Creating Components

The minimal component

Components can be used to implement various functionalities like providing messages to the prompt, executing code, or interacting with external services.

Component is a class that inherits from AgentComponent OR implements one or more protocols. Every protocol inherits AgentComponent, so your class automatically becomes a component once you inherit any protocol.

class MyComponent(AgentComponent):
    pass

This is already a valid component, but it doesn't do anything yet. To add some functionality to it, you need to implement one or more protocols.

Let's create a simple component that adds "Hello World!" message to the agent's prompt. To do this we need to implement MessageProvider protocol in our component. MessageProvider is an interface with get_messages method:

# No longer need to inherit AgentComponent, because MessageProvider already does it
class HelloComponent(MessageProvider):
    def get_messages(self) -> Iterator[ChatMessage]:
        yield ChatMessage.user("Hello World!")

Now we can add our component to an existing agent or create a new Agent class and add it there:

class MyAgent(Agent):
    self.hello_component = HelloComponent()

get_messages will called by the agent each time it needs to build a new prompt and the yielded messages will be added accordingly.

Passing data to and between components

Since components are regular classes you can pass data (including other components) to them via the __init__ method. For example we can pass a config object and then retrieve an API key from it when needed:

class DataComponent(MessageProvider):
    def __init__(self, config: Config):
        self.config = config

    def get_messages(self) -> Iterator[ChatMessage]:
        if self.config.openai_credentials.api_key:
            yield ChatMessage.system("API key found!")
        else:
            yield ChatMessage.system("API key not found!")

Note

Component-specific configuration handling isn't implemented yet.

Configuring components

Components can be configured using a pydantic model. To make component configurable, it must inherit from ConfigurableComponent[BM] where BM is the configuration class inheriting from pydantic's BaseModel. You should pass the configuration instance to the ConfigurableComponent's __init__ or set its config property directly. Using configuration allows you to load confugration from a file, and also serialize and deserialize it easily for any agent. To learn more about configuration, including storing sensitive information and serialization see Component Configuration.

# Example component configuration
class UserGreeterConfiguration(BaseModel):
    user_name: str

class UserGreeterComponent(MessageProvider, ConfigurableComponent[UserGreeterConfiguration]):
    def __init__(self):
        # Creating configuration instance
        # You could also pass it to the component constructor
        # e.g. `def __init__(self, config: UserGreeterConfiguration):`
        config = UserGreeterConfiguration(user_name="World")
        # Passing the configuration instance to the parent class
        UserGreeterComponent.__init__(self, config)
        # This has the same effect as the line above:
        # self.config = UserGreeterConfiguration(user_name="World")

    def get_messages(self) -> Iterator[ChatMessage]:
        # You can use the configuration like a regular model
        yield ChatMessage.system(f"Hello, {self.config.user_name}!")

Providing commands

To extend what an agent can do, you need to provide commands using CommandProvider protocol. For example to allow agent to multiply two numbers, you can create a component like this:

class MultiplicatorComponent(CommandProvider):
    def get_commands(self) -> Iterator[Command]:
        # Yield the command so the agent can use it
        yield self.multiply

    @command(
    parameters={
        "a": JSONSchema(
            type=JSONSchema.Type.INTEGER,
            description="The first number",
            required=True,
        ),
        "b": JSONSchema(
            type=JSONSchema.Type.INTEGER,
            description="The second number",
            required=True,
        )})
    def multiply(self, a: int, b: int) -> str:
        """
        Multiplies two numbers.

        Args:
            a: First number
            b: Second number

        Returns:
            Result of multiplication
        """
        return str(a * b)

To learn more about commands see 🛠️ Commands.

Prompt structure

After components provided all necessary data, the agent needs to build the final prompt that will be send to a llm. Currently, PromptStrategy (not a protocol) is responsible for building the final prompt.

If you want to change the way the prompt is built, you need to create a new PromptStrategy class, and then call relevant methods in your agent class. You can have a look at the default strategy used by the AutoGPT Agent: OneShotAgentPromptStrategy, and how it's used in the Agent (search for self.prompt_strategy).

