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406 repositorios

Awesome GitHub RepositoriesAgent Architectures

Design patterns and structural frameworks for building autonomous agents.

Distinguishing note: Focuses on the structural design of agents rather than specific tool implementations.

Explore 406 awesome GitHub repositories matching artificial intelligence & ml · Agent Architectures. Refine with filters or upvote what's useful.

Awesome Agent Architectures GitHub Repositories

Encuentra los mejores repositorios con IA.Buscaremos los repositorios que mejor coincidan usando IA.
  • openclaw/openclawAvatar de openclaw

    openclaw/openclaw

    380,031Ver en GitHub↗

    Openclaw es una plataforma para gestionar entornos de ejecución de agentes, proporcionando la infraestructura para controlar los ciclos de vida de los agentes, el estado de la sesión y la persistencia del espacio de trabajo. Cuenta con una puerta de enlace centralizada que maneja bucles de modelos, invocación de herramientas y eventos de streaming, al tiempo que admite el enrutamiento multi-agente y la gestión de memoria persistente. El sistema está diseñado para normalizar las firmas de ejecución de herramientas y proporcionar una interfaz estandarizada para la compatibilidad entre proveedores. La plataforma incluye amplias herramientas para desarrolladores, como una interfaz de línea de comandos para la gestión del espacio de trabajo, registro de diagnósticos y una arquitectura de plugins que permite el registro de herramientas y capacidades personalizadas. Admite flujos de trabajo automatizados a través de hooks basados en eventos, programación de tareas e integración con servicios externos. La seguridad se gestiona mediante políticas de ejecución, portabilidad de credenciales y flujos de trabajo de aprobación para las acciones de los agentes. La implementación es compatible con instaladores de infraestructura automatizados y helpers de puerta de enlace en contenedores, con utilidades integradas para copias de seguridad y gestión de configuración. El sistema proporciona un formato estructurado para orquestar flujos de trabajo de varios pasos e incluye herramientas especializadas para la automatización del navegador y la aplicación de parches de código estructurados.

    Utilizes a dedicated sub-agent to perform blocking memory retrieval, ensuring context-aware responses.

    TypeScriptaiassistantcrustacean
    Ver en GitHub↗380,031
  • nousresearch/hermes-agentAvatar de NousResearch

    NousResearch/hermes-agent

    195,049Ver en GitHub↗

    Hermes-agent is an autonomous AI agent framework and runtime designed to execute complex tasks and synthesize new skills from execution traces. It includes a provider-agnostic gateway for routing requests across multiple model backends and a serverless runtime that suspends idle agent instances and resumes them on demand across containers and virtual machines. The project provides a desktop automation toolset that controls native GUI workflows on Linux by querying accessibility APIs and injecting input events. It further distinguishes itself with the ability to generate procedural skills from

    Implements persistent storage for retaining context and user information across multiple sessions.

    Pythonaiai-agentai-agents
    Ver en GitHub↗195,049
  • n8n-io/n8nAvatar de n8n-io

    n8n-io/n8n

    192,772Ver en GitHub↗

    n8n is a workflow automation platform that combines a visual interface with code-based extensibility to design, orchestrate, and manage automated processes. It provides a comprehensive suite of tools for data transformation, filtering, and storage, allowing users to build complex logic through conditional branching, looping, and sub-workflow execution. The platform supports both pre-built integration nodes and custom code execution in JavaScript or Python, enabling connectivity with a wide range of external services and APIs. The platform includes a suite of generative AI capabilities, such a

    Powers autonomous agents by triggering complex, logic-driven workflows that respond to dynamic inputs.

    TypeScriptaiapisautomation
    Ver en GitHub↗192,772
  • significant-gravitas/auto-gptAvatar de Significant-Gravitas

    Significant-Gravitas/Auto-GPT

    184,987Ver en GitHub↗

    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

    Implements persistent storage mechanisms to retain interaction data and context across multiple sessions.

