https://github.com/kevin-rs/autogpt
🦀 A Pure Rust Framework For Building AGI (WIP).
https://github.com/kevin-rs/autogpt
agent agi ai artificial-intelligence autogpt evcxr gemini gemini-pro getimg gpt jupyter-notebook nylas-api rust stable-diffusion
Last synced: 19 days ago
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🦀 A Pure Rust Framework For Building AGI (WIP).
- Host: GitHub
- URL: https://github.com/kevin-rs/autogpt
- Owner: kevin-rs
- License: mit
- Created: 2024-03-31T12:33:41.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-09T06:28:34.000Z (25 days ago)
- Last Synced: 2025-04-11T23:57:15.847Z (22 days ago)
- Topics: agent, agi, ai, artificial-intelligence, autogpt, evcxr, gemini, gemini-pro, getimg, gpt, jupyter-notebook, nylas-api, rust, stable-diffusion
- Language: Rust
- Homepage: https://docs.rs/autogpt
- Size: 295 KB
- Stars: 65
- Watchers: 1
- Forks: 5
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Codeowners: CODEOWNERS
Awesome Lists containing this project
README
# 🤖 AutoGPT
[](https://github.com/wiseaidev)
[](https://www.rust-lang.org/)
[](https://www.rust-lang.org)
[](LICENSE)
[](https://github.com/wiseaidev)
[](https://jupyter.org/)
[](https://reddit.com/submit?url=https://github.com/kevin-rs/autogpt&title=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.)
[](https://news.ycombinator.com/submitlink?u=https://github.com/kevin-rs/autogpt&t=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.)
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[](https://dl.circleci.com/status-badge/redirect/gh/kevin-rs/autogpt/tree/main)
[](https://crates.io/crates/autogpt)
[](./examples/basic.ipynb)
[](https://mybinder.org/v2/gh/kevin-rs/autogpt/main?filepath=examples/basic.ipynb)
[](https://colab.research.google.com/github/kevin-rs/autogpt/blob/main/examples/basic.ipynb)
| 🏗️ `(Recommended)` | 🐋 | 🐋 |
| :------: | :--------: | :--------: |
| [](https://crates.io/crates/autogpt) | [](https://hub.docker.com/r/kevinrsdev/autogpt) | [](https://hub.docker.com/r/kevinrsdev/orchgpt) |
| `cargo install autogpt --all-features` | `docker pull kevinrsdev/autogpt:0.1.0` | `docker pull kevinrsdev/orchgpt:0.1.0` |
| `autogpt -h`
`orchgpt -h` | `docker run autogpt -h` | `docker run orchgpt -h` |
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.
---
## 🚀 Features
- **Agent Creation**: Easily create different types of agents tailored to specific tasks.
- **Task Management**: Efficiently manage tasks and distribute them among agents.
- **Extensible**: Extend functionality by adding new agent types and task handling capabilities.
- **CLI Interface**: Command-line interface for seamless interaction with the framework.
- **SDK Integration**: Software development kit for integrating AutoGPT into existing projects.
---
## 📦 Installation
Please refer to [our tutorial](INSTALLATION.md) 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 two modes of operation, enabling both standalone and distributed use cases:
### 1. 🧠 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.
### 2. 🌐 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`](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).
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`).
1. 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.
1. 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.
1. 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](AGENTS.md) to learn more about how the built-in agents work.
## 📌 Examples
Your can refer to [our examples](EXAMPLES.md) 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](https://docs.rs/autogpt).
## 🤝 Contributing
Contributions are welcome! See the [Contribution Guidelines](CONTRIBUTING.md) for more information on how to get started.
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
---