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align=\"center\"\u003e\n\n# 🤖 AutoGPT\n\n[![Work In Progress](https://img.shields.io/badge/Work%20In%20Progress-red)](https://github.com/wiseaidev)\n[![made-with-rust](https://img.shields.io/badge/Made%20with-Rust-1f425f.svg?logo=rust\u0026logoColor=white)](https://www.rust-lang.org/)\n[![Rust](https://img.shields.io/badge/Rust-1.75%2B-blue.svg)](https://www.rust-lang.org)\n[![License](https://img.shields.io/badge/License-MIT-brightgreen.svg)](LICENSE)\n[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://github.com/wiseaidev)\n[![Jupyter Notebook](https://img.shields.io/badge/Jupyter-Notebook-blue.svg?logo=Jupyter\u0026logoColor=orange)](https://jupyter.org/)\n\n[![Share On Reddit](https://img.shields.io/badge/share%20on-reddit-red?logo=reddit)](https://reddit.com/submit?url=https://github.com/kevin-rs/autogpt\u0026title=World%27s%20First%2C%20Multimodal%2C%20Zero%20Shot%2C%20Most%20General%2C%20Most%20Capable%2C%20Blazingly%20Fast%2C%20and%20Extremely%20Flexible%20Pure%20Rust%20AI%20Agentic%20Framework.)\n[![Share On Ycombinator](https://img.shields.io/badge/share%20on-hacker%20news-orange?logo=ycombinator)](https://news.ycombinator.com/submitlink?u=https://github.com/kevin-rs/autogpt\u0026t=World%27s%20First%2C%20Multimodal%2C%20Zero%20Shot%2C%20Most%20General%2C%20Most%20Capable%2C%20Blazingly%20Fast%2C%20and%20Extremely%20Flexible%20Pure%20Rust%20AI%20Agentic%20Framework.)\n[![Share On 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Linkedin](https://img.shields.io/badge/share%20on-linkedin-3949AB?logo=linkedin)](https://www.linkedin.com/shareArticle?url=https://github.com/kevin-rs/autogpt\u0026title=World%27s%20First%2C%20Multimodal%2C%20Zero%20Shot%2C%20Most%20General%2C%20Most%20Capable%2C%20Blazingly%20Fast%2C%20and%20Extremely%20Flexible%20Pure%20Rust%20AI%20Agentic%20Framework.)\n\n[![CircleCI](https://dl.circleci.com/status-badge/img/gh/kevin-rs/autogpt/tree/main.svg?style=svg\u0026circle-token=CCIPRJ_PifnErxs6Ze2XWpjmUeRV1_4e84825e0f6a366716a77c2dbbe93c3bd3e507fa)](https://dl.circleci.com/status-badge/redirect/gh/kevin-rs/autogpt/tree/main)\n[![Crates.io Downloads](https://img.shields.io/crates/d/autogpt)](https://crates.io/crates/autogpt)\n[![Github](https://img.shields.io/badge/launch-Github-181717.svg?logo=github\u0026logoColor=white)](./examples/basic.ipynb)\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/kevin-rs/autogpt/main?filepath=examples/basic.ipynb)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/kevin-rs/autogpt/blob/main/examples/basic.ipynb)\n\n![banner](https://github.com/kevin-rs/kevin/assets/62179149/8b54dea8-2231-4509-8c18-10ec414578d2)\n\n| 🏗️  `(Recommended)` | 🐋 | 🐋 |\n| :------: | :--------: | :--------: |\n| [![Crates.io Downloads](https://img.shields.io/crates/d/autogpt)](https://crates.io/crates/autogpt) | [![Docker](https://img.shields.io/docker/pulls/kevinrsdev/autogpt.svg)](https://hub.docker.com/r/kevinrsdev/autogpt) | [![Docker](https://img.shields.io/docker/pulls/kevinrsdev/orchgpt.svg)](https://hub.docker.com/r/kevinrsdev/orchgpt) |\n| `cargo install autogpt --all-features` |  `docker pull kevinrsdev/autogpt:0.1.0` | `docker pull kevinrsdev/orchgpt:0.1.0` |\n| `autogpt -h` \u003cbr\u003e `orchgpt -h` |  `docker run autogpt -h` | `docker run orchgpt -h` |\n\n\u003cvideo src=\"https://github.com/kevin-rs/kevin/assets/62179149/ba6f7204-849e-4b89-ae92-5b7faa0be68a\"\u003e\u003c/video\u003e\n\n\u003c/div\u003e\n\nAutoGPT is a pure rust framework that simplifies AI agent creation and management for various tasks. Its remarkable speed and versatility are complemented by a mesh of built-in interconnected GPTs, ensuring exceptional performance and adaptability.\n\n---\n\n## 🚀 Features\n\n- **Agent Creation**: Easily create different types of agents tailored to specific tasks.\n- **Task Management**: Efficiently manage tasks and distribute them among agents.\n- **Extensible**: Extend functionality by adding new agent types and task handling capabilities.\n- **CLI Interface**: Command-line interface for seamless interaction with the framework.\n- **SDK Integration**: Software development kit for integrating AutoGPT into existing projects.\n\n---\n\n## 📦 Installation\n\nPlease refer to [our tutorial](INSTALLATION.md) for guidance on installing, running, and/or building the CLI from source using either Cargo or Docker.\n\n\u003e [!NOTE]\n\u003e For optimal performance and compatibility, we strongly advise utilizing a Linux operating system to install this CLI.