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Component Agents


Legacy plugins no longer work with AutoGPT. They have been replaced by components, although we're still working on a new system to load plug-in components.

This guide explains the component-based architecture of AutoGPT agents. It's a new way of building agents that is more flexible and easier to extend. Components replace some agent's logic and plugins with a more modular and composable system.

Agent is composed of components, and each component implements a range of protocols (interfaces), each one providing a specific functionality, e.g. additional commands or messages. Each protocol is handled in a specific order, defined by the agent. This allows for a clear separation of concerns and a more modular design.

This system is simple, flexible, and doesn't hide any data - anything can still be passed or accessed directly from or between components.

Definitions & Guides

See Creating Components to get started! Or you can explore the following topics in detail:

  • 🧩 Component: a class that implements one or more protocols. It can be added to an agent to provide additional functionality. See what's already provided in Built-in Components.
  • ⚙️ Protocol: an interface that defines a set of methods that a component must implement. Protocols are used to group related functionality.
  • 🛠️ Command: enable agent to interact with user and tools.
  • 🤖 Agent: a class that is composed of components. It's responsible for executing pipelines and managing the components.
  • Pipeline: a sequence of method calls on components. Pipelines are used to execute a series of actions in a specific order. As of now there's no formal class for a pipeline, it's just a sequence of method calls on components. There are two default pipelines implemented in the default agent: propose_action and execute. See 🤖 Agent to learn more.