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 3 modes of operation, non agentic and both standalone and distributed agentic use cases:

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.

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.

sh +------------------------------------+ | User | | Provides | | Project Prompt | +------------------+-----------------+ | v +------------------+-----------------+ | ManagerGPT | | Distributes Tasks | | to Backend, Frontend, | | Designer, Architect | +------------------+-----------------+ | v +--------------------------+-----------+----------+----------------------+ | | | | | v v v +--+---------+ +--------+--------+ +-----+-------+ +-----+-------+ | Backend | | Frontend | | Designer | | Architect | | GPT | | GPT | ... | GPT | | GPT | | | | | | (Optional) | | | +--+---------+ +-----------------+ +-------------+ +-------------+ | | | | v v v v (Backend Logic) (Frontend Logic) ... (Designer Logic) (Architect Logic) | | | | +--------------------------+----------+------------+-----------------------+ | v +------------------+-----------------+ | ManagerGPT | | Collects and Consolidates | | Results from Agents | +------------------+-----------------+ | v +------------------+-----------------+ | User | | Receives Final | | Output from | | ManagerGPT | +------------------------------------+

• ✍️ 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. This configuration ensures that agents contribute effectively to the project's objectives.
• 📋 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.

In networking mode, AutoGPT utilizes a layered architecture:

sh +------------------------------------+ | User | | Sends Prompt via CLI | +------------------+-----------------+ | v TLS + Protobuf over TCP to: +------------------+-----------------+ | Orchestrator | | Receives and Routes Commands | +-----------+----------+-------------+ | | +-----------------------------+ +----------------------------+ | | v v +--------------------+ +--------------------+ | ArchitectGPT |<---------------- IAC ------------------>| ManagerGPT | +--------------------+ +--------------------+ | Agent Layer: | | (BackendGPT, FrontendGPT, DesignerGPT) | +-------------------------------------+---------------------------------+ | v Task Execution & Collection | v +---------------------------+ | 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:

sh /architect create "fastapi app" | python

This is securely sent to the Orchestrator over TLS.

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

2. 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

1. 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.

2. 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.

3. 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 8 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.