\n\n---\n\n## 🔄 Workflow\n\nAutoGPT supports two modes of operation, enabling both standalone and distributed use cases:\n\n### 1. 🧠 Agentic Networkless Mode (Standalone)\n\nIn this mode, the user runs an individual `autogpt` agent directly via a subcommand (e.g., `autogpt arch`). Each agent operates independently without needing a networked orchestrator.\n\n```sh\n                       +------------------------------------+\n                       |                User                |\n                       |             Provides               |\n                       |          Project Prompt            |\n                       +------------------+-----------------+\n                                          |\n                                          v\n                       +------------------+-----------------+\n                       |               ManagerGPT           |\n                       |            Distributes Tasks       |\n                       |          to Backend, Frontend,     |\n                       |           Designer, Architect      |\n                       +------------------+-----------------+\n                                          |\n                                          v\n   +--------------------------+-----------+----------+----------------------+\n   |                          |                      |                      |\n   |                          v                      v                      v\n+--+---------+       +--------+--------+       +-----+-------+       +-----+-------+\n|  Backend   |       |    Frontend     |       |  Designer   |       |  Architect  |\n|    GPT     |       |      GPT        |  ...  |    GPT      |       |  GPT        |\n|            |       |                 |       |  (Optional) |       |             |\n+--+---------+       +-----------------+       +-------------+       +-------------+\n   |                          |                       |                       |\n   v                          v                       v                       v\n(Backend Logic)        (Frontend Logic)   ...   (Designer Logic)        (Architect Logic)\n   |                          |                       |                       |\n   +--------------------------+----------+------------+-----------------------+\n                                         |\n                                         v\n                      +------------------+-----------------+\n                      |               ManagerGPT           |\n                      |       Collects and Consolidates    |\n                      |        Results from Agents         |\n                      +------------------+-----------------+\n                                         |\n                                         v\n                      +------------------+-----------------+\n                      |                User                |\n                      |            Receives Final          |\n                      |             Output from            |\n                      |            ManagerGPT              |\n                      +------------------------------------+\n```\n\n- ✍️ **User Input**: Provide a project's goal (e.g. \"Develop a full stack app that fetches today's weather. Use the axum web framework for the backend and the Yew rust framework for the frontend.\").\n- 🚀 **Initialization**: AutoGPT initializes based on the user's input, creating essential components such as the `ManagerGPT` and individual agent instances (ArchitectGPT, BackendGPT, FrontendGPT).\n- 🛠️ **Agent Configuration**: Each agent is configured with its unique objectives and capabilities, aligning them with the project's defined goals. This configuration ensures that agents contribute effectively to the project's objectives.\n- 📋 **Task Allocation**: ManagerGPT distributes tasks among agents considering their capabilities and project requirements.\n- ⚙️ **Task Execution**: Agents execute tasks asynchronously, leveraging their specialized functionalities.\n- 🔄 **Feedback Loop**: Continuous feedback updates users on project progress and addresses issues.\n\n### 2. 🌐 Agentic Networking Mode (Orchestrated)\n\nIn networking mode, `autogpt` connects to an external orchestrator (`orchgpt`) over a secure TLS-encrypted TCP channel. This orchestrator manages agent lifecycles, routes commands, and enables rich inter-agent collaboration using a unified protocol.