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Decorators

With only a few lines of code, builders can easily deploy custom AI agents to the Naptha hub or their own local node.

Context​

Naptha supports a web of multi-agent systems that grow and evolve. This walkthrough explains how to decorate functions in order to quickly publish existing agents, allowing them to interact with others.

Prerequisites​

  • Python >= 3.10, <= 3.13
  • Poetry package manager
  • Naptha username & password

See installation guide for help.

Step-by-step Walkthrough​

1. Setup​

Configure your .env file:

HUB_USER=<your_naptha_username>
HUB_PASS=<your_naptha_password>
HUB_URL=ws://node.naptha.ai:3001/rpc

NODE_URL=http://node.naptha.ai:7001

These variables are used to connect to the Naptha network.

2. Add Naptha SDK​

Update your pyproject.toml file:

[tool.poetry.dependencies]
naptha-sdk = {git = "https://github.com/NapthaAI/naptha-sdk"}

If not using poetry, you can just add naptha-sdk to your requirements.txt file, then run:

pip install -r requirements.txt

3. Install dependencies:​

Execute via CLI:

poetry install

4. Import Naptha SDK​

Put this in your main Python file: <agent>.py

from naptha_sdk.client.naptha import agent as naptha_agent

We recommend importing as naptha_agent to avoid naming conflicts.

5. Decorate​

Label agent functions:

@naptha_agent("<agent_name>")
def <agent_function>(...):
    # agent logic goes here
    return <agent_output>

Replace <agent_name> with a unique identifier for your agent. Keep <agent_function> and <agent_output> the same, however you named them.

6. Convert (automatically)​

Process and package decorated functions:

poetry run python <agent>.py

This creates a folder named agent_pkgs, which contains your "Napthafied" agent functions. The SDK translates your code into a format compatible with other agents on Naptha.

7. Test​

cd agent_pkgs/<agent_name>
poetry install
poetry run python <agent_name>/run.py

If you're using an agent framework other than CrewAI, you will likely need to change the inputs dict, where tool_name is the name of the agent method that you would like to call, tool_input_type is the type (e.g. pydantic schema name) of the input for that call, and tool_input_value is a dict of the schema parameters and values.

8. Publish​

Enter this command:

naptha publish

This command publishes all agents in the agent_pkgs folder to the Naptha node specified in your .env file.

Verify the agent is working properly:

naptha run <agent_name>

Check for your expected output.

How does this work?​

Let's break it down:

@naptha_agent("<agent_name>") with a unique name for the agent. Below that line, agent functionality can be defined normally using various frameworks.

By running Python code that includes our decorator, agent functions are automatically processed and converted into Naptha-compatible packages.

Later, when you enter naptha publish via the CLI, those agent packages will be added to the Naptha node specified in your .env file.

Usage Examples​

CrewAI​

Stock Analysis​

View the full code sample here.

@naptha_agent("financial_agent")
    def financial_agent(self) -> Agent:
        return Agent(
            ...
        )

Other Frameworks (Coming Soon)​

Feedback​

Create issues in the Naptha SDK GitHub repository to let us know any problems, ideas, or questions. We plan to continue adding examples, and your help would be much appreciated!