provides structured, step-by-step security hardening instructions to help users implement complex standards incrementally.

    TypeScriptawesomeawesome-listcensorship
    在 GitHub 上查看↗21,691
  • claude-code-best/claude-codeclaude-code-best 的头像

    claude-code-best/claude-code

    20,272在 GitHub 上查看↗

    Claude Code is a command-line interface and multi-agent orchestration framework designed for autonomous software engineering. It enables AI agents to perform codebase modifications, debugging, and Git workflow management while coordinating multiple specialized agents to decompose and execute complex engineering tasks in parallel. The system distinguishes itself through a high degree of isolation and safety, utilizing Git worktrees to create independent working directories for concurrent agents and implementing a tiered permission system that combines user rules, project policies, and OS-level

    Uses LLMs to recursively break high-level objectives into actionable, parallelizable sub-tasks.

    TypeScript
    在 GitHub 上查看↗20,272
  • onyx-dot-app/onyxonyx-dot-app 的头像

    onyx-dot-app/onyx

    17,491在 GitHub 上查看↗

    Onyx is an enterprise-grade AI platform designed for knowledge management, search, and autonomous agent orchestration. It functions as a centralized system that aggregates unstructured organizational data, enabling secure, context-aware retrieval and interaction across internal documents and communication history. By integrating retrieval-augmented generation with multi-model orchestration, the platform provides a unified interface for teams to query internal knowledge bases and execute complex, multi-step business processes. The platform distinguishes itself through a focus on private infras

    Decomposes complex research requests into parallel sub-queries to gather information from diverse sources efficiently.

    Pythonaiai-chatchatgpt
    在 GitHub 上查看↗17,491
  • opencv/cvatopencv 的头像

    opencv/cvat

    16,086在 GitHub 上查看↗

    CVAT 是一个开源计算机视觉标注工具和可视化数据集管理平台。它提供了一个自托管界面,用于标注图像、视频和 3D 数据,以创建视觉 AI 模型的数据集。 该平台具有 AI 辅助数据标注功能,可自动创建掩码和边界框,并利用插件系统连接外部机器学习模型。它包括一个基于共识的质量保证系统,通过比较独立标注来验证标签准确性。 该系统涵盖协作团队管理、通过任务分解进行项目组织以及远程云存储集成。它还提供用于程序化工作流控制以及以行业标准格式导入和导出数据的 REST API。

    Organizes massive datasets into smaller manageable units assigned to specific users for scalable labeling and review.

    Python
    在 GitHub 上查看↗16,086
  • rayon-rs/rayonrayon-rs 的头像

    rayon-rs/rayon

    13,071在 GitHub 上查看↗

    Rayon is a data parallelism library for Rust that provides a framework for converting sequential computations into parallel operations. It enables the transformation of standard data structures and loops into parallel iterators, allowing workloads to be distributed across multiple processor cores. By utilizing a work-stealing scheduler, the library dynamically balances tasks to maximize throughput and minimize execution time. The library distinguishes itself through its focus on safe, scoped task synchronization, which ensures that all spawned operations complete before a scope exits to preve

    Orchestrates complex, recursive task decomposition to process sub-tasks in parallel until reaching a sequential base case.

    Rust
    在 GitHub 上查看↗13,071
  • aigc-audio/audiogptAIGC-Audio 的头像

    AIGC-Audio/AudioGPT

    10,174在 GitHub 上查看↗

    AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural audio pipelines. It functions as a multimodal audio generator and processing system, integrating a collection of pretrained models to handle speech synthesis, sound generation, and audio manipulation. The system is distinguished by its ability to generate audio from diverse inputs, including text and images, and its capacity to produce synchronized talking head videos. It also operates as a neural speech translator, converting spoken language between different tongues while pre

    Employs LLMs to recursively decompose complex audio requests into sequences of actionable sub-tasks.

    Pythonaudiogptmusic
    在 GitHub 上查看↗10,174
  • cloudwego/einocloudwego 的头像

    cloudwego/eino

    9,675在 GitHub 上查看↗

    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

    Uses LLMs to recursively break high-level objectives into a structured, actionable todo list.