    Python
    Ver en GitHub↗184,987
  • sst/opencodeAvatar de sst

    sst/opencode

    175,436Ver en GitHub↗

    OpenCode is an autonomous software developer and LLM coding agent designed to write code and manage development workflows. It functions as an AI development automator that executes multi-step coding tasks and modifies project files to build software automatically from high-level instructions. The system employs a task orchestrator to decompose goals into sequences of tool calls and autonomous execution steps. It features a recursive research loop for conducting deep technical searches and a restricted read-only mode for analyzing and exploring large codebases to plan changes without modifying

    Provides a programmable environment that translates high-level goals into executable code for autonomous system interaction.

    TypeScript
    Ver en GitHub↗175,436
  • f/prompts.chatAvatar de f

    f/prompts.chat

    163,814Ver en GitHub↗

    This platform serves as a centralized management system for organizing, refining, and versioning AI instructions and agent skills. It functions as a repository that enables users to store, categorize, and retrieve structured prompts, ensuring consistent performance across various artificial intelligence models. By integrating with the Model Context Protocol, the system allows external AI assistants and development environments to discover and access these instruction libraries directly. The platform distinguishes itself through its focus on prompt engineering and automated refinement, utilizi

    Executes multi-perspective search behaviors to generate comprehensive answers for complex queries.

    HTMLaiartificial-intelligenceawesome-list
    Ver en GitHub↗163,814
  • anthropics/skillsAvatar de anthropics

    anthropics/skills

    151,506Ver en GitHub↗

    This project provides a standardized framework for extending the functional range of artificial intelligence agents through a registry of modular, declarative instructions. It enables agentic workflow automation by allowing developers to define task-specific behaviors and operational constraints that guide how agents interact with external tools and execute multi-step processes. The system distinguishes itself through a directory-based discovery model and a plugin-registry architecture that facilitates the distribution of specialized workflows. By utilizing a schema-driven specification that

    Governs agent logic by interpreting high-level metadata instructions rather than relying on hard-coded routines.

    Pythonagent-skills
    Ver en GitHub↗151,506
  • hwchase17/langchainAvatar de hwchase17

    hwchase17/langchain

    139,533Ver en GitHub↗

    LangChain is a framework for building applications that chain large language models with external data sources and third-party tools. It serves as an orchestrator for autonomous agents that use language models to plan and execute multi-step tasks, while providing a toolkit for linking interoperable AI components into sequences to prototype complex model behaviors. The project provides a model agnostic integration layer, allowing users to switch between different language model providers using a standardized interface. It also includes tools for observability and evaluation to track the perfor

    Implements persistent memory mechanisms to track agent progress and intermediate data across multi-step workflows.

    Python
    Ver en GitHub↗139,533
  • langchain-ai/langchainAvatar de langchain-ai

    langchain-ai/langchain

    139,458Ver en GitHub↗

    LangChain is an orchestration framework designed for building, managing, and deploying applications powered by large language models. It provides a unified integration layer that normalizes disparate model provider APIs into a consistent set of primitives, enabling developers to build complex, multi-step AI workflows that manage state, memory, and tool execution. The project distinguishes itself through a durable execution runtime that maintains persistent state across long-running processes by checkpointing progress to external storage. It models agent workflows as directed graphs, allowing

    Retains information across long-term intervals by leveraging persistent storage mechanisms for cross-session context.

    Pythonagentsaiai-agents
    Ver en GitHub↗139,458
  • anthropics/claude-codeAvatar de anthropics

    anthropics/claude-code

    132,728Ver en GitHub↗

    Anthropic's terminal-native AI coding agent.

    Maintains a continuous loop of environment observation, logical reasoning, and tool-based action execution to reach specified development goals.

    Pythonaiclideveloper-tools
    Ver en GitHub↗132,728
  • mattpocock/skillsAvatar de mattpocock

    mattpocock/skills

    131,422Ver en GitHub↗

    This project is an AI agent workflow framework and development toolkit designed for AI-driven software engineering. It provides a system of modular instructions, prompt libraries, and standardized routines to orchestrate complex engineering sequences and automate the decomposition of plans into technical tasks. The system differentiates itself through advanced context management and prompt engineering, using state compression and handoff documents to preserve conversation history between different AI sessions. It employs a structured library of prompt skills and high-signal trigger words to e

    Defines discrete agent behaviors as structured prompt files that can be assembled into modular capabilities.

