persona-toolkit 1.0.36

Toolkit for Persona, an agent AI system — provides modular, callable tools for dynamic function execution

Persona CLI

Persona CLI is a command-line tool for creating and managing projects that use the persona-toolkit — a modular function-calling framework designed for agent-based systems.

This CLI helps you scaffold projects, generate tools, test them locally, and run the FastAPI server that exposes your tools via REST API.

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🚀 Installation

To use the CLI, first install the persona-toolkit library (assuming it's published or available locally):

pip install persona-toolkit

Or if you're developing locally:

cd persona-toolkit/
poetry install

The CLI is exposed as:

persona-toolkit

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📆 Features

init

Create a new project scaffold with Poetry and the required structure:

persona-toolkit init my-project

This will:

• Initialize a Poetry project
• Install persona-toolkit as a dependency
• Create a tools/ folder where your tools will live

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add-tool

Add a new tool interactively:

persona-toolkit add-tool

You'll be prompted for a tool name. A new Python file will be created in the tools/ directory with a ready-to-edit template including:

• Input and Output models (using Pydantic)
• A run() function

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test-tool

Test a tool locally by manually entering its input values:

persona-toolkit test-tool echo

This will:

• Import the specified tool from the tools/ directory
• Prompt for input fields
• Run the run() function and show the output

You can use the cli to pass input values:

persona-toolkit test-tool echo --input '{"message": "Hello, World!"}'

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run

Start the FastAPI server and expose your tools via HTTP:

persona-toolkit run --port 8000

You can now access:

• GET /tools — list available tools
• GET /tools/{tool}/schema — get tool schema
• POST /invocations — run a tool

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🗂 Project Structure

my-project/
├── pyproject.toml        # Poetry project config
├── tools/                # Directory for your tools
│   └── echo_test.py      # Example tool
|── secret.py             # Example secret validation

Each tool must define:

• NAME (a str with tool name)
• Input (a Pydantic model)
• Output (a Pydantic model)
• run(context: AgentContext, input: Input) -> Output

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💡 Example Tool

from pydantic import BaseModel, Field
from persona_toolkit.model import AgentContext

NAME = "echo"


class Input(BaseModel):
    message: str = Field(description="Message to echo")


class Output(BaseModel):
    message: str


def run(context: AgentContext, args: Input) -> Output:
    """
    Echo the message back.

    The `context` can be used to store and retrieve state across invocations.
    For example, if a previous message exists in the context, it will be appended to the current message.
    """
    previous_message = context.get("message")
    if previous_message:
        input.message = f"{previous_message} {input.message}"

    context.set("message", args.message)

    return Output(message=f"Echo: {args.message}")

Understanding kwargs and context

The context parameter is an instance of AgentContext, which is a dictionary-like object that provides access to the current execution context of the tool, it includes the following keys:

• project_id: The ID of the project where the tool is being executed.
• session_id: The ID of the current session, which can be used to track the state of the conversation or interaction.
• user_id: The ID of the user invoking the tool, which can be useful for personalizing responses or tracking user-specific data.

And expose a set of methods to manage the context:

• get(key: str, default: Any = None): Retrieve a value from the context by key. If the key does not exist, return the default value.
• set(key: str, value: Any): Set a value in the context by key.
• delete(key: str): Delete a value from the context by key.

For example, if a tool needs to keep track of the last message sent by the user, it can store that message in the context and retrieve it in subsequent invocations, it can be used to:

• Store intermediate results.
• Append or modify input values based on previous invocations.
• Share data across different tools in the same session.

Example of using kwargs

# tools/echo_test.py

from pydantic import BaseModel, Field
from persona_toolkit.model import AgentContext

NAME = "echo"


class Input(BaseModel):
    message: str = Field(description="Message to echo")


class Output(BaseModel):
    message: str


def run(context: AgentContext, args: Input) -> Output:
    """
    Echo the message back.
    """
    # Access primary infos from kwargs
    project_id = context.project_id
    session_id = context.session_id
    user_id = context.user_id

    # TODO: Do something with project_id, session_id, user_id

    return Output(message=f"Echo: {args.message}")

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ExternalToolException

The ExternalToolException is a special exception provided by the persona-toolkit to signal that a tool does not perform any internal operations but instead delegates its behavior to an external system. When this exception is raised, it generates a transaction inside the persona-core framework, which can then be handled by any external service or component responsible for managing the tool's behavior. This mechanism is particularly useful for tools that act as placeholders or triggers for external workflows, ensuring seamless integration between the persona-toolkit and external systems.

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🔒 Secrets and Authentication

The persona-toolkit provides a mechanism to validate secrets using the SecretProvider. This can be used to add a basic authentication layer to your tools.

How to Configure Secrets

1. Define a Secret Validation Function

   Create a function to validate the secret. It must be named secret.py and placed in the root of your project.

   from persona_toolkit.model import AgentContext
   
   def validate(secret: str, context: AgentContext, args: dict) -> bool:
       """Validate static secrets."""
       return secret == "your-secret-key"
   

How Secrets Are Used

Once configured, the secret is checked by reading the x-persona-secret header from each tool request. The validation process works as follows:

1. If the x-persona-secret header is present in the request, its value is validated using the validate function.
2. If the secret is valid, the request proceeds as expected.
3. If the secret is invalid, the request is rejected with an appropriate error.

This mechanism ensures that tools can optionally enforce authentication while allowing unauthenticated access if no secret is provided.

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💻 Local Development

To run the CLI locally, you can use the following command:

python persona_toolkit/cli.py --port 8001

You can use local tools folder to test your tools. Just make sure to set the PYTHONPATH to the root of the project:

✅ Requirements

• Python 3.10+
• Poetry
• Uvicorn (installed automatically)

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📃 License

MIT License

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Built for the Persona Agent System 🤖