Introduction | AutoGPT Classic | AutoGPT

Introduction

AutoGPT Classic was conceived when OpenAI published their GPT-4 model accompanied by a paper outlining the advanced reasoning and task-solving abilities of the model. The concept was (and still is) fairly simple: let an LLM decide what to do over and over, while feeding the results of its actions back into the prompt. This allows the program to iteratively and incrementally work towards its objective.

The fact that this program is able to execute actions on behalf of its user makes it an agent. In the case of AutoGPT Classic, the user still has to authorize every action, but as the project progresses we'll be able to give the agent more autonomy and only require consent for select actions.

AutoGPT Classic is a generalist agent, meaning it is not designed with a specific task in mind. Instead, it is designed to be able to execute a wide range of tasks across many disciplines, as long as it can be done on a computer.

AutoGPT Classic Documentation

Welcome to the AutoGPT Classic Documentation.

The AutoGPT Classic project consists of four main components:

• The Agent – also known as just "AutoGPT Classic"

• The Benchmark – AKA agbenchmark

• The Forge

• The Frontend

To tie these together, we also have a CLI at the root of the project.

🤖 Agent

The former heart of AutoGPT, and the project that kicked it all off: a semi-autonomous agent powered by LLMs to execute any task for you*.

We continue to develop this project with the goal of providing access to AI assistance to the masses, and building the future transparently and together.

• 💡 Explore - See what AI can do and be inspired by a glimpse of the future.

• 🚀 Build with us - We welcome any input, whether it's code or ideas for new features or improvements! Join us on Discord and find out how you can join in on the action.

If you'd like to see what's next, check out the AutoGPT Platform.

• it isn't quite there yet, but that is the ultimate goal that we are still pursuing

🎯 Benchmark

Measure your agent's performance! The agbenchmark can be used with any agent that supports the agent protocol, and the integration with the project's CLI makes it even easier to use with AutoGPT Classic and forge-based agents. The benchmark offers a stringent testing environment. Our framework allows for autonomous, objective performance evaluations, ensuring your agents are primed for real-world action.

• 📦 agbenchmark on Pypi

• 🔌 Agent Protocol Standardization - AutoGPT Classic uses the agent protocol from the AI Engineer Foundation to ensure compatibility with many agents, both from within and outside the project.

🏗️ Forge

Forge your own agent! The Forge is a ready-to-go template for your agent application. All the boilerplate code is already handled, letting you channel all your creativity into the things that set your agent apart.

• 🛠️ Building with Ease - We've set the groundwork so you can focus on your agent's personality and capabilities. Comprehensive tutorials are available here.

💻 Frontend

An easy-to-use and open source frontend for any Agent Protocol-compliant agent.

• 🎮 User-Friendly Interface - Manage your agents effortlessly.

• 🔄 Seamless Integration - Smooth connectivity between your agent and our benchmarking system.

🔧 CLI

The project CLI makes it easy to use all of the components of AutoGPT Classic in the repo, separately or together. To install its dependencies, simply run ./run setup, and you're ready to go!

$ ./run
Usage: cli.py [OPTIONS] COMMAND [ARGS]...

Options:
  --help  Show this message and exit.

Commands:
  agent      Commands to create, start and stop agents
  benchmark  Commands to start the benchmark and list tests and categories
  setup      Installs dependencies needed for your system.

Common commands:

• ./run agent start autogpt – runs the AutoGPT Classic agent

• ./run agent create <name> – creates a new Forge-based agent project at agents/<name>

• ./run benchmark start <agent> – benchmarks the specified agent

Glossary of Terms

• Repository: Space where your project resides.

• Forking: Copying a repository under your account.

• Cloning: Making a local copy of a repository.

• Agent: The AutoGPT you'll create and develop.

• Benchmarking: Testing your agent's skills in the Forge.

• Forge: The template for building your AutoGPT agent.

• Frontend: The UI for tasks, logs, and task history.