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align=\"center\"\u003e\n\n# ๐Ÿค– 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-1.89%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/wiseaidotdev/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/wiseaidotdev/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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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/wiseaidotdev/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/wiseaidotdev/autogpt/blob/main/examples/basic.ipynb)\n\n![banner](https://raw.githubusercontent.com/wiseaidotdev/autogpt/refs/heads/main/assets/logo.png)\n\n| ๐Ÿง Linux `(Recommended)` | ๐ŸชŸ Windows | ๐Ÿ‹ | ๐Ÿ‹ |\n| :----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------: |\n| [![Crates.io Downloads](https://img.shields.io/crates/d/autogpt)](https://crates.io/crates/autogpt) | [![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| ![linux-demo](https://raw.githubusercontent.com/wiseaidotdev/autogpt/refs/heads/main/assets/linux.png) | ![windows-demo](https://raw.githubusercontent.com/wiseaidotdev/autogpt/refs/heads/main/assets/windows.png) | - | - |\n| Method 1: [Download Executable File](https://github.com/wiseaidotdev/autogpt/releases/download/v0.3.2/autogpt) | [Download `.exe` File](https://github.com/wiseaidotdev/autogpt/releases/download/v0.3.2/autogpt.exe) | - | - |\n| Method 2: `cargo install autogpt --all-features` | `cargo install autogpt --all-features` | `docker pull kevinrsdev/autogpt` | `docker pull kevinrsdev/orchgpt` |\n| [**Set Environment Variables**](https://github.com/wiseaidotdev/autogpt/blob/main/INSTALLATION.md#environment-variables-setup) | [**Set Environment Variables**](https://github.com/wiseaidotdev/autogpt/blob/main/INSTALLATION.md#environment-variables-setup) | [**Set Environment Variables**](https://github.com/wiseaidotdev/autogpt/blob/main/INSTALLATION.md#-using-docker) | [**Set Environment Variables**](https://github.com/wiseaidotdev/autogpt/blob/main/INSTALLATION.md#-using-docker) |\n| `autogpt -h` \u003cbr\u003e `orchgpt -h` | `autogpt.exe -h` | `docker run kevinrsdev/autogpt -h` | `docker run kevinrsdev/orchgpt -h` |\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/55a28c1a-eba2-4c94-aee1-2661cbeaabc6\"\u003e\u003c/video\u003e\n\n\u003c/div\u003e\n\n\u003e [!NOTE]\n\u003e This project is under active development. There is also a parallel project, [**lmm**](https://github.com/wiseaidotdev/lmm), under equally active development; It does **not** use LLMs at all. Instead, it uses equation-based intelligence to predict new words and reason without gradient-trained models. Check it out if you're interested in a fundamentally different approach to machine intelligence!\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## ๐Ÿง  Framework Overview\n\n### โš™๏ธ Agent Core Architecture\n\nAutoGPT agents are modular and autonomous, built from composable components:\n\n- ๐Ÿ”Œ **Tools \u0026 Sensors**: Interface with the real world via actions (e.g., file I/O, APIs) and perception (e.g., audio, video, data).\n- ๐Ÿง  **Memory \u0026 Knowledge**: Combines long-term vector memory with structured knowledge bases for reasoning and recall.\n- ๐Ÿ“ **No-Code Agent Configs**: Define agents and their behaviors with simple, declarative YAML, no coding required.\n- ๐Ÿงญ **Planner \u0026 Goals**: Breaks down complex tasks into subgoals and tracks progress dynamically.\n- ๐Ÿง **Persona \u0026 Capabilities**: Customizable behavior profiles and access controls define how agents act.\n- ๐Ÿง‘โ€๐Ÿคโ€๐Ÿง‘ **Collaboration**: Agents can delegate, swarm, or work in teams with other agents.\n- ๐Ÿชž **Self-Reflection**: Introspection module to debug, adapt, or evolve internal strategies.\n- ๐Ÿ”„ **Context Management**: Manages active memory (context window) for ongoing tasks and conversations.\n- ๐Ÿ“… **Scheduler**: Time-based or reactive triggers for agent actions.\n\n### ๐Ÿš€ Developer Features\n\nAutoGPT is designed for flexibility, integration, and scalability:\n\n- ๐Ÿงช **Custom Agent Creation**: Build tailored agents for different roles or domains.\n- ๐Ÿ“‹ **Task Orchestration**: Manage and distribute tasks across agents efficiently.\n- ๐Ÿงฑ **Extensibility**: Add new tools, behaviors, or agent types with ease.\n- ๐Ÿ’ป **CLI Tools**: Command-line interface for rapid experimentation and control.\n- ๐Ÿงฐ **SDK Support**: Embed AutoGPT into existing projects or systems seamlessly.