🤖 AutoGPT

🐧 Linux (Recommended)	🪟 Windows	🐋	🐋
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Method 1: Download Executable File	Download .exe File	-	-
Method 2: cargo install autogpt --all-features	cargo install autogpt --all-features	docker pull kevinrsdev/autogpt:0.2.5	docker pull kevinrsdev/orchgpt:0.2.5
Set Environment Variables	Set Environment Variables	Set Environment Variables	Set Environment Variables
autogpt -h orchgpt -h	autogpt.exe -h	docker run kevinrsdev/autogpt:0.2.5 -h	docker run kevinrsdev/orchgpt:0.2.5 -h

[!NOTE]
This project is under active development. There is also a parallel project, 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!

AutoGPT 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.

🧠 Framework Overview

⚙️ Agent Core Architecture

AutoGPT agents are modular and autonomous, built from composable components:

• 🔌 Tools & Sensors: Interface with the real world via actions (e.g., file I/O, APIs) and perception (e.g., audio, video, data).
• 🧠 Memory & Knowledge: Combines long-term vector memory with structured knowledge bases for reasoning and recall.
• 📝 No-Code Agent Configs: Define agents and their behaviors with simple, declarative YAML, no coding required.
• 🧭 Planner & Goals: Breaks down complex tasks into subgoals and tracks progress dynamically.
• 🧍 Persona & Capabilities: Customizable behavior profiles and access controls define how agents act.
• 🧑‍🤝‍🧑 Collaboration: Agents can delegate, swarm, or work in teams with other agents.
• 🪞 Self-Reflection: Introspection module to debug, adapt, or evolve internal strategies.
• 🔄 Context Management: Manages active memory (context window) for ongoing tasks and conversations.
• 📅 Scheduler: Time-based or reactive triggers for agent actions.

🚀 Developer Features

AutoGPT is designed for flexibility, integration, and scalability:

• 🧪 Custom Agent Creation: Build tailored agents for different roles or domains.
• 📋 Task Orchestration: Manage and distribute tasks across agents efficiently.
• 🧱 Extensibility: Add new tools, behaviors, or agent types with ease.
• 💻 CLI Tools: Command-line interface for rapid experimentation and control.
• 🧰 SDK Support: Embed AutoGPT into existing projects or systems seamlessly.

📦 Installation

Please refer to our tutorial for guidance on installing, running, and/or building the CLI from source using either Cargo or Docker.

[!NOTE]
For optimal performance and compatibility, we strongly advise utilizing a Linux operating system to install this CLI.

🔄 Workflow

AutoGPT supports 4 modes of operation: interactive, direct prompt, standalone agentic, and distributed agentic.

0. 🤖 GenericGPT Interactive Mode (Default)

When 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:

autogpt

The interactive shell supports the following commands:

Press ESC at any time to interrupt a running generation.

The .autogpt Directory

GenericGPT maintains all persistent state inside the workspace root (defaults to the current directory):

.autogpt/
├── sessions/          # YAML conversation snapshots, auto-saved after every response
│   ├── <uuid>.yaml
│   └── ...
└── skills/            # TOML lesson files, injected into future prompts automatically
    ├── rust.toml
    ├── web.toml
    └── python.toml

Control the workspace root with AUTOGPT_WORKSPACE:

export AUTOGPT_WORKSPACE=/my/project   # scope all file ops to a specific directory
autogpt

Model Selection

Models are sourced dynamically from each provider's crate, no hardcoded strings. Override the active model without entering the shell:

export GEMINI_MODEL=gemini-2.5-pro-preview-05-06
export OPENAI_MODEL=gpt-4o
export MODEL=<any-model-id>    # global fallback for any provider

How GenericGPT Works

Each prompt goes through a six-step pipeline:

1. Reasoning: structured internal monologue stored in the session log.
2. Task synthesis: decomposition into typed actions (CreateFile, PatchFile, RunCommand, ...).
3. Execution: surgical file edits via PatchFile; shell execution via RunCommand.
4. Build-and-verify: auto-detects Cargo.toml / package.json / Makefile and runs the build; retries on failure up to 3 times.
5. Reflection: reviews outcomes and lesson candidates.
6. Skill extraction: lessons written to .autogpt/skills/<domain>.toml and injected in future sessions.