Example UserInteractionComponent

Let's create a slightly simplified version of the component that is used by the built-in agent. It gives an ability for the agent to ask user for input in the terminal.

1. Create a class for the component that inherits from CommandProvider.

   class MyUserInteractionComponent(CommandProvider):
       """Provides commands to interact with the user."""
       pass
   

2. Implement command method that will ask user for input and return it.

   def ask_user(self, question: str) -> str:
       """If you need more details or information regarding the given goals,
       you can ask the user for input."""
       print(f"\nQ: {question}")
       resp = input("A:")
       return f"The user's answer: '{resp}'"
   

3. The command needs to be decorated with @command.

   @command(
       parameters={
           "question": JSONSchema(
               type=JSONSchema.Type.STRING,
               description="The question or prompt to the user",
               required=True,
           )
       },
   )
   def ask_user(self, question: str) -> str:
       """If you need more details or information regarding the given goals,
       you can ask the user for input."""
       print(f"\nQ: {question}")
       resp = input("A:")
       return f"The user's answer: '{resp}'"
   

4. We need to implement CommandProvider's get_commands method to yield the command.

   def get_commands(self) -> Iterator[Command]:
       yield self.ask_user
   

5. Since agent isn't always running in the terminal or interactive mode, we need to disable this component by setting self._enabled=False when it's not possible to ask for user input.

   def __init__(self, interactive_mode: bool):
       self.config = config
       self._enabled = interactive_mode
   

The final component should look like this:

# 1.
class MyUserInteractionComponent(CommandProvider):
    """Provides commands to interact with the user."""

    # We pass config to check if we're in noninteractive mode
    def __init__(self, interactive_mode: bool):
        self.config = config
        # 5.
        self._enabled = interactive_mode

    # 4.
    def get_commands(self) -> Iterator[Command]:
        # Yielding the command so the agent can use it
        # This won't be yielded if the component is disabled
        yield self.ask_user

    # 3.
    @command(
        # We need to provide a schema for ALL the command parameters
        parameters={
            "question": JSONSchema(
                type=JSONSchema.Type.STRING,
                description="The question or prompt to the user",
                required=True,
            )
        },
    )
    # 2.
    # Command name will be its method name and description will be its docstring
    def ask_user(self, question: str) -> str:
        """If you need more details or information regarding the given goals,
        you can ask the user for input."""
        print(f"\nQ: {question}")
        resp = input("A:")
        return f"The user's answer: '{resp}'"

Now if we want to use our user interaction instead of the default one we need to somehow remove the default one (if our agent inherits from Agent the default one is inherited) and add our own. We can simply override the user_interaction in __init__ method:

class MyAgent(Agent):
    def __init__(
        self,
        settings: AgentSettings,
        llm_provider: MultiProvider,
        file_storage: FileStorage,
        app_config: Config,
    ):
        # Call the parent constructor to bring in the default components
        super().__init__(settings, llm_provider, file_storage, app_config)
        # Disable the default user interaction component by overriding it
        self.user_interaction = MyUserInteractionComponent()

Alternatively we can disable the default component by setting it to None:

class MyAgent(Agent):
    def __init__(
        self,
        settings: AgentSettings,
        llm_provider: MultiProvider,
        file_storage: FileStorage,
        app_config: Config,
    ):
        # Call the parent constructor to bring in the default components
        super().__init__(settings, llm_provider, file_storage, app_config)
        # Disable the default user interaction component
        self.user_interaction = None
        # Add our own component
        self.my_user_interaction = MyUserInteractionComponent(app_config)

Learn more

The best place to see more examples is to look at the built-in components in the classic/original_autogpt/components and classic/original_autogpt/commands directories.

Guide on how to extend the built-in agent and build your own: 🤖 Agents
Order of some components matters, see 🧩 Components to learn more about components and how they can be customized.
To see built-in protocols with accompanying examples visit ⚙️ Protocols.