\n- ๐Ÿ”€ **Mixture of Providers (MoP)**: Parallel fan-out and weighted scoring across multiple AI backends for optimal response quality.\n\n## ๐Ÿ“ฆ 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## ๐Ÿ”„ Workflow\n\nAutoGPT supports 4 modes of operation: interactive, direct prompt, standalone agentic, and distributed agentic.\n\n### 0. ๐Ÿค– GenericGPT Interactive Mode (Default)\n\nWhen you run `autogpt` with **no subcommand or flags**, it launches an interactive AI shell powered by **GenericGPT**, a production-hardened autonomous software engineering agent with session persistence, model switching, and multi-provider support:\n\n```sh\nautogpt\n```\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/55a28c1a-eba2-4c94-aee1-2661cbeaabc6\"\u003e\u003c/video\u003e\n\nThe interactive shell supports the following commands:\n\n| Command | Description |\n| --------------- | ----------------------------------------------------------- |\n| `\u003cyour prompt\u003e` | Send a task to the GenericGPT autonomous agent |\n| `/help` | Show available commands |\n| `/provider` | Switch AI provider (Gemini, OpenAI, Anthropic, XAI, Cohere) |\n| `/models` | Browse and switch between provider-native models |\n| `/sessions` | List and resume previous sessions |\n| `/status` | Show current model, provider, and directory |\n| `/workspace` | Show the current workspace path |\n| `/clear` | Clear the terminal |\n| `exit` / `quit` | Save session and quit |\n\n\u003e Press `ESC` at any time to interrupt a running generation.\n\n### ๐Ÿ”€ Mixture of Providers (MoP)\n\nAutoGPT introduces a high-availability **Mixture of Providers** architecture. When enabled via the `--mixture` or `-m` flag, every prompt is fanned out concurrently to all configured AI providers (Gemini, OpenAI, etc.). A weighted scoring engine evaluates responses based on:\n\n1. **Length calibration** (rewarding detail, penalizing fluff).\n1. **Code quality** (bonus for language-tagged Markdown blocks).\n1. **Structural richness** (headings, lists, hygiene).\n1. **Reasoning depth** (connectivity words and logical flow).\n1. **Completeness** (punctuation and closing delimiters).\n\nThe highest-scored response is selected as the winner and injected into the agent's context, promoting the best \"intelligence\" available from your configured keys.\n\n### The `.autogpt` Directory\n\nGenericGPT maintains all persistent state inside the workspace root (defaults to the **current directory**):\n\n```sh\n.autogpt/\nโ”œโ”€โ”€ sessions/ # Markdown conversation snapshots, auto-saved after every response\nโ”‚ โ”œโ”€โ”€ \u003cuuid\u003e.md\nโ”‚ โ””โ”€โ”€ ...\nโ””โ”€โ”€ skills/ # TOML lesson files, injected into future prompts automatically\n โ”œโ”€โ”€ rust.toml\n โ”œโ”€โ”€ web.toml\n โ””โ”€โ”€ python.toml\n```\n\nControl the workspace root with `AUTOGPT_WORKSPACE`:\n\n```sh\nexport AUTOGPT_WORKSPACE=/my/project # scope all file ops to a specific directory\nautogpt\n```\n\n### Model Selection\n\nModels are sourced dynamically from each provider's crate. Override the active model without entering the shell:\n\n```sh\nexport GEMINI_MODEL=gemini-2.5-pro-preview-05-06\nexport OPENAI_MODEL=gpt-4o\nexport MODEL=\u003cany-model-id\u003e # global fallback for any provider\n```\n\n### How GenericGPT Works\n\nEach prompt goes through a seven-step pipeline:\n\n1. **MoP Fan-out** (optional): Parallel execution across multiple providers.\n1. **Reasoning**: structured internal monologue stored in the session log.\n1. **Task synthesis**: decomposition into typed actions (`CreateFile`, `PatchFile`, `RunCommand`, ...).\n1. **Execution**: file edits via `PatchFile`; shell execution via `RunCommand`.\n1. **Build-and-verify**: auto-detects `Cargo.toml` / `package.json` / `Makefile` and runs the build; retries on failure up to 3 times.\n1. **Reflection**: reviews outcomes and lesson candidates.\n1. **Skill extraction**: lessons written to `.autogpt/skills/\u003cdomain\u003e.toml` and injected in future sessions.\n\n```mermaid\nflowchart TD\n A([User enters prompt]) --\u003e B{Mixture mode?}\n B -- Yes --\u003e C[Run Mixture of Providers]\n B -- No --\u003e D[Standard Provider]\n C \u0026 D --\u003e E[Reasoning pre-step]\n E --\u003e F[Task synthesis]\n F --\u003e G{User approves?}\n G -- yolo mode / yes --\u003e H[Execute actions]\n H --\u003e I[Build-and-verify loop]\n I -- pass --\u003e J[Reflection]\n I -- fail, retry โ‰ค3 --\u003e H\n J --\u003e K[Save skills \u0026 session]\n K --\u003e L([Ready for next prompt])\n```\n\n```mermaid\nflowchart TD\n A([User launches autogpt]) --\u003e B{Any args?