\n\nAutoGPT introduces a novel and scalable communication protocol called [`IAC`](IAC.md) (Inter/Intra-Agent Communication), enabling seamless and secure interactions between agents and orchestrators, inspired by [operating system IPC mechanisms](https://en.wikipedia.org/wiki/Inter-process_communication).\n\nIn networking mode, AutoGPT utilizes a layered architecture:\n\n```sh\n                       +------------------------------------+\n                       |                User                |\n                       |         Sends Prompt via CLI       |\n                       +------------------+-----------------+\n                                          |\n                                          v\n                            TLS + Protobuf over TCP to:\n                       +------------------+-----------------+\n                       |             Orchestrator           |\n                       |     Receives and Routes Commands   |\n                       +-----------+----------+-------------+\n                                   |          |\n     +-----------------------------+          +----------------------------+\n     |                                                                     |\n     v                                                                     v\n+--------------------+                                         +--------------------+\n|   ArchitectGPT     |\u003c---------------- IAC ------------------\u003e|    ManagerGPT      |\n+--------------------+                                         +--------------------+\n   |                        Agent Layer:                                   |\n   |          (BackendGPT, FrontendGPT, DesignerGPT)                       |\n   +-------------------------------------+---------------------------------+\n                                         |\n                                         v\n                               Task Execution \u0026 Collection\n                                         |\n                                         v\n                           +---------------------------+\n                           |           User            |\n                           |     Receives Final Output |\n                           +---------------------------+\n```\n\nAll communication happens securely over **TLS + TCP**, with messages encoded in **Protocol Buffers (protobuf)** for efficiency and structure.\n\n1. User Input: The user provides a project prompt like:\n\n   ```sh\n   /architect create \"fastapi app\" | python\n   ```\n\n   This is securely sent to the Orchestrator over TLS.\n\n1. Initialization: The Orchestrator parses the command and initializes the appropriate agent (e.g., `ArchitectGPT`).\n\n1. Agent Configuration: Each agent is instantiated with its specialized goals:\n\n   - **ArchitectGPT**: Plans system structure\n   - **BackendGPT**: Generates backend logic\n   - **FrontendGPT**: Builds frontend UI\n   - **DesignerGPT**: Handles design\n\n1. Task Allocation: `ManagerGPT` dynamically assigns subtasks to agents using the IAC protocol. It determines which agent should perform what based on capabilities and the original user goal.\n\n1. Task Execution: Agents execute their tasks, communicate with their subprocesses or other agents via IAC (inter/intra communication), and push updates or results back to the orchestrator.\n\n1. Feedback Loop: Throughout execution, agents return status reports. The `ManagerGPT` collects all output, and the Orchestrator sends it back to the user.\n\n## 🤖 Available Agents\n\nAt the current release, Autogpt consists of 8 built-in specialized autonomous AI agents ready to assist you in bringing your ideas to life!\nRefer to [our guide](AGENTS.md) to learn more about how the built-in agents work.\n\n## 📌 Examples\n\nYour can refer to [our examples](EXAMPLES.md) for guidance on how to use the cli in a jupyter environment.\n\n## 📚 Documentation\n\nFor detailed usage instructions and API documentation, refer to the [AutoGPT Documentation](https://docs.rs/autogpt).\n\n## 🤝 Contributing\n\nContributions are welcome! See the [Contribution Guidelines](CONTRIBUTING.md) for more information on how to get started.\n\n## 📝 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkevin-rs%2Fautogpt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkevin-rs%2Fautogpt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkevin-rs%2Fautogpt/lists"}