    Shell
    Ver en GitHub↗131,422
  • shubhamsaboo/awesome-llm-appsAvatar de Shubhamsaboo

    Shubhamsaboo/awesome-llm-apps

    114,725Ver en GitHub↗

    This repository serves as a comprehensive collection of resources, templates, and starter code for building artificial intelligence applications. It provides a centralized hub for developers to access practical implementations of common workflows, including retrieval-augmented generation pipelines and autonomous agent loops, alongside educational materials designed to support rapid prototyping and experimentation. The project distinguishes itself by offering a dual focus on technical implementation and critical analysis. It provides a library of lightweight, single-file agents and tutorials f

    Design patterns explore how autonomous architectures provide new forms of leverage and productivity.

    Pythonagentsllmspython
    Ver en GitHub↗114,725
  • lobehub/lobehubAvatar de lobehub

    lobehub/lobehub

    78,736Ver en GitHub↗

    LobeHub is a comprehensive multi-agent orchestration platform designed for building, configuring, and deploying specialized AI agents. It provides a unified chat-based gateway that allows users to manage autonomous agent teams across web, desktop, and mobile environments. By utilizing a framework that supports persistent memory and granular tool integration, the platform enables the execution of complex, multi-step workflows and domain-specific tasks. The platform distinguishes itself through an interactive artifact renderer that injects dynamic, visual UI elements directly into the chat stre

    Aligns multiple autonomous agents into collaborative units capable of executing complex, multi-step workflows.

    TypeScriptagentagent-collaborationagent-harness
    Ver en GitHub↗78,736
  • opendevin/opendevinAvatar de OpenDevin

    OpenDevin/OpenDevin

    77,460Ver en GitHub↗

    OpenDevin is an autonomous software engineering agent and orchestrator designed to execute coding tasks and manage development workflows using large language models. It functions as a centralized control center for managing and switching between various local and cloud artificial intelligence backends. The system utilizes a Docker sandbox environment to isolate autonomous agents in containers, protecting the host filesystem during code execution. It includes an automated engineering workflow tool that integrates with version control and chat services to trigger tasks via webhooks or scheduled

    Provides an autonomous system that uses large language models to execute coding tasks and manage software development workflows.

    Python
    Ver en GitHub↗77,460
  • all-hands-ai/openhandsAvatar de All-Hands-AI

    All-Hands-AI/OpenHands

    77,468Ver en GitHub↗

    OpenHands is an autonomous AI software engineer and coding assistant designed to execute software engineering tasks by interacting directly with codebases and development environments. It functions as a platform for running AI agents that can write code and manage files to automate complex development workflows. The system distinguishes itself through a container-based execution environment that isolates agent actions within a sandboxed Linux environment. It employs an autonomous agent loop of observation, planning, and action, supported by a standardized communication protocol that allows it

    Provides an autonomous AI agent capable of navigating codebases and implementing software features independently.

    Python
    Ver en GitHub↗77,468
  • openhands/openhandsAvatar de OpenHands

    OpenHands/OpenHands

    77,330Ver en GitHub↗

    OpenHands is an autonomous agent framework designed for software engineering workflows. It provides a modular platform for orchestrating AI agents that reason, plan, and execute tasks within isolated, containerized development environments. By integrating with standard version control and development tools, the system enables agents to autonomously navigate codebases, implement features, and resolve issues through iterative reasoning and tool execution. The platform distinguishes itself through a model-agnostic orchestrator that connects diverse language models to a unified tool registry. It

    Allows primary agents to synchronously delegate sub-tasks to specialized agents and block until completion.

    Pythonagentartificial-intelligencechatgpt
    Ver en GitHub↗77,330
  • pewdiepie-archdaemon/odysseusAvatar de pewdiepie-archdaemon

    pewdiepie-archdaemon/odysseus

    72,184Ver en GitHub↗

    Odysseus is a self-hosted AI workspace and autonomous agent framework designed for deploying and managing large language models. It serves as a centralized platform for orchestrating agentic tasks, utilizing a model context protocol server to connect AI models to external system utilities, browser automation, and local hardware. The system distinguishes itself through a combination of retrieval-augmented generation and a RAG knowledge base, using vector stores and local embeddings to provide persistent semantic memory. It further integrates AI-driven communication management to triage email i

    Uses vector stores and embeddings to retrieve relevant semantic context and tools for AI agents.