}\n B -- No --\u003e C[GenericGPT Interactive Shell]\n B -- Yes --\u003e D{Subcommand}\n C --\u003e E[Select Provider \u0026 Model]\n E --\u003e F[Enter Prompt Loop]\n F --\u003e G{Mixture enabled?}\n G -- Yes --\u003e H[Mixture of Providers]\n G -- No --\u003e I[Standard Prompt]\n H \u0026 I --\u003e J[Agent Generates Response]\n J --\u003e F\n D -- arch --\u003e K[ArchitectGPT]\n D -- back --\u003e L[BackendGPT]\n D -- front --\u003e M[FrontendGPT]\n D -- design --\u003e N[DesignerGPT]\n D -- manage --\u003e O[ManagerGPT]\n D -- -p prompt --\u003e P[Direct LLM Prompt]\n```\n\n### 1. ๐Ÿ’ฌ Direct Prompt Mode\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/505737f6-2fe8-4a93-8cd9-fb036b55b8fd\"\u003e\u003c/video\u003e\n\nIn this mode, you can use the CLI to interact with the LLM directly, no need to define or configure agents. Use the `-p` flag to send prompts to your preferred LLM provider quickly and easily. Combine with `--mixture` to get the best answer from all your providers at once.\n\n```sh\n# Single provider\nautogpt -p \"Explain the Rust borrow checker in simple terms\"\n\n# Mixture of Providers (fanned out)\nautogpt -m -p \"Implement a Red-Black tree in Rust\"\n```\n\n### 2. ๐Ÿง  Agentic Networkless Mode (Standalone)\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/7d47b1d8-b2f2-4d23-a1f4-da926e425330\"\u003e\u003c/video\u003e\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```mermaid\nflowchart TD\n User([User Provides Project Prompt]) --\u003e M[ManagerGPT\\nDistributes Tasks]\n M --\u003e B[BackendGPT]\n M --\u003e F[FrontendGPT]\n M --\u003e D[DesignerGPT\\nOptional]\n M --\u003e A[ArchitectGPT]\n B --\u003e BL[Backend Logic]\n F --\u003e FL[Frontend Logic]\n D --\u003e DL[Design Assets]\n A --\u003e AL[Architecture Diagram]\n BL \u0026 FL \u0026 DL \u0026 AL --\u003e M2[ManagerGPT\\nCollects \u0026 Consolidates]\n M2 --\u003e Result([User Receives Final Output])\n```\n\n- โœ๏ธ **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- ๐Ÿš€ **Initialization**: AutoGPT initializes based on the user's input, creating essential components such as the `ManagerGPT` and individual agent instances (ArchitectGPT, BackendGPT, FrontendGPT).\n- ๐Ÿ› ๏ธ **Agent Configuration**: Each agent is configured with its unique objectives and capabilities, aligning them with the project's defined goals.\n- ๐Ÿ“‹ **Task Allocation**: ManagerGPT distributes tasks among agents considering their capabilities and project requirements.\n- โš™๏ธ **Task Execution**: Agents execute tasks asynchronously, leveraging their specialized functionalities.\n- ๐Ÿ”„ **Feedback Loop**: Continuous feedback updates users on project progress and addresses issues.\n\n### 3. ๐ŸŒ Agentic Networking Mode (Orchestrated)\n\n\u003cvideo src=\"https://github.com/user-attachments/assets/ecd82549-a48f-49c2-b751-23f74820bf3d\"\u003e\u003c/video\u003e\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\n```mermaid\nflowchart TD\n U([User sends prompt via CLI]) -- TLS + Protobuf over TCP --\u003e O[Orchestrator\\nReceives \u0026 Routes Commands]\n O --\u003e AG[ArchitectGPT]\n O --\u003e MG[ManagerGPT]\n AG \u003c-- IAC --\u003e MG\n subgraph IAC [\" IAC - Inter/Intra-Agent Communication Layer\"]\n MG\n BG[BackendGPT]\n FG[FrontendGPT]\n DG[DesignerGPT]\n end\n MG -- IAC --\u003e BG\n MG -- IAC --\u003e FG\n MG -- IAC --\u003e DG\n BG \u0026 FG \u0026 DG --\u003e Exec[Task Execution \u0026 Collection]\n Exec --\u003e R([User Receives Final Output])\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 /arch 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 - **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## ๐Ÿค– Available Agents\n\nAt the current release, AutoGPT consists of 9 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## ๐Ÿ“Œ Examples\n\nYour can refer to [our examples](EXAMPLES.md) for guidance on how to use the cli in a jupyter environment.\n\n## ๐Ÿ“š Documentation\n\nFor detailed usage instructions and API documentation, refer to the [AutoGPT Documentation](https://docs.rs/autogpt).\n\n## ๐Ÿค Contributing\n\nContributions are welcome! See the [Contribution Guidelines](CONTRIBUTING.md) for more information on how to get started.\n\n## ๐Ÿ“ License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwiseaidotdev%2Fautogpt","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwiseaidotdev%2Fautogpt","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwiseaidotdev%2Fautogpt/lists"}