    Goaiai-applicationai-framework
    在 GitHub 上查看↗9,675
  • droidrun/droidrundroidrun 的头像

    droidrun/droidrun

    7,715在 GitHub 上查看↗

    Droidrun is a mobile device automation framework that uses large language models to translate natural language commands into executable actions on mobile operating systems. It functions as an agent orchestrator and UI automation engine, providing a reasoning engine that decomposes complex mobile tasks into smaller, manageable steps. The system distinguishes itself through a hierarchical action translation process and the ability to analyze accessibility trees and screenshots to determine the visual layout and current status of mobile applications. It supports execution across both physical ha

    Uses a planning loop to break complex user goals into smaller executable steps via LLMs.

    Pythonai-agentsandroidandroid-automation
    在 GitHub 上查看↗7,715
  • yaoapp/yaoYaoApp 的头像

    YaoApp/yao

    7,544在 GitHub 上查看↗

    Yao is an LLM agent framework and low-code web app builder designed for orchestrating autonomous AI agents. It provides a platform to design, deploy, and coordinate agents with specialized personas that can plan tasks, utilize external tools, and execute multi-stage pipelines. The project distinguishes itself through a Model Context Protocol server for connecting assistants to external binaries and HTTP services, and a gRPC remote execution engine that allows agents to manage remote servers and devices. It includes a model-agnostic provider bridge that supports dynamic switching between vario

    Uses LLMs to recursively break down high-level strategic goals into a sequence of executable sub-tasks.

    Goagentagentic-aiagents
    在 GitHub 上查看↗7,544
  • datawhalechina/so-large-lmdatawhalechina 的头像

    datawhalechina/so-large-lm

    7,400在 GitHub 上查看↗

    This project is a comprehensive educational curriculum and structured learning path covering the full lifecycle of large language models. It provides a guided progression through the theory, architecture, training, and deployment of these models. The curriculum includes specialized guides on transformer architecture, model training tutorials, and frameworks for designing autonomous agents. It also provides dedicated resources for studying model safety and ethics. The material covers a wide range of technical capabilities, including distributed training strategies, parameter-efficient fine-tu

    Explains methods for breaking down complex objectives into a sequence of executable sub-tasks.

    在 GitHub 上查看↗7,400
  • x-plug/mobileagentX-PLUG 的头像

    X-PLUG/MobileAgent

    7,218在 GitHub 上查看↗

    MobileAgent is an LLM-powered mobile automation agent and framework designed to navigate mobile user interfaces and execute multi-step tasks. It functions as a device interface automation system that maps semantic commands to screen coordinates to perform input events across mobile operating systems. The project operates as a cross-app workflow orchestrator, switching between native on-screen interface actions and external API tools to complete sophisticated operations. It includes a visual grounding system that analyzes screenshots and interface metadata to identify elements and validate the

    Uses LLMs to recursively break high-level objectives into actionable sub-tasks for mobile automation.

    Pythonagentandroidapp
    在 GitHub 上查看↗7,218
  • minedojo/voyagerMineDojo 的头像

    MineDojo/Voyager

    6,987在 GitHub 上查看↗

    Voyager is an autonomous embodied agent and lifelong learning framework that uses a large language model to explore virtual environments. It functions as a code-based action controller, translating natural language instructions into executable scripts to interact with its surroundings. The system features an automatic curriculum generator that creates sequences of exploration goals to discover new items and behaviors without human intervention. It maintains a skill library manager that stores learned behaviors as reusable code fragments, which can be composed to execute complex tasks. The fr

    Utilizes LLMs to recursively break high-level objectives into actionable sub-goals and executable skills.

    JavaScriptembodied-learninglarge-language-modelsminecraft
    在 GitHub 上查看↗6,987
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探索子标签

  • Agentic Goal Decomposition1 个子标签The process of using LLMs to recursively break high-level objectives into actionable sub-tasks. **Distinct from Recursive Decomposers:** Distinct from Recursive Decomposers: focuses on LLM-driven planning for agent goals rather than algorithmic parallel execution.
  • Dataset PartitioningDividing massive datasets into smaller, manageable units for distribution among annotation teams. **Distinct from Task Decompositions:** Focuses on dividing data for human labeling, whereas Task Decompositions usually refers to logic or project planning.
  • Recursive DecomposersMechanisms for breaking down complex operations into smaller sub-tasks for parallel execution. **Distinct from Task Decompositions:** Distinct from Task Decompositions: focuses on recursive algorithmic decomposition for parallel execution rather than AI-driven objective planning.
  • SecurityMethodologies for breaking down complex security standards into granular, actionable implementation steps. **Distinct from Task Decompositions:** Distinct from Task Decompositions: focuses specifically on security hardening workflows rather than general task logic.