    Python
    Ver en GitHub↗72,184
  • bytedance/deer-flowAvatar de bytedance

    bytedance/deer-flow

    71,310Ver en GitHub↗

    Deer-flow is an autonomous agent orchestration platform designed to manage multi-step workflows where AI agents reason, plan, and execute tasks. It functions as a development framework for building agents that utilize various large language models to solve complex problems through structured, sequential, and parallel reasoning. The platform distinguishes itself through a secure, sandboxed execution engine that isolates generated code and system operations from the host environment. This architecture allows agents to safely test and validate solutions within ephemeral containers, ensuring that

    Provides a collaborative workspace for monitoring and managing the operational lifecycle and resource constraints of agent execution environments.

    Pythonagentagenticagentic-framework
    Ver en GitHub↗71,310
  • paperclipai/paperclipAvatar de paperclipai

    paperclipai/paperclip

    70,619Ver en GitHub↗

    Paperclip is an LLM agent orchestration platform and governance suite designed to coordinate teams of autonomous AI agents. It provides a management plane for defining organizational hierarchies, assigning roles, and aligning individual agent tasks with a structured mission tree to ensure work maps to business objectives. The project distinguishes itself through a specialized agent skill registry and workspace manager. It allows for the discovery and injection of reusable workflows into agent runtimes without retraining and provides isolated, sandboxed execution environments with persistent s

    Provides isolated sandboxed execution environments with persistent state and dedicated file systems for agents.

    TypeScript
    Ver en GitHub↗70,619
  • foundationagents/metagptAvatar de FoundationAgents

    FoundationAgents/MetaGPT

    68,844Ver en GitHub↗

    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

    Coordinates complex, multi-step technical processes by delegating specific tasks to autonomous agents that collaborate to achieve project goals.

    Pythonagentgptllm
    Ver en GitHub↗68,844
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  1. Home
  2. Artificial Intelligence & ML
  3. Agent Architectures

Explorar subetiquetas

  • AI Agents7 sub-etiquetasAutonomous software entities capable of perceiving their environment, reasoning, and executing actions to achieve specific user-defined goals.
  • Abstraction InterfacesUnified interfaces that wrap heterogeneous agent implementations to allow optimization tools to interact with diverse architectures without source code modifications. **Distinct from Agent Architectures:** Distinct from general agent architectures: focuses on the abstraction layer for optimization tool compatibility rather than the agent's internal design.
  • Agent Configuration SerializationMethods for converting agent settings and state into portable formats for storage or transmission.
  • Agent Deployment Frameworks1 sub-etiquetaInfrastructure and platforms designed to host, manage, and execute autonomous agents in remote or distributed computing environments.
  • Agent Reasoning Configurations1 sub-etiquetaSettings for controlling how agents process information, including retrieval-augmented generation and internal model selection.
  • Event-Driven Agent LoopsControl cycles that monitor task status and trigger actions based on event-driven state changes.
  • Memory Management Systems1 sub-etiquetaSystems that maintain persistent context and shared knowledge to support information recall across agent workflows.
  • Orchestration Engines2 sub-etiquetasRuntime systems that coordinate multi-agent workflows, task scheduling, and complex multi-step execution logic.
  • Planning Strategies2 sub-etiquetasTechniques for enabling AI agents to decompose and sequence complex tasks.
  • Tool-Based Architectures1 sub-etiquetaAgent architectures where tasks are executed by invoking typed, schema-validated tools with lifecycle hooks and cancellation. **Distinct from Agent Architectures:** Distinct from Agent Architectures: focuses specifically on tool-based execution with typed schemas and lifecycle hooks, not general agent structural patterns.
  • Tooling and Integration Interfaces6 sub-etiquetasMechanisms and bridges that enable agents to interact with external functions, APIs, and secure execution environments.
  • User Intent Modeling3 sub-etiquetasTechniques for inferring user goals and preferences through long-term state tracking.