flowchart TD
    A([User enters prompt]) --> B[Reasoning pre-step]
    B --> C[Task synthesis]
    C --> D{User approves?}
    D -- yolo mode / yes --> E[Execute actions]
    E --> G[Build-and-verify loop]
    G -- pass --> H[Reflection]
    G -- fail, retry ≤3 --> E
    H --> I[Save skills & session]
    I --> K([Ready for next prompt])

flowchart TD
    A([User launches autogpt]) --> B{Any args?}
    B -- No --> C[GenericGPT Interactive Shell]
    B -- Yes --> D{Subcommand}
    C --> E[Select Provider & Model]
    E --> F[Enter Prompt Loop]
    F --> G[Agent Generates Response]
    G --> F
    D -- arch --> H[ArchitectGPT]
    D -- back --> I[BackendGPT]
    D -- front --> J[FrontendGPT]
    D -- design --> K[DesignerGPT]
    D -- manage --> L[ManagerGPT]
    D -- -p prompt --> M[Direct LLM Prompt]

1. 💬 Direct Prompt Mode

In 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.

autogpt -p "Explain the Rust borrow checker in simple terms"

2. 🧠 Agentic Networkless Mode (Standalone)

In 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.

flowchart TD
    User([User Provides Project Prompt]) --> M[ManagerGPT\nDistributes Tasks]
    M --> B[BackendGPT]
    M --> F[FrontendGPT]
    M --> D[DesignerGPT\nOptional]
    M --> A[ArchitectGPT]
    B --> BL[Backend Logic]
    F --> FL[Frontend Logic]
    D --> DL[Design Assets]
    A --> AL[Architecture Diagram]
    BL & FL & DL & AL --> M2[ManagerGPT\nCollects & Consolidates]
    M2 --> Result([User Receives Final Output])

• ✍️ 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.").
• 🚀 Initialization: AutoGPT initializes based on the user's input, creating essential components such as the ManagerGPT and individual agent instances (ArchitectGPT, BackendGPT, FrontendGPT).
• 🛠️ Agent Configuration: Each agent is configured with its unique objectives and capabilities, aligning them with the project's defined goals.
• 📋 Task Allocation: ManagerGPT distributes tasks among agents considering their capabilities and project requirements.
• ⚙️ Task Execution: Agents execute tasks asynchronously, leveraging their specialized functionalities.
• 🔄 Feedback Loop: Continuous feedback updates users on project progress and addresses issues.

3. 🌐 Agentic Networking Mode (Orchestrated)

In 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.

AutoGPT introduces a novel and scalable communication protocol called IAC (Inter/Intra-Agent Communication), enabling seamless and secure interactions between agents and orchestrators, inspired by operating system IPC mechanisms.

flowchart TD
    U([User sends prompt via CLI]) -- TLS + Protobuf over TCP --> O[Orchestrator\nReceives & Routes Commands]
    O --> AG[ArchitectGPT]
    O --> MG[ManagerGPT]
    AG <-- IAC --> MG
    subgraph IAC [" IAC - Inter/Intra-Agent Communication Layer"]
        MG
        BG[BackendGPT]
        FG[FrontendGPT]
        DG[DesignerGPT]
    end
    MG -- IAC --> BG
    MG -- IAC --> FG
    MG -- IAC --> DG
    BG & FG & DG --> Exec[Task Execution & Collection]
    Exec --> R([User Receives Final Output])

All communication happens securely over TLS + TCP, with messages encoded in Protocol Buffers (protobuf) for efficiency and structure.

1. User Input: The user provides a project prompt like:

   /arch create "fastapi app" | python
   

   This is securely sent to the Orchestrator over TLS.

2. Initialization: The Orchestrator parses the command and initializes the appropriate agent (e.g., ArchitectGPT).

3. Agent Configuration: Each agent is instantiated with its specialized goals:

   • ArchitectGPT: Plans system structure
   • BackendGPT: Generates backend logic
   • FrontendGPT: Builds frontend UI
   • DesignerGPT: Handles design

4. 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.

5. 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.

6. Feedback Loop: Throughout execution, agents return status reports. The ManagerGPT collects all output, and the Orchestrator sends it back to the user.

🤖 Available Agents

At the current release, AutoGPT consists of 9 built-in specialized autonomous AI agents ready to assist you in bringing your ideas to life!
Refer to our guide to learn more about how the built-in agents work.

📌 Examples

Your can refer to our examples for guidance on how to use the cli in a jupyter environment.

📚 Documentation

For detailed usage instructions and API documentation, refer to the AutoGPT Documentation.

🤝 Contributing

Contributions are welcome! See the Contribution Guidelines for more information on how to get started.

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.